ORCID Profile
0000-0002-7301-6076
Current Organisations
La Trobe University - Melbourne Campus
,
Flinders University
,
Monash University
,
University of Nottingham
,
CSIRO
,
CSIRO Australian Manufacturing and Materials Precinct
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Nanochemistry and Supramolecular Chemistry | Physical Chemistry (Incl. Structural) | Condensed Matter Modelling and Density Functional Theory | Analytical spectrometry | Analytical chemistry | Basic pharmacology | Catalysis and Mechanisms of Reactions | Physical Chemistry of Materials | Structural Chemistry | Biological And Medical Chemistry | Condensed Matter Physics | Synthesis of Materials | Macromolecular and Materials Chemistry | Organic Chemical Synthesis | Theoretical and Computational Chemistry | Quantum Chemistry | Computer Communications Networks | Theory Of Materials | Biological Sciences Not Elsewhere Classified | Analytical biochemistry | Surfaces and Structural Properties of Condensed Matter |
ANZSRC Socio-Economic Objective (SEO)
Expanding Knowledge in the Chemical Sciences | Expanding Knowledge in the Physical Sciences | Biological sciences | Other | Chemical sciences | Physical sciences | Communication services not elsewhere classified | Industrial Chemicals and Related Products not elsewhere classified | Expanding Knowledge in Engineering | Treatments (e.g. chemicals, antibiotics)
Related Links
Publications
Rational Repurposing of Drugs, Clinical Trial Candidates, and Natural Products for SARS-CoV-2 Therapy
Publisher: Springer International Publishing
Date: 2022
Sparse feature selection identifies H2A.Z as a novel, pattern-specific biomarker for asymmetrically self-renewing distributed stem cells
Publisher: Elsevier BV
Date: 03-2015
An Experimental and Computational Approach to the Development of ZnO Nanoparticles that are Safe by Design
Publisher: Wiley
Date: 11-05-2016
Abstract: Zinc oxide nanoparticles have found wide application due to their unique optoelectronic and photocatalytic characteristics. However, their safety aspects remain of critical concern, prompting the use of physicochemical modifications of pristine ZnO to reduce any potential toxicity. However, the relationships between these modifications and their effects on biology are complex and still relatively unexplored. To address this knowledge gap, a library of 45 types of ZnO nanoparticles with varying particle size, aspect ratio, doping type, doping concentration, and surface coating is synthesized, and their biological effects measured. Three biological assays measuring cell damage or stress are used to study the responses of human umbilical vein endothelial cells (HUVECs) or human hepatocellular liver carcinoma cells (HepG2) to the nanoparticles. These experimental data are used to develop quantitative and predictive computational models linking nanoparticle properties to cell viability, membrane integrity, and oxidative stress. It is found that the concentration of nanoparticles the cells are exposed to, the type of surface coating, the nature and extent of doping, and the aspect ratio of the particles make significant contributions to the cell toxicity of the nanoparticles tested. Our study shows that it is feasible to generate models that could be used to design or optimize nanoparticles with commercially useful properties that are also safe to humans and the environment.
Modelling Atypical Small-Molecule Mimics of an Important Stem Cell Cytokine, Thrombopoietin
Publisher: Wiley
Date: 30-11-2009
Abstract: We report the first comprehensive 3D QSAR study of a large, structurally erse set of compounds that act as atypical thrombopoietin (TPO) mimics by interacting with the transmembrane domain of the TPO receptor, c-MPL. These agonists of c-MPL were superimposed according to a pharmacophore hypothesis, resulting in 3D QSAR models of high statistical significance. The pharmacophore-based superimposition and comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to derive the QSAR models relating structure to the published in vitro bioactivities of the TPO mimics. The CoMFA and CoMSIA models gave high correlation coefficients of the bioactivities with the derived fields, resulting in robust prediction of agonist activity of the superimposed compounds. The models have been interpreted in terms of the requirements for binding to the transmembrane domain of the TPO receptor.
The electronic structure of bicyclo[2.2.2]octa-2,5-dione
Publisher: Elsevier BV
Date: 10-2020
Machine Learning-assisted Exploration of a Universal Polymer Platform with Charge Transfer-dependent Full-color Emission
Publisher: American Chemical Society (ACS)
Date: 18-05-2022
DOI: 10.26434/CHEMRXIV-2022-JF798
Abstract: Understanding the color tuning of solid-state emissive materials is essential from a fundamental mechanistic viewpoint, as well as for practical applications. The development of color-tunable fluorescent materials with simple chemical compositions and easy to synthesize is highly desirable, but practically challenging. Despite copious research into molecular design and engineering, a general and facile polymer platform that offers high flexibility and broad extensibility in emission color tuning is still lacking. Here, we report a universal yet simple platform based on through-space charge transfer (TSCT) polymers, that has full-color tunable emission and was developed with the aid of predictive machine learning models. Using a single acceptor (A) fluorophore as the initiator for atom transfer radical polymerization (ATRP), a series of electron donor (D) groups containing simple polycyclic aromatic moieties (e.g., pyrene) are introduced either by one-step copolymerization or by end-group functionalization of a pre-synthesized polymer. By manipulating donor-acceptor (D-A) interactions via controlled polymer synthesis, continuous blue-to-red emission color tuning was easily achieved in solid polymers. Theoretical investigations confirm the structurally dependent TSCT-induced emission redshifts. We also exemplify how these TSCT polymers can be used as a general design platform for solid-state stimuli-responsive materials with high-contrast photochromic emission, by applying them to proof-of-concept information encryption.
Definitive confirmation for through-space bond dominance in the outermost pi-orbitals of norbornadiene
Publisher: Elsevier BV
Date: 05-2002
Correction to Materials Genome in Action: Identifying the Performance Limits of Physical Hydrogen Storage
Publisher: American Chemical Society (ACS)
Date: 20-11-2017
Prediction of Broad-Spectrum Pathogen Attachment to Coating Materials for Biomedical Devices
Publisher: American Chemical Society (ACS)
Date: 02-01-2018
Abstract: Bacterial infections in healthcare settings are a frequent accompaniment to both routine procedures such as catheterization and surgical site interventions. Their impact is becoming even more marked as the numbers of medical devices that are used to manage chronic health conditions and improve quality of life increases. The resistance of pathogens to multiple antibiotics is also increasing, adding an additional layer of complexity to the problems of employing safe and effective medical procedures. One approach to reducing the rate of infections associated with implanted and indwelling medical devices is the use of polymers that resist the formation of bacterial biofilms. To significantly accelerate the discovery of such materials, we show how state of the art machine learning methods can generate quantitative predictions for the attachment of multiple pathogens to a large library of polymers in a single model for the first time. Such models facilitate design of polymers with very low pathogen attachment across different bacterial species that will be candidate materials for implantable or indwelling medical devices such as urinary catheters, cochlear implants, and pacemakers.
An electron momentum spectroscopy and density functional theory study of the outer valence electronic structure of stella-2,6-dione
Publisher: IOP Publishing
Date: 07-07-2003
Applying quantitative structure–activity relationship approaches to nanotoxicology: Current status and future potential
Publisher: Elsevier BV
Date: 11-2013
DOI: 10.1016/J.TOX.2012.11.005
Abstract: The potential (eco)toxicological hazard posed by engineered nanoparticles is a major scientific and societal concern since several industrial sectors (e.g. electronics, biomedicine, and cosmetics) are exploiting the innovative properties of nanostructures resulting in their large-scale production. Many consumer products contain nanomaterials and, given their complex life-cycle, it is essential to anticipate their (eco)toxicological properties in a fast and inexpensive way in order to mitigate adverse effects on human health and the environment. In this context, the application of the structure-toxicity paradigm to nanomaterials represents a promising approach. Indeed, according to this paradigm, it is possible to predict toxicological effects induced by chemicals on the basis of their structural similarity with chemicals for which toxicological endpoints have been previously measured. These structure-toxicity relationships can be quantitative or qualitative in nature and they can predict toxicological effects directly from the physicochemical properties of the entities (e.g. nanoparticles) of interest. Therefore, this approach can aid in prioritizing resources in toxicological investigations while reducing the ethical and monetary costs that are related to animal testing. The purpose of this review is to provide a summary of recent key advances in the field of QSAR modelling of nanomaterial toxicity, to identify the major gaps in research required to accelerate the use of quantitative structure-activity relationship (QSAR) methods, and to provide a roadmap for future research needed to achieve QSAR models useful for regulatory purposes.
NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment
Publisher: Elsevier BV
Date: 2020
Introduction to a review series on advances in cell-based immune therapeutics in hematology
Publisher: American Society of Hematology
Date: 20-05-2001
Computational Modeling and Simulation of CO2 Capture by Aqueous Amines
Publisher: American Chemical Society (ACS)
Date: 18-05-2017
DOI: 10.1021/ACS.CHEMREV.6B00662
Abstract: We review the literature on the use of computational methods to study the reactions between carbon dioxide and aqueous organic amines used to capture CO
Linear Binary Classifier to Predict Bacterial Biofilm Formation on Polyacrylates
Publisher: American Chemical Society (ACS)
Date: 07-03-2023
Discovery and optimization of materials using evolutionary approaches
Publisher: American Chemical Society (ACS)
Date: 12-05-2016
DOI: 10.1021/ACS.CHEMREV.5B00691
Abstract: Materials science is undergoing a revolution, generating valuable new materials such as flexible solar panels, biomaterials and printable tissues, new catalysts, polymers, and porous materials with unprecedented properties. However, the number of potentially accessible materials is immense. Artificial evolutionary methods such as genetic algorithms, which explore large, complex search spaces very efficiently, can be applied to the identification and optimization of novel materials more rapidly than by physical experiments alone. Machine learning models can augment experimental measurements of materials fitness to accelerate identification of useful and novel materials in vast materials composition or property spaces. This review discusses the problems of large materials spaces, the types of evolutionary algorithms employed to identify or optimize materials, and how materials can be represented mathematically as genomes, describes fitness landscapes and mutation operators commonly employed in materials evolution, and provides a comprehensive summary of published research on the use of evolutionary methods to generate new catalysts, phosphors, and a range of other materials. The review identifies the potential for evolutionary methods to revolutionize a wide range of manufacturing, medical, and materials based industries.
6‐(2‐Methylpropylthio)‐2‐pyridyl methanesulfonate
Publisher: International Union of Crystallography (IUCr)
Date: 15-11-1993
Critical-like self-organization and natural selection: Two facets of a single evolutionary process?
Publisher: Elsevier BV
Date: 05-2008
DOI: 10.1016/J.BIOSYSTEMS.2008.01.005
Abstract: We argue that critical-like dynamics self-organize relatively easily in non-equilibrium systems, and that in biological systems such dynamics serve as templates upon which natural selection builds further elaborations. These critical-like states can be modified by natural selection in two fundamental ways, reflecting the selective advantage (if any) of heritable variations either among avalanche participants or among whole systems. First, reproducing (avalanching) units can differentiate, as units adopt systematic behavioural variations. Second, whole systems that are exposed to natural selection can become increasingly or decreasingly critical. We suggest that these interactions between SOC-like dynamics and natural selection have profound consequences for biological systems because they could have facilitated the evolution of ision of labour, compartmentalization and computation, key features of biological systems. The logical conclusion of these ideas is that the fractal geometry of nature is anything but coincidental, and that natural selection is itself a fractal process, occurring on many temporal and spatial scales.
Checkpoint blockade therapy is improved by altering the immune suppressive microenvironment with IPI-549, a potent and selective inhibitor of PI3K-gamma, in preclinical models
Publisher: American Association for Cancer Research (AACR)
Date: 15-07-2016
DOI: 10.1158/1538-7445.AM2016-554
Abstract: The phosphoinositide-3-kinase (PI3K) lipid kinases transduce signals in response to various stimuli in different cell types. PI3K-γ is predominantly expressed in leukocytes and not expressed in most epithelial tumors or sarcomas. Genetic studies highlight an important role for PI3K-γ in myeloid-derived cells that constitute a key component of the immune suppressive tumor microenvironment (Schmid et al. Canc Cell 2011). Targeting PI3K-γ could therefore alter the immune tumor microenvironment, enabling the immune system to attack tumor cells more effectively. We are developing IPI-549, an investigational small molecule inhibitor of PI3K-γ, and provide data to support the therapeutic potential of breaking tumor immune tolerance through PI3K-γ inhibition. IPI-549 is a potent and selective inhibitor of PI3K-γ with favorable pharmacological properties. In vitro functional assays demonstrated that IPI-549 blocked bone marrow derived M2 murine macrophage polarization, but did not affect M1 polarization. Oral administration of IPI-549 to tumor-bearing mice resulted in significant tumor growth inhibition in multiple syngeneic solid tumor models at PI3K-γ selective doses. Analysis of the tumor-associated immune cells demonstrated that IPI-549 treatment results in decreased immune suppressive myeloid cells and increased CD8+ T cells, suggesting enhanced anti-tumor immunity. To address the requirement for targeting myeloid cells by IPI-549, CD11b+ cells were depleted from a transplanted whole tumor Lewis Lung Carcinoma model and the effect of IPI-549 on limiting tumor growth was abrogated. In addition, a myeloid-infiltrated B16-GMCSF model, but not the isogenic B16 model without GMCSF, was responsive to IPI-549. Studies in immune-deficient mice or CD8 T-cell depleted tumor bearing mice demonstrated the T-cell dependence of IPI-549-mediated tumor growth inhibition. IPI-549 treatment also led to a significant reduction in lung metastases in the 4T1 and B16-GMCSF models. Importantly, in vivo studies with IPI-549 in combination with the immune checkpoint inhibitors anti-PD-1, anti-PDL-1 and anti-CTLA-4 showed increased tumor growth inhibition in multiple models compared to monotherapies alone. These data can inform combinations for future clinical trials. Our studies support a role for PI3K-γ in immune suppressive myeloid cells in the tumor microenvironment and provide evidence that targeted inhibition of PI3K-γ by IPI-549 can restore antitumor immune responses and inhibit tumor growth in preclinical models. A Phase 1 study evaluating IPI-549 as an orally administered therapeutic, as a single agent and in combination with an anti-PD-1 antibody therapy, in patients with selected solid tumors is expected to begin in early 2016. Citation Format: Olivier De Henau, Taha Merghoub, David Winkler, Sujata Sharma, Melissa Pink, Jeremy Tchaicha, Matthew Rausch, Jennifer Proctor, Nicole Kosmider, John Soglia, Vito Palombella, Jeffery L. Kutok, Jedd D. Wolchok, Karen McGovern. Checkpoint blockade therapy is improved by altering the immune suppressive microenvironment with IPI-549, a potent and selective inhibitor of PI3K-gamma, in preclinical models. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research 2016 Apr 16-20 New Orleans, LA. Philadelphia (PA): AACR Cancer Res 2016 (14 Suppl):Abstract nr 554.
Journal of Molecular Graphics and Modelling: Foreword
Publisher: Elsevier BV
Date: 07-2004
Potent agonists of a hematopoietic stem cell cytokine receptor, c-Mpl
Publisher: Wiley
Date: 28-03-2013
Abstract: Several growth factors feature prominently in the control of hematopoiesis. Thrombopoietin, a class I hematopoietic cytokine, plays critical roles in regulating hematopoietic stem cell numbers and also stimulates the production and differentiation of megakaryocytes, the bone marrow cells that ultimately produce platelets. Thrombopoietin interacts with the c-Mpl cell-surface receptor. Recently, several peptide and small-molecule agonists and antagonists of c-Mpl have been reported. We conducted a bioinformatics and molecular modeling study aimed at understanding the agonist activities of peptides that bind to c-Mpl, and developed new potent peptide agonists with low nanomolar activity. These agonists also show very high activity in human CD34(+) primary cell cultures, and doubled the mean blood platelet counts when injected into mice.
Design, Synthesis and Testing of Transition State Analogues of Alanine Racemase as Antibacterials
Publisher: CSIRO Publishing
Date: 1985
DOI: 10.1071/CH9850297
Abstract: Transition state analogues have been designed for alanine racemase , an important enzyme target for the development of bacterial cell wall inhibitors. These analogues are based on the transition state structure obtained from MINDO/3 calculations on the reaction pathway of a model alanine racemization . A number of analogues have been synthesized and are shown to have weak anti-bacterial activity, but results from microbiological assays indicate that their antibacterial activity is not due to inhibition of alanine racemase . Subsequent n.m.r .studies have shown that they are unstable in aqueous environments. Formaldehyde has been identified as one of the degradation products, and the likely source of antibacterial activity.
Discovery of (meth)acrylate polymers that resist colonization by fungi associated with pathogenesis and biodeterioration
Publisher: American Association for the Advancement of Science (AAAS)
Date: 05-06-2020
Abstract: Anti-attachment materials that are sprayable and 3D-printable passively prevent colonization by harmful fungi.
Capturing the Crystal: Prediction of Enthalpy of Sublimation, Crystal Lattice Energy, and Melting Points of Organic Compounds (vol 53, pg 223, 2013)
Publisher: American Chemical Society (ACS)
Date: 27-02-2013
DOI: 10.1021/CI400111G
Computational Nanotoxicology: Challenges and Perspectives
Publisher: Jenny Stanford Publishing
Date: 13-11-2019
Nonlinear predictive modeling of MHC class II-peptide binding using Bayesian neural networks
Publisher: Humana Press
Date: 2007
DOI: 10.1007/978-1-60327-118-9_27
Abstract: Methods for predicting the binding affinity of peptides to the MHC have become more sophisticated in the past 5-10 years. It is possible to use computational quantitative structure-activity methods to build models of peptide affinity that are truly predictive. Two of the most useful methods for building models are Bayesian regularized neural networks for continuous or discrete (categorical) data and support vector machines (SVMs) for discrete data. We illustrate the application of Bayesian regularized neural networks to modeling MHC class II-binding affinity of peptides. Training data comprised sequences and binding data for nonamer (nine amino acid) peptides. Peptides were characterized by mathematical representations of several types. Independent test data comprised sequences and binding data for peptides of length < or = 25. We also internally validated the models by using 30% of the data in an internal test set. We obtained robust models, with near-identical statistics for multiple training runs. We determined how predictive our models were using statistical tests and area under the receiver operating characteristic (ROC) graphs (A(ROC)). Some mathematical representations of the peptides were more efficient than others and were able to generalize to unknown peptides outside of the training space. Bayesian neural networks are robust, efficient "universal approximators" that are well able to tackle the difficult problem of correctly predicting the MHC class II-binding activities of a majority of the test set peptides.
De novo Drug Design – Ye olde Scoring Problem Revisited
Publisher: Wiley
Date: 2017
QUANTITATIVE STRUCTURE-ACTIVITY-RELATIONSHIPS IN INSECTICIDAL PYRETHROID ETHERS
Publisher: Wiley
Date: 1988
Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations
Publisher: American Chemical Society (ACS)
Date: 18-08-2021
Biomimetic molecular design tools that learn, evolve, and adapt
Publisher: Beilstein Institut
Date: 29-06-2017
DOI: 10.3762/BJOC.13.125
Abstract: A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known “S curve”, with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine.
A Predictive Model for Monolayer-Selective Metal-Mediated MoS2 Exfoliation Incorporating Electrostatics
Publisher: Wiley
Date: 30-10-2023
Synthesis, binding and bioactivity of gamma-methylene gamma-lactam ecdysone receptor ligands: Advantages of QSAR models for flexible receptors
Publisher: Elsevier BV
Date: 08-2010
DOI: 10.1016/J.BMC.2010.06.020
Abstract: Nuclear hormone receptors, such as the ecdysone receptor, often display a large amount of induced fit to ligands. The size and shape of the binding pocket in the EcR subunit changes markedly on ligand binding, making modelling methods such as docking extremely challenging. It is, however, possible to generate excellent 3D QSAR models for a given type of ligand, suggesting that the receptor adopts a relatively restricted number of binding site configurations or 'attractors'. We describe the synthesis, in vitro binding and selected in vivo toxicity data for gamma-methylene gamma-lactams, a new class of high-affinity ligands for ecdysone receptors from Bovicola ovis (Phthiraptera) and Lucilia cuprina (Diptera). The results of a 3D QSAR study of the binding of methylene lactams to recombinant ecdysone receptor protein suggest that this class of ligands is indeed recognised by a single conformation of the EcR binding pocket.
DEVELOPMENT OF LIGAND-IMMOBILISED SURFACES FOR EX VIVO EXPANSION OF HAEMOPOIETIC STEM CELLS
Publisher: Elsevier BV
Date: 08-2013
A quantitative structure-activity relationships model for the acute toxicity of substituted benzenes to Tetrahymena pyriformis using Bayesian-regularized neural networks
Publisher: American Chemical Society (ACS)
Date: 18-05-2000
DOI: 10.1021/TX9900627
Modeling the Influence of Fatty Acid Incorporation on Mesophase Formation in Amphiphilic Therapeutic Delivery Systems
Publisher: American Chemical Society (ACS)
Date: 10-02-2016
DOI: 10.1021/ACS.MOLPHARMACEUT.5B00848
Abstract: Dispersed hiphile-fatty acid systems are of great interest in drug delivery and gene therapies because of their potential for triggered release of their payload. The mesophase behavior of these systems is extremely complex and is affected by environmental factors such as drug loading, percentage and nature of incorporated fatty acids, temperature, pH, and so forth. It is important to study phase behavior of hiphilic materials as the mesophases directly influence the release rate of the incorporated drugs. We describe a robust machine learning method for predicting the phase behavior of these systems. We have developed models for each mesophase that simultaneous and reliably model the effects of hiphile and fatty acid structure, concentration, and temperature and that make accurate predictions of these mesophases for conditions not used to train the models.
Electron momentum spectroscopy of [1.1.1]propellane
Publisher: Elsevier BV
Date: 10-1995
Production of interstellar HCN and HNC
Publisher: American Astronomical Society
Date: 12-1989
DOI: 10.1086/168175
A General Model for Binary Cell Fate Decision Gene Circuits with Degeneracy: Indeterminacy and Switch Behavior in the Absence of Cooperativity
Publisher: Public Library of Science (PLoS)
Date: 19-05-2011
Immobilisation of a thrombopoietin peptidic mimic by self-assembled monolayers for culture of CD34+ cells
Publisher: Elsevier BV
Date: 2015
DOI: 10.1016/J.BIOMATERIALS.2014.10.029
Abstract: Compared to soluble cytokines, surface-tethered ligands can deliver biological signalling with precise control of spatial positioning and concentration. A strategy that immobilises ligand molecules on a surface in a uniform orientation using non-cleavable linkages under physiological conditions would enhance the specific and systemic delivery of signalling in the local environment. We used mixed self-assembled monolayers (SAMs) of oxyamine- and oligo(ethylene glycol)-terminated thiols on gold to covalently install aldehyde- or ketone-functionalised ligands via oxime conjugation. Characterisation by electrochemistry and X-ray photoelectron spectroscopy showed quantitative immobilisation of the ligands on SAM surfaces. The thrombopoietin mimetic peptide, RILL, was immobilised on SAMs and the bioactivity of the substrate was demonstrated by culturing factor-dependent cells. We also optimised the immobilisation and wash conditions so that the peptide was not released into the culture medium and the immobilised RILL could be re-used for consecutive cell cultures. The surface also supported the growth of haematopoietic CD34+ cells comparable to the standard thrombopoietin-supplemented culture. Furthermore, the RILL-immobilised SAM surface was as effective in expanding uncommitted CD34+ cells as standard culture. The stimulatory effect of surface-tethered ligands in haematopoietic stem cell expansion supports the use of ligand immobilisation strategies to replicate the haematopoietic stem cell niche.
Predicting the performance of organic corrosion inhibitors
Publisher: MDPI AG
Date: 08-12-2017
DOI: 10.3390/MET7120553
A conformational study of the topographical requirements of a phytotropin recognition site on the naphthylphthalamic acid receptor
Publisher: Elsevier BV
Date: 1987
Predictive Mesoscale Network Model of Cell Fate Decisions during C. elegans Embryogenesis
Publisher: MIT Press - Journals
Date: 10-2009
DOI: 10.1162/ARTL.2009.WINKLER.011
Abstract: The differentiation pathway of the nematode worm model organism C. elegans has been studied as a surrogate for future work on the human embryonic stem cell genetic networks. We extend earlier work on recursive networks by the introduction of a regularizer and more robust convergence algorithms, and by training the model to recapitulate experimental gene expression patterns rather than random expression patterns. We also assess the ability of the model to predict the expression profile on the next cell(s) in the lineage. The weight matrix from the model may be interpreted as a set of rules that guides the differentiation of the cells via a set of regulatory factors: internal genes or external entities. The activity of the regulatory factors shows patterns across the differentiation pathway that reflect the left- or right-hand split. Using these patterns, it may be possible to identify the actual factors responsible for the differentiation and to interpret the associated weights. The model was able to predict expression profiles of cells not used in training the model with a relatively low error rate.
Signalling pathway crosstalk stimulated by L-proline drives differentiation of mouse embryonic stem cells to primitive-ectoderm-like cells
Publisher: Cold Spring Harbor Laboratory
Date: 15-02-2023
DOI: 10.1101/2023.02.14.528585
Abstract: The amino acid L-proline exhibits novel growth factor-like properties during development - from improving blastocyst development to driving neurogenesis in vitro . Addition of 400 μM L-proline to self-renewal medium drives mouse embryonic stem cells (ESCs) to a transcriptionally distinct pluripotent cell population - early primitive ectoderm-like (EPL) cells - which lies between the naïve and primed states. EPL cells retain expression of pluripotency genes, upregulate primitive ectoderm markers, undergo a morphological change and have increased cell number. These changes are facilitated by a complex signalling network hinging on the Mapk, Fgfr, Pi3k and mTor pathways. We use a factorial experimental design coupled with linear modelling and Bayesian regularised neural networks to understand which signalling pathways are involved in the transition between ESCs and EPL cells, and how they underpin changes in morphology, cell number, apoptosis, proliferation and gene expression. This approach allows for consideration of where pathways work antagonistically or synergistically. Modelling showed that most properties were affected by more than one inhibitor, and each inhibitor blocked specific aspects of differentiation. These mechanisms underpin both progression of stem cells across the in vitro pluripotency continuum and serve as a model for pre-, peri- and post-implantation embryogenesis. L-proline acts as growth factor to modulate phosphorylation of the Mapk, Pi3k, Fgf and mTor signalling pathways to drive embryonic stem cells to primitive ectoderm-like cells.
The PI3K-gamma inhibitor, IPI-549, increases antitumor immunity by targeting tumor-associated myeloid cells and remodeling the immune-suppressive tumor microenvironment
Publisher: American Association for Cancer Research (AACR)
Date: 31-10-2016
DOI: 10.1158/2326-6066.IMM2016-B032
Abstract: The PI3 kinases (PI3K) belong to a family of signal-transducing enzymes that mediate key cellular functions in cancer and immunity. The PI3K-gamma (γ) isoform plays an important role in macrophage/myeloid cell function and migration, and a role for PI3K-γ in tumor growth and immune tolerance has been established in studies utilizing PI3K-γ knockout (KO) mice (Schmid et al., Cancer Cell, 2011 Gunderson et al., Cancer Discovery, 2015). We propose that pharmacological inhibition of PI3K-γ in myeloid cells can alter the tumor-immune microenvironment leading to enhanced antitumor T-cell responses. IPI-549 is an oral, potent, and selective inhibitor of PI3K-γ. Prior studies showed single agent antitumor activity in multiple murine tumor models, and enhanced antitumor activity and improved survival when combined with immune-checkpoint blockade. This antitumor activity is dependent on the presence of both immune-suppressive tumor-associated CD11b+ myeloid cells and CD8+ cytotoxic T cells. IPI-549 can reduce the T-cell-suppressive activity of both murine and human myeloid-derived suppressor cells in vitro (Kutok et al, 2015 CRI-CIMT-EATI-AACR Cancer Immunotherapy Meeting De Henau et al, 2016 AACR Annual Meeting). We now show that IPI‑549 treatment of tumor‑bearing mice leads to a shift in tumor-associated myeloid cells from an immunosuppressive M2 phenotype to a proinflammatory M1 phenotype, characterized by reduced CD206 expression and enhanced expression of MHC class II and NOS2. Compared to vehicle-treated controls, short-term (9 days) treatment of CT26 tumor‑bearing animals with IPI‑549 revealed an increased frequency of circulating tumor-specific T cells, an increased percentage of tumor-infiltrating CD8+IFNγ+ T cells, and a reduced percentage of CD4+Foxp3+ regulatory T cells, leading to a trend towards increasing the CD8+/T-reg cell ratio. Treatment of 4T1 and B16GM tumor-bearing mice with IPI-549 for 14 days led to a significant increase in the CD8+/T-reg cell ratio. Together these data show that IPI-549 treatment leads to a proinflammatory tumor microenvironment. Importantly, gene and protein expression analysis of whole tumor tissue collected from IPI-549-treated mice revealed a cytotoxic T-cell signature characterized by increased production of proinflammatory cytokines, and enhanced expression costimulatory and coinhibitory genes relative to vehicle-treated animals. These findings indicate that IPI-549 increases antitumor immunity by remodeling the tumor-immune microenvironment via blockade of tumor-associated myeloid cells. In addition, the up-regulation of costimulatory and coinhibitory genes with IPI-549 treatment provides a mechanistic rationale for the observed combination activity with immune checkpoint inhibition. IPI-549 is currently in Phase I development, both as a single agent and in combination with an anti-PD-1 antibody, in solid tumors (ClinicalTrials.gov NCT02637531). Citation Format: Matthew Rausch, Jeremy Tchaicha, Thomas Tibbitts, Olivier De Henau, Sujata Sharma, Melissa Pink, Joseph Gladstone, Jennifer Proctor, Mark Douglas, Howard Stern, Taha Merghoub, Jedd Wolchok, Karen McGovern, Jeff Kutok, David Winkler. The PI3K-γ inhibitor, IPI-549, increases antitumor immunity by targeting tumor-associated myeloid cells and remodeling the immune-suppressive tumor microenvironment [abstract]. In: Proceedings of the Second CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival 2016 Sept 25-28 New York, NY. Philadelphia (PA): AACR Cancer Immunol Res 2016 (11 Suppl):Abstract nr B032.
The microwave spectrum of (Z)-Ethanimine
Publisher: CSIRO Publishing
Date: 1980
DOI: 10.1071/CH9800001
Abstract: The microwave spectrum of ethanimine, CH3CH=NH, has been measured over the range of 18-76 GHz. A series of lines have been attributed to the Z-isomer. These have been fitted to an asymmetric rotor with inclusion of centrifugal distortion parameters. An excited torsional state has also been assigned and the barrier to internal rotation of the methyl group has been determined. The dipole moment has been evaluated from the Stark effect as 2.42 D. The quadrupole coupling constants of the nitrogen atom have been obtained from high-resolution studies. In contrast to methanimine, we found no evidence of magnetic hyperfine interaction in the structures of the multiplets.
The role of quantitative structure--activity relationships (QSAR) in biomolecular discovery.
Publisher: Oxford University Press (OUP)
Date: 2002
DOI: 10.1093/BIB/3.1.73
The impact of machine learning on future tuberculosis drug discovery
Publisher: Informa UK Limited
Date: 02-08-2022
Bayesian neural nets for modeling in drug discovery
Publisher: Elsevier BV
Date: 05-2004
Data-driven selection of electrolyte additives for aqueous magnesium batteries
Publisher: Royal Society of Chemistry (RSC)
Date: 2022
DOI: 10.1039/D2TA04538A
Abstract: Data-driven quantitative structure–property relationship models facilitate the selection of potent electrolyte additives for aqueous magnesium batteries in an active design of experiments approach.
5-epi-Deoxyrhamnojirimycin is a potent inhibitor of an a-L-rhamnosidase: 5-epi-deoxymannojirimycin is not a potent inhibitor of an a-D-mannosidase
Publisher: Elsevier BV
Date: 08-1998
Toward Novel Universal Descriptors: Charge Fingerprints
Publisher: American Chemical Society (ACS)
Date: 12-02-2009
DOI: 10.1021/CI800290H
Abstract: Although there are a myriad of molecular descriptors for QSAR described in the literature, many descriptors contain similar information as others or are information poor. Recent work has suggested that it may be possible to discover a relatively small pool of 'universal' descriptors from which subsets can be drawn to build a erse variety of models. We describe a new type of descriptor of this type, the charge fingerprint. This descriptor family can build good QSAR models of a erse range of physicochemical and biological properties and can be calculated quickly and easily. It appears to be useful for modeling large data sets and has potential for screening large virtual libraries.
Robust QSAR models from novel descriptors and Bayesian Regularised Neural Networks
Publisher: Informa UK Limited
Date: 08-2000
Aluminum toxicity risk reduction as a result of reduced acid deposition in Adirondack lakes and ponds
Publisher: Springer Science and Business Media LLC
Date: 25-10-2016
DOI: 10.1007/S10661-016-5589-4
Abstract: In 1990, the US Congress amended the Clean Air Act (CAA) to reduce regional-scale ecosystem degradation from SO
Rapid evaluation of immobilized immunoglobulins using automated mass-segmented ToF-SIMS
Publisher: AIP Publishing
Date: 11-2019
DOI: 10.1063/1.5121450
Abstract: Surface interactions largely control how biomaterials interact with biology and how many other types of materials function in industrial applications. ToF-SIMS analysis is extremely useful for interrogating the surfaces of complex materials and shows great promise in analyzing biological s les. Previously, the authors demonstrated that segmentation (between 1 and 0.005 m/z mass bins) of the mass spectral axis can be used to differentiate between polymeric materials with both very similar and dissimilar molecular compositions. Here, the same approach is applied for the analysis of proteins on surfaces, focusing on the effect of binding and orientation of an antibody on the resulting ToF-SIMS spectrum. Due to the complex nature of the s les that contain combinations of only 20 amino acids differing in sequence, it is enormously challenging and prohibitively time-consuming to distinguish the minute variances presented in each dataset through manual analysis alone. Herein, the authors describe how to apply the newly developed rapid data analysis workflow to previously published ToF-SIMS data for complex biological materials, immobilized antibodies. This automated method reduced the analysis time by two orders of magnitudes while enhancing data quality and allows the removal of any user bias. The authors used mass segmentation at 0.005 m/z over a 1–300 mass range to generate 60 000 variables. In contrast to the previous manual binning approach, this method captures the entire mass range of the spectrum resulting in an information-rich dataset rather than specifically selected mass spectral peaks. This work constitutes an additional proof of concept that rapid and automated data analyses involving mass-segmented ToF-SIMS spectra can efficiently and robustly analyze a broader range of complex materials, ranging from generic polymers to complicated biological s les. This automated analysis method is also ideally positioned to provide data to train machine learning models of surface-property relationships that can greatly enhance the understanding of how the surface interacts with biology and provides more accurate and robust quantitative predictions of the biological properties of new materials.
Comparison of Linear and Nonlinear Classification Algorithms for the Prediction of Drug and Chemical Metabolism by Human UDP-Glucuronosyltransferase Isoforms
Publisher: American Chemical Society (ACS)
Date: 06-09-2003
DOI: 10.1021/CI034108K
Abstract: Partial least squares discriminant analysis (PLSDA), Bayesian regularized artificial neural network (BRANN), and support vector machine (SVM) methodologies were compared by their ability to classify substrates and nonsubstrates of 12 isoforms of human UDP-glucuronosyltransferase (UGT), an enzyme "superfamily" involved in the metabolism of drugs, nondrug xenobiotics, and endogenous compounds. Simple two-dimensional descriptors were used to capture chemical information. For each data set, 70% of the data were used for training, and the remainder were used to assess the generalization performance. In general, the SVM methodology was able to produce models with the best predictive performance, followed by BRANN and then PLSDA. However, a small number of data sets showed either equivalent or better predictability using PLSDA, which may indicate relatively linear relationships in these data sets. All SVM models showed predictive ability (>60% of test set predicted correctly) and five out of the 12 test sets showed excellent prediction (>80% prediction accuracy). These models represent the first use of pattern recognition methods to discriminate between substrates and nonsubstrates of human drug metabolizing enzymes and the first thorough assessment of three classification algorithms using multiple metabolic data sets.
Competitive Inhibition Mechanism of Acetylcholinesterase without Catalytic Active Site Interaction: Study on Functionalized C
Publisher: American Chemical Society (ACS)
Date: 24-05-2017
Abstract: Acetylcholinesterase (AChE) activity regulation by chemical agents or, potentially, nanomaterials is important for both toxicology and pharmacology. Competitive inhibition via direct catalytic active sites (CAS) binding or noncompetitive inhibition through interference with substrate and product entering and exiting has been recognized previously as an AChE-inhibition mechanism for bespoke nanomaterials. The competitive inhibition by peripheral anionic site (PAS) interaction without CAS binding remains unexplored. Here, we proposed and verified the occurrence of a presumed competitive inhibition of AChE without CAS binding for hydrophobically functionalized C
Optimal machine learning models for robust materials classification using ToF-SIMS data
Publisher: Elsevier BV
Date: 09-2019
Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems
Publisher: American Vacuum Society
Date: 03-2022
DOI: 10.1116/6.0001590
Abstract: Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormous amount of information about the chemical and molecular composition of a surface. However, this information is inherently complex, making interpretation and analysis of the vast amount of data produced by a single ToF-SIMS experiment a considerable challenge. Much research over the past few decades has focused on the application and development of multivariate analysis (MVA) and machine learning (ML) techniques that find meaningful patterns and relationships in these datasets. Here, we review the unsupervised algorithms—that is, algorithms that do not require ground truth labels—that have been applied to ToF-SIMS images, as well as other algorithms and approaches that have been used in the broader family of mass spectrometry imaging (MSI) techniques. We first give a nontechnical overview of several commonly used classes of unsupervised algorithms, such as matrix factorization, clustering, and nonlinear dimensionality reduction. We then review the application of unsupervised algorithms to various organic, bioorganic, and biological systems including cells and tissues, organic films, residues and coatings, and spatially structured systems such as polymer microarrays. We then cover several novel algorithms employed for other MSI techniques that have received little attention from ToF-SIMS imaging researchers. We conclude with a brief outline of potential future directions for the application of MVA and ML algorithms to ToF-SIMS images.
Adrien albert award: How to mine chemistry space for new drugs and biomedical therapies
Publisher: CSIRO Publishing
Date: 2015
DOI: 10.1071/CH15172
Abstract: It is clear that the sizes of chemical, ‘drug-like’, and materials spaces are enormous. If scientists working in established therapeutic, and newly established regenerative medicine fields are to discover better molecules or materials, they must find better ways of probing these enormous spaces. There are essentially five ways that this can be achieved: combinatorial and high throughput synthesis and screening approaches fragment-based methods de novo molecular design, design of experiments, ersity libraries supramolecular approaches evolutionary approaches. These methods either synthesise materials and screen them more quickly, or constrain chemical spaces using biology or other types of ‘fitness functions’. High throughput experimental approaches cannot explore more than a minute part of chemical space. We are nevertheless entering into an era that is data dominated. High throughput experiments, robotics, automated crystallographic beam lines, combinatorial and flow synthesis, high content screening, and the ‘omics’ technologies are providing a flood of data, and efficient methods for extracting meaning from it are essential. This paper describes how new developments in mathematics have provided excellent, robust computational modelling tools for exploring large chemical spaces, for extracting meaning from large datasets, for designing new bioactive agents and materials, and for making truly predictive, quantitative models of the properties of molecules and materials for use in therapeutic and regenerative medicine. We describe these broadly applicable modelling tools and provide ex les of their application to serum free stem cell culture, pathogen resistant polymers for implantable devices, new markers and biological mechanisms derived from mathematical analyses of gene array data, and pharmacokinetically important physicochemical properties of small molecules. We also discuss biologically conserved peptide motifs as a design framework for small molecule drugs and give ex les of the application of this concept to drug design.
Modelling blood-brain barrier partitioning using Bayesian neural nets
Publisher: Elsevier BV
Date: 07-2004
Probing the properties of molecules and complex materials using machine learning
Publisher: CSIRO Publishing
Date: 13-09-2022
DOI: 10.1071/CH22138
Abstract: The application of machine learning to predicting the properties of small and large discrete (single) molecules and complex materials (polymeric, extended or mixtures of molecules) has been increasing exponentially over the past few decades. Unlike physics-based and rule-based computational systems, machine learning algorithms can learn complex relationships between physicochemical and process parameters and their useful properties for an extremely erse range of molecular entities. Both the breadth of machine learning methods and the range of physical, chemical, materials, biological, medical and many other application areas have increased markedly in the past decade. This Account summarises three decades of research into improved cheminformatics and machine learning methods and their application to drug design, regenerative medicine, biomaterials, porous and 2D materials, catalysts, biomarkers, surface science, physicochemical and phase properties, nanomaterials, electrical and optical properties, corrosion and battery research.
Corrigendum to: Validating Eaton's Hypothesis: Cubane as a Benzene Bioisostere (Angewandte Chemie International Edition, (2016), 55, 11, (3580-3585), 10.1002/anie.201510675)
Publisher: Wiley
Date: 05-07-2018
The diverse biological properties of the chemically inert noble gases
Publisher: Elsevier BV
Date: 04-2016
DOI: 10.1016/J.PHARMTHERA.2016.02.002
Abstract: The noble gases represent an intriguing scientific paradox. They are extremely inert chemically but display a remarkable spectrum of clinically useful biological properties. Despite a relative paucity of knowledge of their mechanisms of action, some of the noble gases have been used successfully in the clinic. Studies with xenon have suggested that the noble gases as a class may exhibit valuable biological properties such as anaesthesia amelioration of ischemic damage tissue protection prior to transplantation analgesic properties and a potentially wide range of other clinically useful effects. Xenon has been shown to be safe in humans, and has useful pharmacokinetic properties such as rapid onset, fast wash out etc. The main limitations in wider use are that: many of the fundamental biochemical studies are still lacking the lighter noble gases are likely to manifest their properties only under hyperbaric conditions, impractical in surgery and administration of xenon using convectional gaseous anaesthesia equipment is inefficient, making its use very expensive. There is nonetheless a significant body of published literature on the biochemical, pharmacological, and clinical properties of noble gases but no comprehensive reviews exist that summarize their properties and the existing knowledge of their models of action at the molecular (atomic) level. This review provides such an up-to-date summary of the extensive, useful biological properties of noble gases as drugs and prospects for wider application of these atoms.
Validating Eaton's Hypothesis: Cubane as a Benzene Bioisostere
Publisher: Wiley
Date: 05-02-2016
Abstract: Pharmaceutical and agrochemical discovery programs are under considerable pressure to meet increasing global demand and thus require constant innovation. Classical hydrocarbon scaffolds have long assisted in bringing new molecules to the market place, but an obvious omission is that of the Platonic solid cubane. Eaton, however, suggested that this molecule has the potential to act as a benzene bioisostere. Herein, we report the validation of Eaton's hypothesis with cubane derivatives of five molecules that are used clinically or as agrochemicals. Two cubane analogues showed increased bioactivity compared to their benzene counterparts whereas two further analogues displayed equal bioactivity, and the fifth one demonstrated only partial efficacy. Ramifications from this study are best realized by reflecting on the number of bioactive molecules that contain a benzene ring. Substitution with the cubane scaffold where possible could revitalize these systems, and thus expedite much needed lead candidate identification.
Modeling the molecular basis for alpha 4 beta 1 integrin antagonism
Publisher: Elsevier BV
Date: 10-2011
DOI: 10.1016/J.BMC.2011.08.011
Abstract: We report a 3D QSAR study of almost 300 structurally erse small molecule antagonists of the integrin α4β1 whose biological activity spans six orders of magnitude. The alignment of the molecules was based on the conformation of a structurally related ligand bound to the αIIBβ3 and αvβ3 integrins in X-ray crystallographic studies. The molecular field method, CoMSIA, was used to generate the 3D QSAR models. The resulting models showed that the lipophilic properties were the most important, with hydrogen bond donor and steric properties less relevant. The models were highly significant (r(2)=0.89, q2(LOO)=0.67, r(2) (test set)=0.76), and could make robust predictions of the data (SEE=0.46, SEP=0.78, SEP (test set)=0.66). We predicted the antagonist activities of a further ten compounds with useful accuracy. The model appears capable of predicting α4β1 integrin antagonist activity to within a factor of five for compounds within its domain of applicability. The implications for design of improved integrin antagonists will be discussed.
Modelling topoisomerase I inhibition by minor groove binders
Publisher: Elsevier BV
Date: 02-2011
DOI: 10.1016/J.BMC.2011.01.003
Abstract: Topoisomerase inhibition is an extremely useful target for anticancer and antimicrobial drugs, and an undesirable side effect of some drugs targeting other proteins. Published modelling studies are sparse, and have used small data sets with relatively low molecular ersity. Given the important role of minor groove binding in the mechanism of topoisomerase I inhibition, we have conducted the first 3D QSAR study of topoisomerase I inhibition of a large, erse set of minor groove binders using the minor groove binding conformation as the alignment template. The highly significant QSAR models resulting from this alignment identify the roles played by molecular features, most importantly the hydrogen bond donor properties.
Transformation of substituted 2H-pyran-5-carboxylates into 3R*-vinyl-1,2R*-cyclopropanedicarboxylates
Publisher: Royal Society of Chemistry (RSC)
Date: 1998
DOI: 10.1039/A804237C
A density functional theory and electron momentum spectroscopy study into the complete valence electronic structure of cubane
Publisher: American Chemical Society (ACS)
Date: 04-2000
DOI: 10.1021/JA9940423
Orbital based electronic structural signatures of the guanine keto G-7H/G-9H tautomer pair as studied using dual space analysis
Publisher: Elsevier BV
Date: 05-2006
DOI: 10.1016/J.BPC.2005.12.006
Abstract: Electronic structural signatures of the guanine-7H and guanine-9H tautomers have been investigated on an orbital by orbital basis using dual space analysis. A combination of density functional theory (B3LYP/TZVP), the statistical average of model orbital potentials (SAOP/TZ2P) method and outer valence Green's function theory (OVGF/TZVP) has been used to generate optimal tautomer geometries and accurate ionization energy spectra for the guanine tautomer pair. The present work found that the non-planar form for both of the guanine keto pair possesses lower energies than their corresponding planar counterparts, and that the canonical form of the guanine-7H tautomer has slightly lower total energy than guanine-9H. This latter result is in agreement with previous experimental and theoretical findings. In the planar guanine pair the geometric parameters and anisotropic molecular properties are compared, focusing on changes caused by the mobile proton transfer. It is demonstrated that the mobile proton only causes limited disturbance to isotropic properties, such as geometry and the energetics, of the guanine keto tautomer pair. The exception to this general statement is for related local changes such as the N((7))-C((8)) and C((8))-N((9)) bond length resonance between the single and double bonds, reflecting the nitrogen atom being bonded with the mobile proton in the tautomers. The mobile proton distorts the electron distribution of the tautomers, which leads to significant changes in the molecular anisotropic properties. The dipole moment of guanine-7H is altered by about a factor of three, from 2.23 to 7.05 D (guanine-9H), and the molecular electrostatic potentials also reflect significant electron charge distortion. The outer valence orbital momentum distributions, which were obtained using the plane wave impulse approximation (PWIA), have demonstrated quantitatively that the outer valence orbitals of the tautomer pair can be ided into three groups. That is orbitals 1a''-7a'' and 18a', which do not have visible alternations in the tautomeric process (which consist of either pi orbitals or are close to the inner valence shell) a second group comprising orbitals 19a'-22a', 25a', 26a', 28a', 29a' and 31a', which show small perturbations as a result of the mobile hydrogen locations and group three, orbitals 23a', 24a', 27a', 30a' and 32a', which demonstrate significant changes due to the mobile proton transfer and are therefore considered as signature orbitals of the G-7H/G-9H keto tautomeric process.
Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR
Publisher: Wiley
Date: 26-10-2017
Abstract: Neural networks have generated valuable Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models for a wide variety of small molecules and materials properties. They have grown in sophistication and many of their initial problems have been overcome by modern mathematical techniques. QSAR studies have almost always used so-called "shallow" neural networks in which there is a single hidden layer between the input and output layers. Recently, a new and potentially paradigm-shifting type of neural network based on Deep Learning has appeared. Deep learning methods have generated impressive improvements in image and voice recognition, and are now being applied to QSAR and QSAR modelling. This paper describes the differences in approach between deep and shallow neural networks, compares their abilities to predict the properties of test sets for 15 large drug data sets (the kaggle set), discusses the results in terms of the Universal Approximation theorem for neural networks, and describes how DNN may ameliorate or remove troublesome "activity cliffs" in QSAR data sets.
Theoretical and (e,2e) Experimental Investigation into the Complete Valence Electronic Structure of [1.1.1]Propellane
Publisher: American Chemical Society (ACS)
Date: 03-1997
DOI: 10.1021/JA963058F
CONFORMATIONAL-ANALYSIS OF THE ERGOT ALKALOIDS ERGOTAMINE AND ERGOTAMININE
Publisher: American Chemical Society (ACS)
Date: 08-1982
DOI: 10.1021/JM00350A010
Abstract: Conformational analyses by 1H NMR and potential-energy calculations are reported for the ergot alkaloids ergotamine and ergotaminine, both as free bases and as the protonated species. In the neutral forms in CDCl3. two strong intramolecular hydrogen bonds fix the molecules in folded conformations, but the protonated species adopt a more extended conformation, with a single intramolecular hydrogen bond. Of the 24 alternative conformations available to ergotamine, the most likely biologically active species in environments with low dielectric constants, e.g., the presumed ergotamine binding site, is the folded, hydrogen-bonded conformation observed for the neutral molecule in CDCl3 solution.
Electron momentum spectroscopy studies on atom and molecules using monochromated electrons
Publisher: EDP Sciences
Date: 06-1999
DOI: 10.1051/JP4:1999626
Machine learning property prediction for organic photovoltaic devices
Publisher: Springer Science and Business Media LLC
Date: 06-11-2020
DOI: 10.1038/S41524-020-00429-W
Abstract: Organic photovoltaic (OPV) materials are promising candidates for cheap, printable solar cells. However, there are a very large number of potential donors and acceptors, making selection of the best materials difficult. Here, we show that machine-learning approaches can leverage computationally expensive DFT calculations to estimate important OPV materials properties quickly and accurately. We generate quantitative relationships between simple and interpretable chemical signature and one-hot descriptors and OPV power conversion efficiency (PCE), open circuit potential ( V oc ), short circuit density ( J sc ), highest occupied molecular orbital (HOMO) energy, lowest unoccupied molecular orbital (LUMO) energy, and the HOMO–LUMO gap. The most robust and predictive models could predict PCE (computed by DFT) with a standard error of ±0.5 for percentage PCE for both the training and test set. This model is useful for pre-screening potential donor and acceptor materials for OPV applications, accelerating design of these devices for green energy applications.
Fusing ToF-SIMS images for spatial-spectral resolution enhancement using a convolutional neural network
Publisher: Wiley
Date: 10-10-2022
Abstract: Hyperspectral data sets generated by time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) contain valuable spatial‐spectral information characterizing the distribution of atomic and molecular species across a s le surface. Modern ToF‐SIMS instruments have high spatial resolution (in the order of tens of nanometers) relative to most other mass spectrometry imaging (MSI) techniques. However, there is generally a trade‐off between spatial and mass resolution when using different instrument modes. In this study, a convolutional neural network (CNN) fusion method is used to fuse correlated high spatial and high mass resolution ToF‐SIMS hyperspectral data sets. This process generates resolution‐enhanced data, which exhibit both high spatial and mass resolution. The CNN fusion method is applied to ToF‐SIMS images of a simple, well‐characterized gold mesh s le and a significantly more complex biological (tumor) tissue section. The method is compared to another linear fusion method used in the broader MSI community and a substantial improvement is found. This comparison focuses on both visual quality observations as well as statistical similarity measures. This work demonstrates the utility of the CNN fusion method for ToF‐SIMS data, enabling investigation of the atomic and molecular characteristics of surfaces at high spatial and mass spectral resolution.
The computer simulation of high throughput screening of bioactive molecules
Publisher: Springer US
Date: 2000
CARBOCATION-MEDIATED REARRANGEMENTS WITHIN [NM1]PROPELLANE FRAMEWORKS
Publisher: CSIRO Publishing
Date: 1991
DOI: 10.1071/CH9910593
Abstract: Mild thermolysis of the tetracyclic mesylate (6) in the presence of traces of mineral acid afforded a mixture of the rearrangement products (21) and (22) as well the σ-anti- bishomotetralin (20). On treatment with methanesulfonic acid in dichloromethane, compound (20) gives rise to the [5.4.1] propellene (21) which, on prolonged exposure to these reaction conditions, isomerizes to the mesyloxymethyl compound (22). Under the same conditions, the σ-anti- bishomoindan (4) is converted into the [5.3.1] propellene (3) which, in turn, slowly rearranges to the [4.3.1] propellene (23). A mechanistic rationalization of these observations is advanced which also accounts for the formation of the novel by-product (16) observed during Simmons-Smith cyclopropanation of allylic alcohol (15a).
MOLECULAR-IDENTIFICATION NUMBER FOR SUBSTRUCTURE SEARCHES
Publisher: American Chemical Society (ACS)
Date: 08-1989
DOI: 10.1021/CI00063A011
Computational Approaches
Publisher: Elsevier
Date: 2012
Cross-validatory selection of test and validation sets in multivariate calibration and neural networks as applied to spectroscopy
Publisher: Royal Society of Chemistry (RSC)
Date: 1997
DOI: 10.1039/A703565I
Abstract: Cross-validated and non-cross-validated regression models using principal component regression (PCR), partial least squares (PLS) and artificial neural networks (ANN) have been used to relate the concentrations of polycyclic aromatic hydrocarbon pollutants to the electronic absorption spectra of coal tar pitch volatiles. The different trends in the cross-validated and non-cross-validated results are discussed as well as a method for the production of a true cross-validated neural network regression model. It is shown that the methods must be compared through the errors produced in the validation sets as well as those given for the final model. Various methods for calculation of errors are described and compared. The separation of training, validation and test sets into fully independent groups is emphasized. PLS outperforms PCR using all indicators. ANNs are inferior to multivariate techniques for in idual compounds but are reasonably effective in predicting the sum of PAHs in the mixture set.
Principal signalling complexes in haematopoiesis: Structural aspects and mimetic discovery
Publisher: Elsevier BV
Date: 08-2011
DOI: 10.1016/J.CYTOGFR.2011.09.001
Abstract: Blood production is a highly regulated process involving multiple inhibitory and stimulatory cytokines present in the haematopoietic stem cell niche. Small molecules mimics of these signalling molecules have substantial potential as drugs and in the development of bioreactors to generate blood products. We review the structural biology of the extracellular signalling domains of five of the most important cytokines, analyze their structure-property relationships, and summarize the progress in developing small molecule mimics using the molecular information from structural biology and mutation studies.
Opening the black box of neural networks: methods for interpreting neural network models in clinical applications
Publisher: AME Publishing Company
Date: 06-2018
An investigation into FTIR spectroscopy as a biodiagnostic tool for cervical cancer
Publisher: Wiley
Date: 07-12-1996
DOI: 10.1002/(SICI)1520-6343(1996)2:3<143::AID-BSPY1>3.0.CO;2-9
The synthesis of bisguanidinoalkanes and guanidinoalkanes, N- or N'-substituted with pyrimidines, as analogues of chlorhexidine
Publisher: CSIRO Publishing
Date: 1996
DOI: 10.1071/CH9960573
Abstract: A series of N,N???- alkanediylbis [N?-(5-halopyrimidin-2-yl)guanidine] salts has been synthesized along with N,N???-(trans-cyclohexane-1,4-diyl) bis [N'-(5-chloropyrimidin-2-yl)guanidine], N,N???-(cis-cyclohexane-1,4-diyl) bis [N?-(5-chloropyrimidin-2-yl)guanidine] dihydrochloride and N-(cis-4-amino-cyclohexan-1-yl)-N'-(5-chloropyrimidin-2-yl)guanidine dihydrochloride . Furthermore, a series of N-(alkan-1-yl)-N?-(5-chloropyrimidin-2yl)guanidine hydrochlorides and N-(6-aminohexan-1-yl)-N?-(5-chloropyrimidin-2-yl)guanidine dihydrochloride were synthesized. This series of compounds was prepared by displacement reactions of 2-methylsulfonylpyrimidines with bisguanidinoalkanes or by condensation of 5-chloro-2-cyanoaminopyrimidine (5-chloropyrimidin-2-ylcyanamide) with alkylamines .
Antifibrotic Strategies for Medical Devices
Publisher: Elsevier BV
Date: 12-2020
Erratum to Orbital based electronic structural signatures of the guanine keto G-7H/G-9H tautomer pair as studied using dual space analysis [Biophys. Chem. 121 (2006) 105-120] (DOI:10.1016/j.bpc.2005.12.006)
Publisher: Elsevier BV
Date: 02-2007
Use of artificial intelligence and machine learning for discovery of drugs for neglected tropical diseases
Publisher: Frontiers Media SA
Date: 15-03-2021
DOI: 10.3389/FCHEM.2021.614073
Abstract: Neglected tropical diseases continue to create high levels of morbidity and mortality in a sizeable fraction of the world’s population, despite ongoing research into new treatments. Some of the most important technological developments that have accelerated drug discovery for diseases of affluent countries have not flowed down to neglected tropical disease drug discovery. Pharmaceutical development business models, cost of developing new drug treatments and subsequent costs to patients, and accessibility of technologies to scientists in most of the affected countries are some of the reasons for this low uptake and slow development relative to that for common diseases in developed countries. Computational methods are starting to make significant inroads into discovery of drugs for neglected tropical diseases due to the increasing availability of large databases that can be used to train ML models, increasing accuracy of these methods, lower entry barrier for researchers, and widespread availability of public domain machine learning codes. Here, the application of artificial intelligence, largely the subset called machine learning, to modelling and prediction of biological activities and discovery of new drugs for neglected tropical diseases is summarized. The pathways for the development of machine learning methods in the short to medium term and the use of other artificial intelligence methods for drug discovery is discussed. The current roadblocks to, and likely impacts of, synergistic new technological developments on the use of ML methods for neglected tropical disease drug discovery in the future are also discussed.
Robust, quantitative tools for modelling ex-vivo expansion of haematopoietic stem cells and progenitors
Publisher: Royal Society of Chemistry (RSC)
Date: 2012
DOI: 10.1039/C2MB05439F
Abstract: Despite substantial research activity on bioreactor design and experiments, there are very few reports of modelling tools that can be used to generate predictive models describing how bioreactor parameters affect performance. New developments in mathematics, such as sparse Bayesian feature selection methods and nonlinear model-free modelling regression methods, offer considerable promise for modelling erse types of data. The utility of these mathematical tools in stem cell biology are demonstrated by analysis of a large set of bioreactor data derived from the literature. In spite of the ersity of the data sources, and the inherent difficulty in representing bioreactor variables, these modelling methods were able to develop robust, quantitative, predictive models. These models relate bioreactor operational parameters to the degree of expansion of haematopoietic stem cells or their progenitors, and also identify the bioreactor variables that are most likely to affect performance across many experiments. These methods show substantial promise in assisting the design and optimisation of stem cell bioreactors.
DETECTION OF J=2-] 1 EMISSION OF ACETONITRILE (CH3CN) IN SGR B2
Publisher: Oxford University Press (OUP)
Date: 09-1977
Structural impact on the methano bridge in norbornadiene, norbornene and norbornane
Publisher: Elsevier BV
Date: 12-2004
Capturing the Crystal: Prediction of Enthalpy of Sublimation, Crystal Lattice Energy, and Melting Points of Organic Compounds
Publisher: American Chemical Society (ACS)
Date: 02-01-2013
DOI: 10.1021/CI3005012
Abstract: Accurate computational prediction of melting points and aqueous solubilities of organic compounds would be very useful but is notoriously difficult. Predicting the lattice energies of compounds is key to understanding and predicting their melting behavior and ultimately their solubility behavior. We report robust, predictive, quantitative structure-property relationship (QSPR) models for enthalpies of sublimation, crystal lattice energies, and melting points for a very large and structurally erse set of small organic compounds. Sparse Bayesian feature selection and machine learning methods were employed to select the most relevant molecular descriptors for the model and to generate parsimonious quantitative models. The final enthalpy of sublimation model is a four-parameter multilinear equation that has an r(2) value of 0.96 and an average absolute error of 7.9 ± 0.3 kJ.mol(-1). The melting point model can predict this property with a standard error of 45° ± 1 K and r(2) value of 0.79. Given the size and ersity of the training data, these conceptually transparent and accurate models can be used to predict sublimation enthalpy, lattice energy, and melting points of organic compounds in general.
Parasiticidal 2-alkoxy- and 2-aryloxyiminoalkyl trifluoromethanesulfonanilides
Publisher: Elsevier BV
Date: 2008
DOI: 10.1016/J.BMCL.2007.10.090
Abstract: A series of novel 2-alkoxy- and 2-aryloxyiminoalkyl trifluoromethanesulfonanilide derivatives have shown significant in vitro parasiticidal activity against the ectoparasites Ctenocephalides felis and Rhipicephalus sanguineus. A number of these compounds also displayed significant in vitro endoparasite activity against the nematode Haemonchus contortus.
Predicting maximum bioactivity by effective inversion of neural networks using genetic algorithms
Publisher: Elsevier BV
Date: 10-1997
Potent In Vitro Peptide Antagonists of the Thrombopoietin Receptor as Potential Myelofibrosis Drugs
Publisher: Wiley
Date: 20-01-2021
Robust modelling of solubility in supercritical carbon dioxide using Bayesian methods
Publisher: Elsevier BV
Date: 04-2010
DOI: 10.1016/J.JMGM.2009.12.004
Abstract: Two sparse Bayesian methods were used to derive predictive models of solubility of organic dyes and polycyclic aromatic compounds in supercritical carbon dioxide (scCO(2)), over a wide range of temperatures (285.9-423.2K) and pressures (60-1400 bar): a multiple linear regression employing an expectation maximization algorithm and a sparse prior (MLREM) method and a non-linear Bayesian Regularized Artificial Neural Network with a Laplacian Prior (BRANNLP). A randomly selected test set was used to estimate the predictive ability of the models. The MLREM method resulted in a model of similar predictivity to the less sparse MLR method, while the non-linear BRANNLP method created models of substantially better predictivity than either the MLREM or MLR based models. The BRANNLP method simultaneously generated context-relevant subsets of descriptors and a robust, non-linear quantitative structure-property relationship (QSPR) model for the compound solubility in scCO(2). The differences between linear and non-linear descriptor selection methods are discussed.
Synthesis of homorhamnojirimycins and related trihydroxypipecolic acid derivatives via divergent bicyclic amino lactone intermediates: Inhibition of naringinase (L-rhamnosidase) and dTDP-rhamnose biosynthesis
Publisher: Royal Society of Chemistry (RSC)
Date: 1999
DOI: 10.1039/A904064A
Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture.
Publisher: Wiley
Date: 26-10-2022
Abstract: Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods-data collection, featurization, model generation, and model evaluation-and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature-property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.
Robust Prediction of Personalized Cell Recognition from a Cancer Population by a Dual Targeting Nanoparticle Library
Publisher: Wiley
Date: 20-10-2015
Computationally repurposed drugs and natural products against RNA dependent RNA polymerase as potential COVID-19 therapies
Publisher: Springer Science and Business Media LLC
Date: 20-09-2021
DOI: 10.1186/S43556-021-00050-3
Abstract: Repurposing of existing drugs and drug candidates is an ideal approach to identify new potential therapies for SARS-CoV-2 that can be tested without delay in human trials of infected patients. Here we applied a virtual screening approach using Autodock Vina and molecular dynamics simulation in tandem to calculate binding energies for repurposed drugs against the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). We thereby identified 80 promising compounds with potential activity against SARS-Cov2, consisting of a mixture of antiviral drugs, natural products and drugs with erse modes of action. A substantial proportion of the top 80 compounds identified in this study had been shown by others to have SARS-CoV-2 antiviral effects in vitro or in vivo, thereby validating our approach. Amongst our top hits not previously reported to have SARS-CoV-2 activity, were eribulin, a macrocyclic ketone analogue of the marine compound halichondrin B and an anticancer drug, the AXL receptor tyrosine kinase inhibitor bemcentinib. Our top hits from our RdRp drug screen may not only have utility in treating COVID-19 but may provide a useful starting point for therapeutics against other coronaviruses. Hence, our modelling approach successfully identified multiple drugs with potential activity against SARS-CoV-2 RdRp.
Neural networks as robust tools in drug lead discovery and development
Publisher: Springer Science and Business Media LLC
Date: 2004
DOI: 10.1385/MB:27:2:139
Triatomic molecules: meeting places for experiment and theory
Publisher: Elsevier BV
Date: 06-1990
Toward a Rosetta stone for the stem cell genome: Stochastic gene expression, network architecture, and external influences
Publisher: Elsevier BV
Date: 09-2008
DOI: 10.1016/J.SCR.2008.03.001
Abstract: We review literature relating to three types of factors known to influence stem cell behavior. These factors are stochastic gene expression, regulatory network architecture, and the influence of external signals, such as those emanating from the niche. Although these factors are considered separately, their shared evolutionary history necessitates integration. Stochastic gene expression pervades network components network architecture controls, modulates, or exploits this noise while performing additional computation and such complexity also interplays with factors external to cells. Adequate understanding of each of these components, and how they interact, will lead to a conceptual model of the stem cell regulatory system that can be used to drive hypothesis-driven research and facilitate interpretation of experimental data.
FACS in the 21st century
Publisher: Israel Chemical Society (ICS)
Date: 2020
DOI: 10.51167/ACM00002
Abstract: FACS is ideally positioned to be a powerful, inclusive, an outward-facing federation of chemical and allied societies in the Asia Pacific region. The Federation promotes networking and collaboration within the region and strong engagement in the broader international chemical community. Over the past three years, FACS has been refocused to capture these opportunities by the restructuring of three critical aspects of the FACS operations.
Analyzing 3D hyperspectral TOF-SIMS depth profile data using self-organizing map-relational perspective mapping
Publisher: American Vacuum Society
Date: 11-2020
DOI: 10.1116/6.0000614
Abstract: The advantages of applying multivariate analysis to mass spectrometry imaging (MSI) data have been thoroughly demonstrated in recent decades. The identification and visualization of complex relationships between pixels in a hyperspectral data set can provide unique insights into the underlying surface chemistry. It is now recognized that most MSI data contain nonlinear relationships, which has led to increased application of machine learning approaches. Previously, we exemplified the use of the self-organizing map (SOM), a type of artificial neural network, for analyzing time-of-flight secondary ion mass spectrometry (TOF-SIMS) hyperspectral images. Recently, we developed a novel methodology, SOM-relational perspective mapping (RPM), which incorporates the algorithm RPM to improve visualization of the SOM for 2D TOF-SIMS images. Here, we use SOM-RPM to characterize and interpret 3D TOF-SIMS depth profile data, voxel-by-voxel. An organic Irganox™ multilayer standard s le was depth profiled using TOF-SIMS, and SOM-RPM was used to create 3D similarity maps of the depth-profiled s le, in which the mass spectral similarity of in idual voxels is modeled with color similarity. We used this similarity map to segment the data into spatial features, demonstrating that the unsupervised method meaningfully differentiated between Irganox-3114 and Irganox-1010 nanometer-thin multilayer films. The method also identified unique clusters at the surface associated with environmental exposure and s le degradation. Key fragment ions characteristic of each cluster were identified, tying clusters to their underlying chemistries. SOM-RPM has the demonstrable ability to reduce vast data sets to simple 3D visualizations that can be used for clustering data and visualizing the complex relationships within.
Exploring the Relationship between Polymer Surface Chemistry and Bacterial Attachment Using ToF‐SIMS and Self‐Organizing maps
Publisher: Wiley
Date: 15-02-2023
Abstract: Biofilm formation is a major cause of hospital‐acquired infections. Research into biofilm‐resistant materials is therefore critical to reduce the frequency of these events. Polymer microarrays offer a high‐throughput approach to enable the efficient discovery of novel biofilm‐resistant polymers. Herein, bacterial attachment and surface chemistry are studied for a polymer microarray to improve the understanding of Pseudomonas aeruginosa biofilm formation on a erse set of polymeric surfaces. The relationships between time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) data and biofilm formation are analyzed using linear multivariate analysis (partial least squares [PLS] regression) and a nonlinear self‐organizing map (SOM). The SOM models revealed several combinations of fragment ions that are positively or negatively associated with bacterial biofilm formation, which are not identified by PLS. With these insights, a second PLS model is calculated, in which interactions between key fragments (identified by the SOM) are explicitly considered. Inclusion of these terms improved the PLS model performance and shows that, without such terms, certain key fragment ions correlated with bacterial attachment may not be identified. The chemical insights provided by the combination of PLS regression and SOM will be useful for the design of materials that support negligible pathogen attachment.
Simulation and modelling of chemical and biological complex systems
Publisher: CSIRO Publishing
Date: 2006
DOI: 10.1071/CH06375
Abstract: Most sciences, and notably chemistry and biology, are becoming more interdisciplinary with overlaps between disciplines providing fertile new fields of research. As scientists attempt to model more complicated matter such as protein complexes, regulatory networks, cells, smart materials, biomaterials, and the like, it is clear that the complexity of these systems is difficult to describe using traditional reductionist tools. We describe how the tools and concepts of complex systems science may be applied to the simulation and modelling of complex chemical and biological systems.
Identifying medically relevant xenon protein targets by in silico screening of the structural proteome
Publisher: Medknow
Date: 2023
New QSAR methods applied to structure-activity mapping and combinatorial chemistry
Publisher: American Chemical Society (ACS)
Date: 13-10-1999
DOI: 10.1021/CI980070D
Molecular conformational studies of cellulose nitrate
Publisher: Elsevier BV
Date: 05-1986
HYPERFINE INTERACTIONS IN THE MICROWAVE-SPECTRUM OF 2-PROPEN-1-IMINE (VINYLIMINE)
Publisher: Elsevier BV
Date: 08-1981
Discovery of synergistic material-topography combinations to achieve immunomodulatory osteoinductive biomaterials using a novel in vitro screening method: The ChemoTopoChip
Publisher: Elsevier BV
Date: 04-2021
European Registry of Materials: global, unique identifiers for (undisclosed) nanomaterials
Publisher: Springer Science and Business Media LLC
Date: 24-08-2022
DOI: 10.1186/S13321-022-00614-7
Abstract: Management of nanomaterials and nanosafety data needs to operate under the FAIR (findability, accessibility, interoperability, and reusability) principles and this requires a unique, global identifier for each nanomaterial. Existing identifiers may not always be applicable or sufficient to definitively identify the specific nanomaterial used in a particular study, resulting in the use of textual descriptions in research project communications and reporting. To ensure that internal project documentation can later be linked to publicly released data and knowledge for the specific nanomaterials, or even to specific batches and variants of nanomaterials utilised in that project, a new identifier is proposed: the European Registry of Materials Identifier. We here describe the background to this new identifier, including FAIR interoperability as defined by FAIRSharing, identifiers.org, Bioregistry, and the CHEMINF ontology, and show how it complements other identifiers such as CAS numbers and the ongoing efforts to extend the InChI identifier to cover nanomaterials. We provide ex les of its use in various H2020-funded nanosafety projects. Graphical Abstract
Application of neural networks to large dataset QSAR, virtual screening, and library design
Publisher: Humana Press
Date: 2002
Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures
Publisher: Wiley
Date: 22-08-2021
Abstract: The bandgap is one of the most fundamental properties of condensed matter. However, an accurate calculation of its value, which could potentially allow experimentalists to identify materials suitable for device applications, is very computationally expensive. Here, active machine learning algorithms are used to leverage a limited number of accurate density functional theory calculations to robustly predict the bandgap of a very large number of novel 2D heterostructures. Using this approach, a database of ≈2.2 million bandgap values for various novel 2D van der Waals heterostructures is produced.
A renaissance of neural networks in drug discovery
Publisher: Informa UK Limited
Date: 04-07-2016
DOI: 10.1080/17460441.2016.1201262
Abstract: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to choose the most appropriate approach. In this review, the authors discuss traditional and newly emerging neural network approaches to drug discovery. Their focus is on backpropagation neural networks and their variants, self-organizing maps and associated methods, and a relatively new technique, deep learning. The most important technical issues are discussed including overfitting and its prevention through regularization, ensemble and multitask modeling, model interpretation, and estimation of applicability domain. Different aspects of using neural networks in drug discovery are considered: building structure-activity models with respect to various targets predicting drug selectivity, toxicity profiles, ADMET and physicochemical properties characteristics of drug-delivery systems and virtual screening. Neural networks continue to grow in importance for drug discovery. Recent developments in deep learning suggests further improvements may be gained in the analysis of large chemical data sets. It's anticipated that neural networks will be more widely used in drug discovery in the future, and applied in non-traditional areas such as drug delivery systems, biologically compatible materials, and regenerative medicine.
Toward Interpretable Machine Learning Models for Materials Discovery
Publisher: Wiley
Date: 14-10-2019
Journal of Molecular Graphics and Modelling: Foreword
Publisher: Elsevier BV
Date: 03-2003
Exploring structure-property relationships in magnesium dissolution modulators
Publisher: Springer Science and Business Media LLC
Date: 08-01-2021
DOI: 10.1038/S41529-020-00148-Z
Abstract: Small organic molecules that modulate the degradation behavior of Mg constitute benign and useful materials to modify the service environment of light metal materials for specific applications. The vast chemical space of potentially effective compounds can be explored by machine learning-based quantitative structure-property relationship models, accelerating the discovery of potent dissolution modulators. Here, we demonstrate how unsupervised clustering of a large number of potential Mg dissolution modulators by structural similarities and sketch-maps can predict their experimental performance using a kernel ridge regression model. We compare the prediction accuracy of this approach to that of a prior artificial neural networks study. We confirm the robustness of our data-driven model by blind prediction of the dissolution modulating performance of 10 untested compounds. Finally, a workflow is presented that facilitates the automated discovery of chemicals with desired dissolution modulating properties from a commercial database. We subsequently prove this concept by blind validation of five chemicals.
Energy Interplay in Materials: Unlocking Next-Generation Synchronous Multisource Energy Conversion with Layered 2D Crystals
Publisher: Wiley
Date: 02-08-2022
Abstract: Layered 2D crystals have unique properties and rich chemical and electronic ersity, with over 6000 2D crystals known and, in principle, millions of different stacked hybrid 2D crystals accessible. This ersity provides unique combinations of properties that can profoundly affect the future of energy conversion and harvesting devices. Notably, this includes catalysts, photovoltaics, superconductors, solar‐fuel generators, and piezoelectric devices that will receive broad commercial uptake in the near future. However, the unique properties of layered 2D crystals are not limited to in idual applications and they can achieve exceptional performance in multiple energy conversion applications synchronously. This synchronous multisource energy conversion (SMEC) has yet to be fully realized but offers a real game‐changer in how devices will be produced and utilized in the future. This perspective highlights the energy interplay in materials and its impact on energy conversion, how SMEC devices can be realized, particularly through layered 2D crystals, and provides a vision of the future of effective environmental energy harvesting devices with layered 2D crystals.
Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties
Publisher: American Chemical Society (ACS)
Date: 17-01-2012
DOI: 10.1021/CR200066H
THE CONFORMATIONS OF 1,1,2,2-TETRACYANOETHANE AND 1,2-DICHLOROTETRACYANOETHANE - A MOLECULAR-ORBITAL, DIPOLE-MOMENT AND KERR EFFECT STUDY
Publisher: Elsevier BV
Date: 02-1988
Conformational energy calculations and electrostatic potentials of dihydrofolate reductase ligands: relevance to mode of binding and species specificity
Publisher: American Chemical Society (ACS)
Date: 05-1986
DOI: 10.1021/JM00155A020
Abstract: Classical potential energy calculations are reported for a series of 11 structurally erse substrates, products, and inhibitors of dihydrofolate reductase. In almost every case, the calculations reveal a range of potential biologically active conformations accessible to the molecule, and geometry optimization with molecular mechanics and molecular orbital calculations further expands the range of accessible conformations. The energy calculations are supplemented with electrostatic potential energy surfaces for the heterocyclic components of each molecule. These data are used in conjunction with the energy calculations and the crystallographically determined enzyme structures to compare two alternative proposed binding modes of folates known to bind with their pteridine rings inverted relative to that of methotrexate. It is shown that the conformational flexibility of the connecting chain between the benzoyl glutamate and pteridine moieties in the folates actually allows the pteridine ring to shift between these alternative binding modes, a combination of which may offer the best explanation for the observed activity. The electrostatic potentials and conformational energy data are also used in an attempt to account for the species specificity of inhibitors of mammalian, bacterial, and protozoal dihydrofolate reductases. The results show that while these techniques can be used to explain many of the observed results, others require recourse to the observed crystal structures to provide a satisfactory explanation.
Conducting Polymers from Polybutadiene: Molecular Configuration Effects on the Iodine-Induced Conjugation Reactions
Publisher: American Chemical Society (ACS)
Date: 11-1994
DOI: 10.1021/MA00101A008
Toward a systematic exploration of nano-bio interactions
Publisher: Elsevier BV
Date: 05-2017
Use of automatic relevance determination in QSAR studies using Bayesian neural networks
Publisher: American Chemical Society (ACS)
Date: 28-10-2000
DOI: 10.1021/CI000450A
Role of Artificial Intelligence and Machine Learning in Nanosafety
Publisher: Wiley
Date: 15-06-2020
Illuminating flash point: Comprehensive prediction models
Publisher: Wiley
Date: 04-11-2015
Abstract: Flash point is an important property of chemical compounds that is widely used to evaluate flammability hazard. However, there is often a significant gap between the demand for experimental flash point data and their availability. Furthermore, the determination of flash point is difficult and costly, particularly for some toxic, explosive, or radioactive compounds. The development of a reliable and widely applicable method to predict flash point is therefore essential. In this paper, the construction of a quantitative structure - property relationship model with excellent performance and domain of applicability is reported. It uses the largest data set to date of 9399 chemically erse compounds, with flash point spanning from less than -130 °C to over 900 °C. The model employs only computed parameters, eliminating the need for experimental data that some earlier computational models required. The model allows accurate prediction of flash point for a broad range of compounds that are unavailable or not yet synthesized. This single model with a very broad range of chemical and flash point applicability will allow accurate predictions of this important property to be made for a broad range of new materials.
Relevance Vector Machines: Sparse Classification Methods for QSAR
Publisher: American Chemical Society (ACS)
Date: 21-07-2015
Abstract: Sparse machine learning methods have provided substantial benefits to quantitative structure property modeling, as they make model interpretation simpler and generate models with improved predictivity. Sparsity is usually induced via Bayesian regularization using sparsity-inducing priors and by the use of expectation maximization algorithms with sparse priors. The focus to date has been on using sparse methods to model continuous data and to carry out sparse feature selection. We describe the relevance vector machine (RVM), a sparse version of the support vector machine (SVM) that is one of the most widely used classification machine learning methods in QSAR and QSPR. We illustrate the superior properties of the RVM by modeling eight data sets using SVM, RVM, and another sparse Bayesian machine learning method, the Bayesian regularized artificial neural network with Laplacian prior (BRANNLP). We show that RVM models are substantially sparser than the SVM models and have similar or superior performance to them.
Quantitative structure-activity studies of pyrethroids. 27. Physicochemical substituent effects of meta-phenoxybenzyl α,α-disubstituted acetates on insecticidal activity (II)
Publisher: Elsevier BV
Date: 09-1991
Using artificial neural networks to predict biological activity from simple molecular structural considerations
Publisher: Wiley
Date: 1996
Discovery of (Z)-2-phenyl-3-(1H-pyrrol-2-yl)acrylonitrile derivatives active against Haemonchus contortus and Ctenocephalides felis (cat flea)
Publisher: Elsevier BV
Date: 02-2007
DOI: 10.1016/J.BMCL.2006.11.043
Abstract: A series of 2-phenyl-3-(1H-pyrrol-2-yl)acrylonitrile derivatives were synthesized and evaluated for in vitro activity against the endoparasite Haemonchus contortus and the ectoparasite Ctenocephalides felis. Some compounds had significant in vitro activity against these parasites.
Neural networks in ADME and toxicity prediction
Publisher: Portico
Date: 2004
Coatings Releasing the Relaxin Peptide Analogue B7-33 Reduce Fibrotic Encapsulation
Publisher: American Chemical Society (ACS)
Date: 12-11-2019
Abstract: The development of antifibrotic materials and coatings that can resist the foreign body response (FBR) continues to present a major hurdle in the advancement of current and next-generation implantable medical devices, biosensors, and cell therapies. From an implant perspective, the most important issue associated with the FBR is the prolonged inflammatory response leading to a collagenous capsule that ultimately blocks mass transport and communication between the implant and the surrounding tissue. Up to now, most attempts to reduce the capsule thickness have focused on providing surface coatings that reduce protein fouling and cell attachment. Here, we present an approach that is based on the sustained release of a peptide drug interfering with the FBR. In this study, the biodegradable polymer poly(lactic-
Core molecular orbital contribution to N2O isomerization as studied using theoretical electron momentum spectroscopy
Publisher: Elsevier BV
Date: 2001
Multivariate analysis of ToF‐SIMS data using mass segmented peak lists
Publisher: Wiley
Date: 10-05-2018
DOI: 10.1002/SIA.6462
Molecular Modeling Studies of “Flap Up” Mannosyl Cation Mimics
Publisher: American Chemical Society (ACS)
Date: 1996
DOI: 10.1021/JM960237Z
Electron momentum spectroscopy and density functional theory investigation into the complete valence electronic structure of ethylene oxide
Publisher: IOP Publishing
Date: 1999
Self-Organizing Map and Relational Perspective Mapping for the Accurate Visualization of High-Dimensional Hyperspectral Data
Publisher: American Chemical Society (ACS)
Date: 02-07-2020
Ultrafast Fabrication of Covalently Cross‐linked Multifunctional Graphene Oxide Monoliths
Publisher: Wiley
Date: 23-04-2014
Predictive Bayesian neural network models of MHC class II peptide binding
Publisher: Elsevier BV
Date: 06-2005
DOI: 10.1016/J.JMGM.2005.03.001
Abstract: We used Bayesian regularized neural networks to model data on the MHC class II-binding affinity of peptides. Training data consisted of sequences and binding data for nonamer (nine amino acid) peptides. Independent test data consisted of sequences and binding data for peptides of length 0.8. We also used both amino acid indicator variables (bin20) and property-based descriptors to generate models for MHC class II-binding of peptides. The property-based descriptors were more parsimonious than the indicator variable descriptors, making them applicable to larger peptides, and their design makes them able to generalize to unknown peptides outside of the training space. None of the external test data sets contained any of the nonamer sequences in the training sets. Consequently, the models attempted to predict the activity of truly unknown peptides not encountered in the training sets. Our models were well able to tackle the difficult problem of correctly predicting the MHC class II-binding activities of a majority of the test set peptides. Exceptions to the assumption that nonamer motif activities were invariant to the peptide in which they were embedded, together with the limited coverage of the test data, and the fuzziness of the classification procedure, are likely explanations for some misclassifications.
Modelling human embryoid body cell adhesion to a combinatorial library of polymer surfaces
Publisher: Royal Society of Chemistry (RSC)
Date: 2012
DOI: 10.1039/C2JM34782B
Modelling Inhalational Anaesthetics Using Bayesian Feature Selection and QSAR Modelling Methods
Publisher: Wiley
Date: 23-07-2010
Abstract: The development of robust and predictive QSAR models is highly dependent on the use of molecular descriptors that contain information relevant to the property being modelled. Selection of these relevant features from a large pool of possibilities is difficult to achieve effectively. Modern Bayesian methods provide substantial advantages over conventional feature selection methods for feature selection and QSAR modelling. We illustrate the importance of descriptor choice and the beneficial properties of Bayesian methods to select context-dependent relevant descriptors and build robust QSAR models, using data on anaesthetics. Our results show the effectiveness of Bayesian feature selection methods in choosing the best descriptors when these are mixed with less informative descriptors. They also demonstrate the efficacy of the Abraham descriptors and identify deficiencies in ParaSurf descriptors for modelling anaesthetic action.
Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches
Publisher: Informa UK Limited
Date: 06-04-2016
DOI: 10.3109/17435390.2016.1161857
Abstract: The number of engineered nanomaterials (ENMs) being exploited commercially is growing rapidly, due to the novel properties they exhibit. Clearly, it is important to understand and minimize any risks to health or the environment posed by the presence of ENMs. Data-driven models that decode the relationships between the biological activities of ENMs and their physicochemical characteristics provide an attractive means of maximizing the value of scarce and expensive experimental data. Although such structure-activity relationship (SAR) methods have become very useful tools for modelling nanotoxicity endpoints (nanoSAR), they have limited robustness and predictivity and, most importantly, interpretation of the models they generate is often very difficult. New computational modelling tools or new ways of using existing tools are required to model the relatively sparse and sometimes lower quality data on the biological effects of ENMs. The most commonly used SAR modelling methods work best with large datasets, are not particularly good at feature selection, can be relatively opaque to interpretation, and may not account for nonlinearity in the structure-property relationships. To overcome these limitations, we describe the application of a novel algorithm, a genetic programming-based decision tree construction tool (GPTree) to nanoSAR modelling. We demonstrate the use of GPTree in the construction of accurate and interpretable nanoSAR models by applying it to four erse literature datasets. We describe the algorithm and compare model results across the four studies. We show that GPTree generates models with accuracies equivalent to or superior to those of prior modelling studies on the same datasets. GPTree is a robust, automatic method for generation of accurate nanoSAR models with important advantages that it works with small datasets, automatically selects descriptors, and provides significantly improved interpretability of models.
Conformational analysis of picrotoxinin by N.M.R., X-ray crystallography, and molecular orbital and classical potential-energy calculations
Publisher: CSIRO Publishing
Date: 1983
DOI: 10.1071/CH9832219
Abstract: The conformation of the potent convulsant drug picrotoxinin has been studied by proton n.m.r., X-ray crystallography, molecular orbital calculations and classical calculations. The calculations reveal two alternative low-energy conformations, either of which is consistent with the n.m.r. data, and one of which is also observed crystallographically. The energy difference is sufficiently small to suggest that either conformation may be the biologically active form.
Potent antimalarial drugs with validated activities
Publisher: Springer Science and Business Media LLC
Date: 23-02-2022
Identifying SARS-CoV-2 Drugs Binding to the Spike Fatty Acid Binding Pocket Using In Silico Docking and Molecular Dynamics
Publisher: MDPI AG
Date: 20-02-2023
DOI: 10.3390/IJMS24044192
Abstract: Drugs against novel targets are needed to treat COVID-19 patients, especially as SARS-CoV-2 is capable of rapid mutation. Structure-based de novo drug design and repurposing of drugs and natural products is a rational approach to discovering potentially effective therapies. These in silico simulations can quickly identify existing drugs with known safety profiles that can be repurposed for COVID-19 treatment. Here, we employ the newly identified spike protein free fatty acid binding pocket structure to identify repurposing candidates as potential SARS-CoV-2 therapies. Using a validated docking and molecular dynamics protocol effective at identifying repurposing candidates inhibiting other SARS-CoV-2 molecular targets, this study provides novel insights into the SARS-CoV-2 spike protein and its potential regulation by endogenous hormones and drugs. Some of the predicted repurposing candidates have already been demonstrated experimentally to inhibit SARS-CoV-2 activity, but most of the candidate drugs have yet to be tested for activity against the virus. We also elucidated a rationale for the effects of steroid and sex hormones and some vitamins on SARS-CoV-2 infection and COVID-19 recovery.
THE NON-FICKIAN DIFFUSION OF DETERRENTS INTO A NITROCELLULOSE-BASED PROPELLANT
Publisher: Wiley
Date: 1988
Validating Eaton's Hypothesis: Cubane as a Benzene Bioisostere
Publisher: Wiley
Date: 05-02-2016
PREPARATION OF BICYCLIC HERBICIDE PRECURSORS BY INTRAMOLECULAR STORK-DANHEISER KINETIC ALKYLATION REACTIONS OF METHYL 1-(HALOALKYL)-3-METHOXY-5-OXOCYCLOHEX-3-ENE-1-CARBOXYLATE DERIVATIVES
Publisher: CSIRO Publishing
Date: 1992
DOI: 10.1071/CH9920759
Abstract: Reductive alkylation of methyl 3,5-dimethoxybenzoate with the dibromoalkane and the bromochloroalkane derivatives (4a-d) and (4e-g) afforded, after acid hydrolysis, the corresponding methyl 1-( haloalkyl )-3-methoxy-5-oxocyclohex-3-ene-1-carboxylate derivatives (6a-g). Reaction of (6a-c) with lithium diisopropylamide afforded methyl 3-methoxy-5-oxobicyclo[4.2.0]oct-3-ene- 1-carboxylate (2a), methyl 5-methoxy-7-oxo-1,2,3,4,7,7a-hexahydro-3aH-indene-3a-carboxylate (2b) and methyl 6-methoxy-8-oxo-1,3,4,5,8,8a-hexahydronaphtha1ene-4a(2 H)- carboxylate (2c), respectively. Compounds (2b) and (2c) were also obtained from the reaction of (6f) and (6g) with lithium diisopropylamide . Compounds (2a) and (2b) were each obtained as a single bridgehead isomer, the relative stereochemistry of ring fusion of which was assigned as cis on the basis of semiempirical molecular orbital calculations. Compound (2c) was obtained as a mixture of cis-and trans-fused bridgehead isomers. The ester (2b) was converted into the herbicide methyl 5,7-dioxo-6-[1-[(prop-2-enyloxy) imino ] butyl]octahydro-3aH-indene-3a-carboxylate (3a).
Nuclear quadrupole coupling in the microwave spectrum of 1,2,3-triazole
Publisher: Elsevier BV
Date: 05-1977
Influence of benign cellular changes in diagnosis of cervical cancer using IR microspectroscopy
Publisher: Wiley
Date: 2002
DOI: 10.1002/BIP.10114
Abstract: IR spectroscopy and principal components analysis (PCA) of endocervical cells and smears diagnosed with benign cellular changes were investigated to determine the influence of these potential confounding variables in the diagnosis of cervical cancer. Spectral differences in all cell and diagnostic types investigated were found in the phosphodiester and carbohydrate regions. However, the spectral differences in other bands were not distinct enough to allow differentiation between groups. The PCA was successfully used to obtain a separation of normal ectocervical smears from normal endocervical cells and smears diagnosed with inflammation, Candida albicans , and bacterial vaginosis. A separation with a slight overlap of abnormal ectocervical smears from normal endocervical cells, inflammation, and bacterial vaginosis was obtained with PCA. Candida was not separated from abnormal ectocervical smears with any success. © 2002 Wiley Periodicals Inc. Biopolymers (Biospectroscopy) 67: 362–366, 2002
Mesenchymal stromal cell turnover in the normal adult lung revisited
Publisher: American Physiological Society
Date: 11-2013
DOI: 10.1152/AJPLUNG.00092.2013
Abstract: We have employed a simple and robust noninvasive method of continuous in vivo long-term bromodeoxyuridine (BrdU) labeling to analyze lung mesenchymal stromal cell turnover in adult mice in the steady state. Mathematical modeling of BrdU uptake in flow cytometrically sorted CD45 neg CD31 neg Sca-1 pos lung cells following long-term feeding of BrdU to mice in their drinking water reveals that lung mesenchymal stromal cells cycle continuously throughout life. Analysis of BrdU incorporation during long-term feeding and during chasing (delabeling) following replacement of BrdU-water with normal water shows that the CD45 neg CD31 neg Sca-1 pos lung mesenchymal stromal cell compartment turns over at a rate of ∼2.26% per day with a time to half-cycled of 44 days, an estimated cell proliferation rate of 0.004/day, and a cell death rate of 0.018/day.
Towards chromate-free corrosion inhibitors: Structure-property models for organic alternatives
Publisher: Royal Society of Chemistry (RSC)
Date: 2014
DOI: 10.1039/C3GC42540A
Abstract: Progressive restrictions on the use of toxic chromate-based corrosion inhibitors present serious technical challenges.
INVALIDATION OF LOWRY PROTEIN ESTIMATIONS AND MACROPHAGE [H-3] DEOXYGLUCOSE UPTAKE BY PLASTIC TUBES AND AIR-FLOW
Publisher: Elsevier BV
Date: 04-1987
DOI: 10.1016/0022-1759(87)90018-4
Abstract: Nosocomial infection of health-care workers (HCWs) during outbreaks of respiratory infections (e.g. Influenza A H1N1 (2009)) is a significant concern for public health policy makers. World Health Organization (WHO)-defined 'aerosol generating procedures' (AGPs) are thought to increase the risk of aerosol transmission to HCWs, but there are presently insufficient data to quantify risk accurately or establish a hierarchy of risk-prone procedures. This study measured the amount of H1N1 (2009) RNA in aerosols in the vicinity of H1N1 positive patients undergoing AGPs to help quantify the potential risk of transmission to HCWs. There were 99 s ling occasions (windows) producing a total of 198 May stages for analysis in the size ranges 0.86-7.3 µm. Considering stages 2 (4-7.3 µm) and 3 (0.86-4 µm) as comprising one s le, viral RNA was detected in 14 (14.1%) air s les from 10 (25.6%) patients. Twenty three air s les were collected while potential AGPs were being performed of which 6 (26.1%) contained viral RNA in contrast, 76 May s les were collected when no WHO 2009 defined AGP was being performed of which 8 (10.5%) contained viral RNA (unadjusted OR = 2.84 (95% CI 1.11-7.24) adjusted OR = 4.31 (0.83-22.5)). With our small s le size we found that AGPs do not significantly increase the probability of s ling an H1N1 (2009) positive aerosol (OR (95% CI) = 4.31 (0.83-22.5). Although the probability of detecting positive H1N1 (2009) positive aerosols when performing various AGPs on intensive care patients above the baseline rate (i.e. in the absence of AGPs) did not reach significance, there was a trend towards hierarchy of AGPs, placing bronchoscopy and respiratory and airway suctioning above baseline (background) values. Further, larger studies are required but these preliminary findings may be of benefit to infection control teams.
Robust QSAR models using Bayesian regularized neural networks
Publisher: American Chemical Society (ACS)
Date: 24-07-1999
DOI: 10.1021/JM980697N
Modelling and predicting the biological effects of nanomaterials
Publisher: Informa UK Limited
Date: 02-2014
Ligand Entropy Is Hard but Should Not Be Ignored
Publisher: American Chemical Society (ACS)
Date: 14-10-2020
Predicting the Complex Phase Behavior of Self-Assembling Drug Delivery Nanoparticles
Publisher: American Chemical Society (ACS)
Date: 20-03-2013
DOI: 10.1021/MP3006402
Abstract: Amphiphilic lyotropic liquid crystalline self-assembled nanomaterials have important applications in the delivery of therapeutic and imaging agents. However, little is known about the effect of the incorporated drug on the structure of nanoparticles. Predicting these properties is widely considered intractable. We present computational models for three drug delivery carriers, loaded with 10 drugs at six concentrations and two temperatures. These models predicted phase behavior for 11 new drugs. Subsequent synchrotron small-angle X-ray scattering experiments validated the predictions.
Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery
Publisher: American Chemical Society (ACS)
Date: 21-07-2022
DOI: 10.1021/ACS.CHEMREV.2C00061
Abstract: Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro hotocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro hotocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro hotocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro hotocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.
Rapid Prediction of Chemical Metabolism by Human UDP-glucuronosyltransferase Isoforms Using Quantum Chemical Descriptors Derived with the Electronegativity Equalization Method
Publisher: American Chemical Society (ACS)
Date: 15-09-2004
DOI: 10.1021/JM0495529
Abstract: This study aimed to evaluate in silico models based on quantum chemical (QC) descriptors derived using the electronegativity equalization method (EEM) and to assess the use of QC properties to predict chemical metabolism by human UDP-glucuronosyltransferase (UGT) isoforms. Various EEM-derived QC molecular descriptors were calculated for known UGT substrates and nonsubstrates. Classification models were developed using support vector machine and partial least squares discriminant analysis. In general, the most predictive models were generated with the support vector machine. Combining QC and 2D descriptors (from previous work) using a consensus approach resulted in a statistically significant improvement in predictivity (to 84%) over both the QC and 2D models and the other methods of combining the descriptors. EEM-derived QC descriptors were shown to be both highly predictive and computationally efficient. It is likely that EEM-derived QC properties will be generally useful for predicting ADMET and physicochemical properties during drug discovery.
STRUCTURE ACTIVITY RELATIONSHIPS OF CONVULSANT AND ANTI-CONVULSANT BARBITURATES - A COMPUTER-GRAPHIC-BASED PATTERN-RECOGNITION ANALYSIS
Publisher: American Chemical Society (ACS)
Date: 09-1983
DOI: 10.1021/JM00363A004
Abstract: A computer-graphic-based pattern-recognition study of two series of 5-ethyl-5-substituted barbiturates has been undertaken in an attempt to find a correlation between molecular conformation and convulsant and anticonvulsant activity. Studies of a first (trial) set of barbiturates related to pentobarbital revealed a region of space in which at least one low-energy conformation of the hydrocarbon side chain of each of the anticonvulsant barbiturates resides. Another region was occupied by a low-energy conformation of each of the convulsant barbiturates. These regions of space are, thus, possible pharmacophores for convulsant and anticonvulsant activity. Analysis of a second (test) set of barbiturates related to phenobarbital has shown that the activities and structures of these molecules are consistent with the above model. These pharmacophores thus provide a basis for the design of rigid, new analogues with potent convulsant or anticonvulsant activities.
Protein corona formation on silver nanoparticles under different conditions
Publisher: Elsevier BV
Date: 10-2022
Discovery of a Novel Polymer for Human Pluripotent Stem Cell Expansion and Multilineage Differentiation
Publisher: Wiley
Date: 06-2015
Correction to Competitive Inhibition Mechanism of Acetylcholinesterase without Catalytic Active Site Interaction: Study on Functionalized C
Publisher: American Chemical Society (ACS)
Date: 12-12-2017
Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using Complementary DFT and Machine Learning Approaches
Publisher: Wiley
Date: 31-10-2018
Investigation of 5-HT4 agonist activities using molecular field analysis
Publisher: Royal Society of Chemistry (RSC)
Date: 1999
DOI: 10.1039/A806347H
Nanoparticle-protein corona complex: understanding multiple interactions between environmental factors, corona formation, and biological activity.
Publisher: Informa UK Limited
Date: 26-11-2022
DOI: 10.1080/17435390.2022.2025467
Abstract: The surfaces of pristine nanoparticles become rapidly coated by proteins in biological fluids, forming the so-called protein corona. The corona modifies key physicochemical characteristics of nanoparticle surfaces that modulate its biological and pharmacokinetic activity, biodistribution, and safety. In the two decades since the protein corona was identified, the importance of nanoparticles surface properties in regulating biological responses have been recognized. However, there is still a lack of clarity about the relationships between physiological conditions and corona composition over time, and how this controls biological activities/interactions. Here we review recent progress in characterizing the structure and composition of protein corona as a function of biological fluid and time. We summarize the influence of nanoparticle characteristics on protein corona composition and discuss the relevance of protein corona to the biological activity and fate of nanoparticles. The aim is to provide a critical summary of the key factors that affect protein corona formation (e.g. characteristics of nanoparticles and biological environment) and how the corona modulates biological activity, cellular uptake, biodistribution, and drug delivery. In addition to a discussion on the importance of the characterization of protein corona adsorbed on nanoparticle surfaces under conditions that mimic relevant physiological environment, we discuss the unresolved technical issues related to the characterization of nanoparticle-protein corona complexes during their journey in the body. Lastly, the paper offers a perspective on how the existing nanomaterial toxicity data obtained from
Network models in drug discovery and regenerative medicine
Publisher: Elsevier
Date: 2008
Computational modeling and prediction of the complex time-dependent phase behavior of lyotropic liquid crystals under in meso crystallization conditions
Publisher: American Chemical Society (ACS)
Date: 22-02-2013
DOI: 10.1021/CG301730Z
Online Learners’ Reading Ability Detection Based on Eye-Tracking Sensors
Publisher: MDPI AG
Date: 10-09-2016
DOI: 10.3390/S16091457
I2 -Doping of 1,4-polydienes
Publisher: Elsevier BV
Date: 03-1995
Controlling clipping probability in DMT transmission
Publisher: IEEE Comput. Soc
Date: 1998
A bright future for engineering piezoelectric 2D crystals
Publisher: Royal Society of Chemistry (RSC)
Date: 2022
DOI: 10.1039/D1CS00844G
Abstract: We explore piezoelectricity in 2D crystals, envisioning assessment, prediction, and engineering 2D piezoelectricity via chemical, computational, and physical approaches.
Application of DFT and EMS to the Study of Strained Organic Molecules
Publisher: CSIRO Publishing
Date: 1998
DOI: 10.1071/P97092
Abstract: Electron momentum spectroscopy (EMS) studies of the valence shells of [1.1.1]propellane, 1,3-butadiene, ethylene oxide and cubane are reviewed. Binding energy spectra were measured in the energy regime of 3·5–46·5 eV over a range of different target electron momenta, so that momentum distributions (MDs) could be determined for each ion state. Each experimental electron momentum distribution is compared with those calculated in the plane wave impulse approximation (PWIA) using both a triple-? plus polarisation level self-consistent field (SCF) wave function and a further range of basis sets as calculated using density functional theory (DFT). A critical comparison between the experimental and theoretical momentum distributions allows us to determine the ‘optimum’ wave function for each molecule from the basis sets we studied. This ‘optimum’ wave function then allows us to investigate chemically or biologically significant molecular properties of these molecules. EMS-DFT also shows promise in elucidating the character of molecular orbitals and the hybridisation state of atoms.
Molecular Markers for Pyrethrin Autoxidation in Stored Pyrethrum Crop: Analysis and Structure Determination
Publisher: American Chemical Society (ACS)
Date: 19-09-2016
Abstract: Pyrethrum is a natural insecticide extracted from Tanacetum cinerariifolium. Six esters, the pyrethrins, are responsible for the extract's insecticidal activity. The oxidative degradation of pyrethrins through contact with aerial oxygen is a potential cause of pyrethrin losses during pyrethrum manufacture. Described here is the first investigation of the autoxidation chemistry of the six pyrethrin esters isolated from pyrethrum. It was found that pyrethrins I and II, the major pyrethrin esters present in pyrethrum, undergo autoxidation more readily than the minor pyrethrin esters, the jasmolins and cinerins. Chromatographic analysis of pyrethrin I and II autoxidation mixtures showed some correlation with a similar analysis performed on extracts from T. cinerariifolium crop, which had been stored for 12 weeks without added antioxidants. Two pyrethrin II autoxidation products were isolated, characterized, and shown to be present in extracts of stored T. cinerariifolium crop, confirming that autoxidation of pyrethrin esters does occur during crop storage.
Structure-based design of inhibitors of the rice blast fungal enzyme trihydroxynaphthalene reductase
Publisher: Elsevier BV
Date: 10-2001
DOI: 10.1016/S1093-3263(00)00103-0
Abstract: Rice Blast Disease, caused by the fungus Pyricularia oryzae, is one of the most important diseases of rice. Several enzymes in the melanin biosynthetic pathway have proven to be valuable targets for development of rice blast fungicides. In particular, inhibitors of trihydroxynaphthalene reductase (3HNR), which catalyzes the conversion of trihydroxynaphthalene to vermelone, have yielded commercially useful rice fungicides. The X-ray structure of 3HNR has been published recently, presenting an opportunity to use this information in the de novo design of novel 3HNR inhibitors that may exhibit useful rice blast activity. We used the LeapFrog program to develop a docking model for interaction of ligands with the active site of THNR. The final model gave a good correlation between calculated binding energy and log Ki and was used to design novel ligands and score compounds for synthesis. Using this as a tool, we synthesized inhibitors in the nanomolar range and also developed several inhibitors that did not conform to the properties of the THNR active site. Leapfrog was able to locate a previously unrecognized binding pocket that could accommodate these otherwise anomalous regions of structure.
Sparse QSAR modelling methods for therapeutic and regenerative medicine
Publisher: Springer Science and Business Media LLC
Date: 14-02-2018
DOI: 10.1007/S10822-018-0106-1
Abstract: The quantitative structure-activity relationships method was popularized by Hansch and Fujita over 50 years ago. The usefulness of the method for drug design and development has been shown in the intervening years. As it was developed initially to elucidate which molecular properties modulated the relative potency of putative agrochemicals, and at a time when computing resources were scarce, there is much scope for applying modern mathematical methods to improve the QSAR method and to extending the general concept to the discovery and optimization of bioactive molecules and materials more broadly. I describe research over the past two decades where we have rebuilt the unit operations of the QSAR method using improved mathematical techniques, and have applied this valuable platform technology to new important areas of research and industry such as nanoscience, omics technologies, advanced materials, and regenerative medicine. This paper was presented as the 2017 ACS Herman Skolnik lecture.
New insights on the reaction of alkyl 3-oxo-4-(triphenylphosphoranylidene)butanoate and aldehydes under concentrated condensation conditions. One-pot synthesis of highly functionalised 6-oxocyclohex-4-ene-1,3-dicarboxylates
Publisher: Royal Society of Chemistry (RSC)
Date: 1998
DOI: 10.1039/A806577B
Frontispiece: Validating Eaton's Hypothesis: Cubane as a Benzene Bioisostere
Publisher: Wiley
Date: 29-02-2016
Nitrile Oxide Cycloaddition Chemistry Using Benzotriazole as a Steric Auxiliary
Publisher: CSIRO Publishing
Date: 2005
DOI: 10.1071/CH05189
Abstract: The relatively hindered 2,2-bis(benzotriazol-1-yl)acetonitrile oxide was prepared in situ from the precursor hydroximinoyl chloride, and was allowed to react with dipolarophiles to give rise to several isoxazoles. The benzotriazole substituents acted as steric auxiliaries to prevent unwanted dimerization of the nitrile oxide to the furoxan by-product.
Evolving embodied intelligence from materials to machines
Publisher: Springer Science and Business Media LLC
Date: 07-01-2019
Holographic QSAR of benzodiazepines
Publisher: Wiley
Date: 06-1998
DOI: 10.1002/(SICI)1521-3838(199806)17:03<224::AID-QSAR224>3.3.CO;2-Y
Towards computational design of zeolite catalysts for CO2 reduction
Publisher: Royal Society of Chemistry (RSC)
Date: 2015
DOI: 10.1039/C5RA06214D
Abstract: Computational search of structure database for CO 2 reduction catalysts using molecular simulation and machine learning.
Single-Cell Tracking on Polymer Microarrays Reveals the Impact of Surface Chemistry on Pseudomonas aeruginosa Twitching Speed and Biofilm Development
Publisher: American Chemical Society (ACS)
Date: 06-11-2020
Using high throughput experimental data and in silico models to discover alternatives to toxic chromate corrosion inhibitors
Publisher: Elsevier BV
Date: 05-2016
Brooktrout Lake Case Study: Biotic Recovery from Acid Deposition 20 Years after the 1990 Clean Air Act Amendments
Publisher: American Chemical Society (ACS)
Date: 12-02-2015
DOI: 10.1021/ES5036865
Abstract: The Adirondack Mountain region is an extensive geographic area (26,305 km(2)) in upstate New York where acid deposition has negatively affected water resources for decades and caused the extirpation of local fish populations. The water quality decline and loss of an established brook trout (Salvelinus fontinalis [Mitchill]) population in Brooktrout Lake were reconstructed from historical information dating back to the late 1880s. Water quality and biotic recovery were documented in Brooktrout Lake in response to reductions of S deposition during the 1980s, 1990s, and 2000s and provided a unique scientific opportunity to re-introduce fish in 2005 and examine their critical role in the recovery of food webs affected by acid deposition. Using C and N isotope analysis of fish collagen and state hatchery feed as well as Bayesian assignment tests of microsatellite genotypes, we document in situ brook trout reproduction, which is the initial phase in the restoration of a preacidification food web structure in Brooktrout Lake. Combined with sulfur dioxide emissions reductions promulgated by the 1990 Clean Air Act Amendments, our results suggest that other acid-affected Adirondack waters could benefit from careful fish re-introduction protocols to initiate the ecosystem reconstruction of important components of food web dimensionality and functionality.
Multi-Dimensional Machine Learning Analysis of Polyaniline Films Using Stitched Hyperspectral ToF-SIMS Data
Publisher: American Chemical Society (ACS)
Date: 12-05-2023
TRANSITION-STATE ANALOGS AS INHIBITORS FOR GABA-AMINOTRANSFERASE
Publisher: Elsevier BV
Date: 03-1991
Decoding the Rich Biological Properties of Noble Gases: How Well Can We Predict Noble Gas Binding to Diverse Proteins?
Publisher: Wiley
Date: 13-08-2018
Abstract: The chemically inert noble gases display a surprisingly rich spectrum of useful biological properties. Relatively little is known about the molecular mechanisms behind these effects. It is clearly not feasible to conduct large numbers of pharmacological experiments on noble gases to identify activity. Computational studies of the binding of noble gases and proteins can address this paucity of information and provide insight into mechanisms of action. We used bespoke computational grid calculations to predict the positions of energy minima in the interactions of noble gases with erse proteins. The method was validated by quantifying how well simulations could predict binding positions in 131 erse protein X-ray structures containing 399 Xe and Kr atoms. We found excellent agreement between calculated and experimental binding positions of noble gases. 94 % of all crystallographic xenon atoms were within 1 Xe van der Waals (vdW) diameter of a predicted binding site, and 97 % lay within 2 vdW diameters. 100 % of crystallographic krypton atoms were within 1 Kr vdW diameter of a predicted binding site. We showed the feasibility of large-scale computational screening of all ≈60 000 unique structures in the Protein Data Bank. This will elucidate biochemical mechanisms by which these novel 'atomic drugs' elicit their valuable biochemical properties and identify new medical uses.
Contrasting effects of nanoparticle binding on protein denaturation
Publisher: American Chemical Society (ACS)
Date: 10-09-2014
DOI: 10.1021/JP506135M
Atomistic topological indices applied to benzodiazepines using various regression methods
Publisher: Wiley
Date: 1998
DOI: 10.1002/(SICI)1521-3838(199801)17:01<14::AID-QSAR14>3.0.CO;2-U
Stem cell decision making and critical-like exploratory networks
Publisher: Elsevier BV
Date: 05-2009
DOI: 10.1016/J.SCR.2009.03.001
Abstract: A sound theoretical or conceptual model of gene regulatory processes that control stem cell fate is still lacking, compromising our ability to manipulate stem cells for therapeutic benefit. The complexity of the regulatory and signaling pathways limits development of useful, predictive models that employ solely reductionist methods using molecular components. However, there is clear evidence from other complex systems that coarse-grained or mesoscale models can yield useful insights and provide workable models for the prediction of some emergent properties such as cell phenotype. We present such a coarse-grained model of stem cell decision making, utilizing the concept of self-organized criticality, which is an order that propagates in some nonequilibrium systems. The model proposes that stochastic gene expression within a stem cell gene regulatory network self-organizes to a critical-like state, characterized by cascades of gene expression that prime various transcriptional programs associated with different cell fates. This ersity of cell fate options is reduced during the decision-making process, which involves a supercritical connectivity in the gene regulatory network as a stem cell leaves its niche microenvironment and an overall increase in transcription occurs. As modules of genes that correspond to specific cell fates approach their critical points, competitive interactions occur between them that are influenced by prevailing microenvironmental conditions. The conceptual model incorporates both intrinsic and extrinsic factors governing stem cell fate and provides a logical pathway to the development of a computational model. We further suggest that rapid self-organized criticality, rather than self-organized criticality, best describes the mesoscale organization of gene regulatory networks.
Getting to the Source: Selective Drug Targeting of Cancer Stem Cells
Publisher: Wiley
Date: 23-04-2014
Abstract: Cancers are among the most important and most difficult to treat diseases of the 21st century. Conventional therapies include surgery, immunotherapy, and radiotherapy, as well many forms of drug treatments such as tamoxifen and Gleevec. However, these forms of treatment often do not eradicate the cancer stem cells, only managing to decrease the size of the tumor, allowing the cancer to return. The cancer stem cell hypothesis stipulates that malignancy is maintained through self-renewal of cancer stem cells (CSCs), which generate rapidly iding progeny that comprise the tumors, and that are largely untouched by conventional therapies. Evidence for the central role of CSCs in many tumors has provided a paradigm shift in the way cancer chemotherapy may be addressed. Recent discoveries regarding the nature of the stem cell niche, and the key signaling pathways involved in stem cell self-renewal and differentiation from regenerative medicine, have provided key information that facilitates selective targeting of CSCs by small-molecule drugs. The growing body of biochemical knowledge on the nature of CSCs, and differences between them and normal adult stem cells essential for maintaining organisms, has augmented the increasing number of small molecules shown to control normal and aberrant stem cells. Here, we review small-molecule approaches to the selective targeting of CSCs.
Quadrupole hyperfine structure of the microwave spectrum of 1,2,4-triazole and N-deuterotriazole
Publisher: Elsevier BV
Date: 08-1975
Optimal sparse descriptor selection for QSAR using Bayesian methods
Publisher: Wiley
Date: 07-2009
Abstract: Choosing a set of molecular descriptors (features) that is most relevant to a given biological response variable is a very important problem in QSAR that has not be solved in an optimal robust way. It is an interesting and important class of mathematical problems, where the number of variables greatly outweighs the number of observations (grossly underdetermined systems). We have used two Bayesian approaches to carry out this task using a suite of QSAR data sets. We employed a specialized sparse Bayesian feature reduction method based on an EM algorithm with a Laplacian prior to select a small set of the most relevant descriptors for modeling the response variables from a much larger pool of possibilities. Having chosen the optimum descriptors in a supervised manner, we used a Bayesian regularized neural network to carry out nonlinear regression and derive robust parsimonious QSAR models for five drug data sets. Models were validated using independent test sets, and results compared with other contemporary descriptor selection methods. Issues around validating small QSAR data sets were also discussed in detail. The sparse feature selection algorithm proved to be an excellent, robust method for selecting descriptors for QSAR models, as it is supervised (descriptors chosen in a context‐dependent manner), parsimonious (models not overly complex), and inherently interpretable. Coupled to a robust parsimonious nonlinear modeling method such as the Bayesian regularized neural net, the combination provides a means of optimally modeling the data, and allowing interpretation of the model in terms of the most relevant descriptors.
PALEOECOLOGY AND HYPSILOPHODONTID BEHAVIOR AT THE PROCTOR LAKE DINOSAUR LOCALITY (EARLY CRETACEOUS), TEXAS
Publisher: Geological Society of America
Date: 1989
DOI: 10.1130/SPE238-P55
A Carbon-13 NMR Study of Carbon Dioxide Absorption and Desorption with Aqueous Amine Solutions
Publisher: Elsevier BV
Date: 02-2009
Bayesian Regularization of Neural Networks
Publisher: Humana Press
Date: 2008
DOI: 10.1007/978-1-60327-101-1_3
Abstract: Bayesian regularized artificial neural networks (BRANNs) are more robust than standard back-propagation nets and can reduce or eliminate the need for lengthy cross-validation. Bayesian regularization is a mathematical process that converts a nonlinear regression into a "well-posed" statistical problem in the manner of a ridge regression. The advantage of BRANNs is that the models are robust and the validation process, which scales as O(N2) in normal regression methods, such as back propagation, is unnecessary. These networks provide solutions to a number of problems that arise in QSAR modeling, such as choice of model, robustness of model, choice of validation set, size of validation effort, and optimization of network architecture. They are difficult to overtrain, since evidence procedures provide an objective Bayesian criterion for stopping training. They are also difficult to overfit, because the BRANN calculates and trains on a number of effective network parameters or weights, effectively turning off those that are not relevant. This effective number is usually considerably smaller than the number of weights in a standard fully connected back-propagation neural net. Automatic relevance determination (ARD) of the input variables can be used with BRANNs, and this allows the network to "estimate" the importance of each input. The ARD method ensures that irrelevant or highly correlated indices used in the modeling are neglected as well as showing which are the most important variables for modeling the activity data. This chapter outlines the equations that define the BRANN method plus a flowchart for producing a BRANN-QSAR model. Some results of the use of BRANNs on a number of data sets are illustrated and compared with other linear and nonlinear models.
Modelling and Prediction of Bacterial Attachment to Polymers
Publisher: Wiley
Date: 04-12-2013
Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials.
Publisher: Elsevier BV
Date: 05-2016
DOI: 10.1016/J.TAAP.2015.12.016
Abstract: Nanomaterials research is one of the fastest growing contemporary research areas. The unprecedented properties of these materials have meant that they are being incorporated into products very quickly. Regulatory agencies are concerned they cannot assess the potential hazards of these materials adequately, as data on the biological properties of nanomaterials are still relatively limited and expensive to acquire. Computational modelling methods have much to offer in helping understand the mechanisms by which toxicity may occur, and in predicting the likelihood of adverse biological impacts of materials not yet tested experimentally. This paper reviews the progress these methods, particularly those QSAR-based, have made in understanding and predicting potentially adverse biological effects of nanomaterials, and also the limitations and pitfalls of these methods.
Immune-Instructive Polymers Control Macrophage Phenotype and Modulate the Foreign Body Response In Vivo
Publisher: Elsevier BV
Date: 06-2020
Comprehensive experimental and theoretical study into the complete valence electronic structure of norbornadiene
Publisher: American Chemical Society (ACS)
Date: 17-09-2002
DOI: 10.1021/JP021338D
Hydration and Dynamics of Ligands Determine the Antifouling Capacity of Functionalized Surfaces
Publisher: American Chemical Society (ACS)
Date: 21-11-2019
Scaled vehicle tire characteristics: dimensionless analysis
Publisher: Informa UK Limited
Date: 02-2006
A chemically intuitive molecular index based on the eigenvalues of a modified adjacency matrix
Publisher: Wiley
Date: 1997
Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?
Publisher: MDPI AG
Date: 11-12-2020
DOI: 10.3390/NANO10122493
Abstract: Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs (NInChI). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered) surface topography surface coatings or functionalization doping with other chemicals and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a “nano” extension to the InChI standard.
Exploring the electronic structure of 2,6-stelladione from momentum space I: the p-dominant molecular orbitals in the outer valence shell
Publisher: Elsevier BV
Date: 12-2003
A variational description of the rotational and vibrational states of triatomic molecules using numerical wavefunctions
Publisher: Informa UK Limited
Date: 09-1987
Evolutionary design of optimal surface topographies for biomaterials
Publisher: Springer Science and Business Media LLC
Date: 17-12-2020
DOI: 10.1038/S41598-020-78777-2
Abstract: Natural evolution tackles optimization by producing many genetic variants and exposing these variants to selective pressure, resulting in the survival of the fittest. We use high throughput screening of large libraries of materials with differing surface topographies to probe the interactions of implantable device coatings with cells and tissues. However, the vast size of possible parameter design space precludes a brute force approach to screening all topographical possibilities. Here, we took inspiration from Nature to optimize materials surface topographies using evolutionary algorithms. We show that successive cycles of material design, production, fitness assessment, selection, and mutation results in optimization of biomaterials designs. Starting from a small selection of topographically designed surfaces that upregulate expression of an osteogenic marker, we used genetic crossover and random mutagenesis to generate new generations of topographies.
Biomedical nanomaterials: applications, toxicological concerns, and regulatory needs
Publisher: Informa UK Limited
Date: 18-12-2020
CNDO/2 CALCULATIONS FOR ORGANOHALIDES
Publisher: CSIRO Publishing
Date: 1986
DOI: 10.1071/CH9860233
Abstract: A CNDO/2 parameterization for performing semiempirical molecular orbital calculations for organic molecules containing bromine and iodine is presented the results are superior to those from other parameterizations, and generally agree with ab initio calculations and experiment.
THE MICROWAVE-SPECTRUM OF HCN DIMER
Publisher: Elsevier BV
Date: 10-1981
Molecular orbital studies of reaction mechanism and transition state structures for GABA-transaminase
Publisher: Elsevier BV
Date: 03-1988
Understanding the Roles of the “Two QSARs”
Publisher: American Chemical Society (ACS)
Date: 20-01-2016
Abstract: Quantitative structure-activity relationship (QSAR) modeling has matured over the past 50 years and has been very useful in discovering and optimizing drug leads. Although its roots were in extra-thermodynamic relationships within small sets of chemically similar molecules focused on mechanistic interpretation, a second class of QSAR models has emerged that relies on machine learning methods to generate models from large, chemically erse data sets for predictive purposes. There has been a tension between the two groups of QSAR practitioners that is unnecessary and possibly counterproductive. This paper explains the difference in philosophy and application of these two distinct, but equally important, classes of QSAR models and how they can work together synergistically to accelerate the discovery of new drugs or materials.
A high-resolution electron momentum spectroscopy and density functional theory study into the complete valence electronic structure of allene
Publisher: Wiley
Date: 31-07-2001
DOI: 10.1002/JCC.1090
Corrosion Inhibition, Inhibitor Environments, and the Role of Machine Learning
Publisher: MDPI AG
Date: 29-11-2022
DOI: 10.3390/CMD3040037
Abstract: Machine learning (ML) is providing a new design paradigm for many areas of technology, including corrosion inhibition. However, ML models require relatively large and erse training sets to be most effective. This paper provides an overview of developments in corrosion inhibitor research, focussing on how corrosion performance data can be incorporated into machine learning and how large sets of inhibitor performance data that are suitable for training robust ML models can be developed through various corrosion inhibition testing approaches, especially high-throughput performance testing. It examines different types of environments where corrosion by-products and electrolytes operate, with a view to understanding how conventional inhibitor testing methods may be better designed, chosen, and applied to obtain the most useful performance data for inhibitors. The authors explore the role of modern characterisation techniques in defining corrosion chemistry in occluded structures (e.g., lap joints) and examine how corrosion inhibition databases generated by these techniques can be exemplified by recent developments. Finally, the authors briefly discuss how the effects of specific structures, alloy microstructures, leaching structures, and kinetics in paint films may be incorporated into machine learning strategies.
Signalling pathway crosstalk stimulated by L-proline drives mouse embryonic stem cells to primitive-ectoderm-like cells
Publisher: The Company of Biologists
Date: 15-10-2023
DOI: 10.1242/DEV.201704
Discovery and Design of Radiopharmaceuticals by In silico Methods
Publisher: Bentham Science Publishers Ltd.
Date: 12-2022
DOI: 10.2174/1874471015666220831091403
Abstract: There has been impressive growth in the use of radiopharmaceuticals for therapy, selective toxic payload delivery, and noninvasive diagnostic imaging of disease. The increasing timeframes and costs involved in the discovery and development of new radiopharmaceuticals have driven the development of more efficient strategies for this process. Computer-Aided Drug Design (CADD) methods and Machine Learning (ML) have become more effective over the last two decades for drug and materials discovery and optimization. They are now fast, flexible, and sufficiently accurate to accelerate the discovery of new molecules and materials. Radiopharmaceuticals have also started to benefit from rapid developments in computational methods. Here, we review the types of computational molecular design techniques that have been used for radiopharmaceuticals design. We also provide a thorough examination of success stories in the design of radiopharmaceuticals, and the strengths and weaknesses of the computational methods. We begin by providing a brief overview of therapeutic and diagnostic radiopharmaceuticals and the steps involved in radiopharmaceuticals design and development. We then review the computational design methods used in radiopharmaceutical studies, including molecular mechanics, quantum mechanics, molecular dynamics, molecular docking, pharmacophore modelling, and datadriven ML. Finally, the difficulties and opportunities presented by radiopharmaceutical modelling are highlighted. The review emphasizes the potential of computational design methods to accelerate the production of these very useful clinical radiopharmaceutical agents and aims to raise awareness among radiopharmaceutical researchers about computational modelling and simulation methods that can be of benefit to this field.
Correction: QSAR without borders
Publisher: Royal Society of Chemistry (RSC)
Date: 2020
DOI: 10.1039/D0CS90041A
Abstract: Correction for ‘QSAR without borders’ by Eugene N. Muratov et al. , Chem. Soc. Rev. , 2020, DOI: 10.1039/d0cs00098a.
Therapsids from the Permian Chiweta beds and the age of the Karoo Supergroup in Malawi
Publisher: Springer London
Date: 14-12-2005
Consistent Concepts of Self-organization and Self-assembly
Publisher: Wiley
Date: 11-2008
DOI: 10.1002/CPLX.20235
Classification of self-organization and emergence in chemical and biological systems
Publisher: CSIRO Publishing
Date: 2006
DOI: 10.1071/CH06191
Abstract: Most chemical and biological systems are complex, but the application of complex systems science to these fields is relatively new compared to the traditional reductionist approaches. Complexity can provide a new paradigm for understanding the behaviour of interesting chemical and biological systems, and new tools for studying, modelling, and simulating them. It is also likely that some very important, but very complicated systems may not be accessible by reductionist approaches. This paper provides a brief review of two important concepts in complexity, self-organization and emergence, and describes why they are relevant to chemical and biological systems
Exploiting Advances in Automation and Artificial Intelligence to Find Drugs for Neglected Tropical Diseases
Publisher: Maad Rayan Publishing Company
Date: 29-06-2023
DOI: 10.34172/PS.2023.15
The broader applications of neural and genetic modelling methods
Publisher: Elsevier BV
Date: 12-2001
Highly Flexible Ligand Binding Pocket of Ecdysone Receptor
Publisher: Elsevier BV
Date: 06-2004
A critical overview of computational approaches employed for COVID-19 drug discovery
Publisher: Royal Society of Chemistry (RSC)
Date: 2021
DOI: 10.1039/D0CS01065K
Abstract: We cover erse methodologies, computational approaches, and case studies illustrating the ongoing efforts to develop viable drug candidates for treatment of COVID-19.
Potential intermediates for incorporation of polyhydroxylated prolines into combinatorial libraries
Publisher: Elsevier BV
Date: 08-1998
MORPHEUS - A CONFORMATION-ACTIVITY RELATIONSHIPS AND RECEPTOR MODELING PACKAGE
Publisher: Elsevier BV
Date: 09-1989
DOI: 10.1016/0263-7855(89)80017-7
Abstract: Our molecular modeling software package, MORPHEUS, allows the study of the interactions between biologically active molecules and their receptors. The package is capable of exploring the multidimensional conformational space accessible to each molecule of the data set under study. By specifying distance constraints or hypothetical receptor binding points, the package is able to filter the biologically accessible conformations of each active compound and deduce a three-dimensional model of the binding sites consistent with the properties of the agonists (or antagonists) under scrutiny. The electrostatic potentials in the environment of a putative binding site can also be investigated using the MORPHEUS package. The molecular modeling module CRYS-X, which is written in FORTRAN 77 for IBM PC machines, is capable of building, displaying and manipulating molecules.
Sparse feature selection methods identify unexpected global cellular response to strontium-containing materials
Publisher: Proceedings of the National Academy of Sciences
Date: 23-03-2015
Abstract: Although new-generation biomaterials are increasingly complex and sophisticated, their development remains largely empirical, and functional outcomes are difficult to predict. Extending the biological evaluation of biomaterials beyond the assessment of preassumed effects would allow a better understanding of the material-driven cell responses. Here we illustrate how applying an objective, nondiscriminative approach to explore the global cell responses to a series of bone substitutes with various compositions can uncover unexpected, important changes at the gene and cellular levels and can provide in-depth knowledge of the effects of specific material properties on cell behavior.
Quantitative design rules for protein-resistant surface coatings using machine learning
Publisher: Springer Science and Business Media LLC
Date: 22-01-2019
DOI: 10.1038/S41598-018-36597-5
Abstract: Preventing biological contamination (biofouling) is key to successful development of novel surface and nanoparticle-based technologies in the manufacturing industry and biomedicine. Protein adsorption is a crucial mediator of the interactions at the bio – nano -materials interface but is not well understood. Although general, empirical rules have been developed to guide the design of protein-resistant surface coatings, they are still largely qualitative . Herein we demonstrate that this knowledge gap can be addressed by using machine learning approaches to extract quantitative relationships between the material surface chemistry and the protein adsorption characteristics. We illustrate how robust linear and non-linear models can be constructed to accurately predict the percentage of protein adsorbed onto these surfaces using lysozyme or fibrinogen as prototype common contaminants. Our computational models could recapitulate the adsorption of proteins on functionalised surfaces in a test set with an r 2 of 0.82 and standard error of prediction of 13%. Using the same data set that enabled the development of the Whitesides rules, we discovered an extension to the original rules. We describe a workflow that can be applied to large, consistently obtained data sets covering a broad range of surface functional groups and protein types.
Frontispiz: Validating Eaton's Hypothesis: Cubane as a Benzene Bioisostere
Publisher: Wiley
Date: 29-02-2016
High-resolution electron momentum spectroscopy of molecules
Publisher: Royal Society of Chemistry (RSC)
Date: 22-11-2002
DOI: 10.1039/B100700I
Holographic neural networks as nonlinear discriminants for chemical applications
Publisher: American Chemical Society (ACS)
Date: 19-01-1998
DOI: 10.1021/CI9702860
Abstract: A holographic neural network has been investigated for use as a discriminant. Six sets of artificial data and two data sets of infrared spectra, reduced using principal component analysis, of prepared cervical smears were analyzed by four regular discriminant methods as well as by the holographic neural network method. In all cases, it was found that the holographic neural network method gave comparable, and in some cases superior, results to the other discriminant methods. The holographic neural network method is simple to apply and has the advantage that it can be easily refined when new data become available without disturbing the original mapping. It is suggested that the holographic neural network method should be seriously considered when discrimination methods need to be applied.
A Thrombopoietin Antagonist Is Capable of Affecting Stem and Progenitor Cells of Patients with Myelofibrosis
Publisher: American Society of Hematology
Date: 06-12-2014
DOI: 10.1182/BLOOD.V124.21.819.819
Abstract: Aberrant thrombopoietin (TPO)/MPL signaling has been hypothesized to contribute to the pathogenesis of myelofibrosis (MF) (Kaushansky K. J Clin Invest. 2005 115: 3339 Moliterno AR, et al. N Engl J Med.1998 338:572). Agents that would be capable of inhibiting this signaling pathway are possible novel therapeutic agents that might be effective for MF treatment. A peptide antagonist of TPO, LCP4, has been created which is highly antagonistic to CB CD34+ cell proliferation and differentiation induced by TPO. In this report we examined the effect of LCP4 on the proliferation of MF CD34+ cells and their differentiation to megakaryocytes (Mk). Elevated levels of TPO (345±114ng/ml, n=13) were detected in MF plasmas as compared to that detected in normal plasma (10±4ng/ml, n=6, P=0.049), indicating the possibility that TPO affects MF hematopoietic stem cells (HSC) and progenitor cells (HPC). MF splenic CD34+ cells (2.5×104/mL) were incubated in serum free expansion media (SFEM) alone, with 50 ng/ml SCF+ varying doses of TPO (0, 10, 30,100 ng/mL) or 50 ng/ml SCF +varying doses of TPO+ varying concentrations of LCP4 (0, 10, 50, 100, 500, 1000nM) for 1 or 2 wks. Cells were then enumerated and stained with CD34, lineage cocktail, CD15 and CD41a mAbs. When splenic MF CD34+ cells were cultured in the presence of SCF and varying concentrations of TPO, 100ng/ml TPO resulted in the generation of the greatest numbers of CD34+Lin-, CD41a+CD34-CD15- and CD15+CD34-CD41a- cells, suggesting that TPO promotes the proliferation of MF HSCs/HPCs and the production of MF MKs and myeloid cells. By contrast, after 1 wk of treatment of MF CD34+ cells (n=8 5 MF spleens and 3 MF PB) with 100nM and 500nM LCP4, the numbers of MF total cells, CD34+Lin- (MF HSCs/HPCs), CD34+CD41a+ (immature Mks), CD41a+CD34-CD15- (mature Mks), CD15+CD34-CD41a- cells (myeloid cells) as well as hematopoietic colonies (HC), including CFU-Mk, CFU-GM, BFU-E/CFU-E, CFU-GEMM, were all significantly reduced (P all .05), as compared with cells cultured with SCF and TPO alone (Table 1). Two days of treatment of MF splenic CD34+ with 100nM LCP4 led to a greater degree of apoptosis (15.4% ± 3.5%) as compared with cells treated with cytokines alone (6.75% ± 1.7%, P =0.05 n=4). These findings suggest that LCP4 is able to inhibit the proliferation of MF HSCs/HPCs and the generation of immature and mature Mks as well as myeloid cells in a dose dependent fashion. Furthermore, the treatment of JAK2V617F-positive MF CD34+ cells from 4 JAK2V617F-positive MF patients (JAK2V617F allele burden: 78-90%) with 100nM LCP4 for 7 days reduced the percentage of JAK2V617F-positive HCs by 9-20% and JAK2V617F homozygous HCs by 1-20%. Since LCP4 treatment of MF CD34+ cells resulted in a reduction in the number of HPCs, LCP4 treatment reduced the absolute numbers of JAK2V617F-positive HCs generated by 53.0±4.0% (P .0002) and the absolute number of JAK2V617F homozygous HCs by 50.3±4.3% (P .0003). These data suggest that LCP4 treatment is able to impair the in vitro generation of MF HPCs and thereby leads to a depletion but not elimination of the number of malignant HPCs. We next examined the effect of LCP4 on MF HSCs by transplanting NSG mice with cells generated after splenic MF CD34+ cells were cultured in the presence of cytokines alone or cytokines plus LCP4 for 1 week. Two months after the transplantation, hCD45+ cells were detected in the BM of recipient mice receiving splenic MF CD34+ cells treated with cytokines alone and were reduced by 28% in mice receiving grafts treated with LCP4. MF CD34+ cells treated with or not treated with LCP4 had similar multilineage differentiation patterns (myeloid, lymphoid and erythroid). These data suggest that TPO antagonist therapy is capable of depleting MF HSCs and HPCs and that therapeutic strategy utilizing such strategies might serve as novel approaches to treating MF. Abstract 819. Table 1 Inhibitory effects of LCP4 on MF HSC/HPC proliferation and generation of Mks and myeloid cells Treatment Cells or HCs Generated (% of Cytokines Alone) Total Cells CD34+ Lin- Cells CD34+CD41a+ Cells CD41a+CD34-CD15- Cells CD15+CD34-CD41a- Cells CFU-Mk CFU-GM BFU-E /CFU-E CFU-GEMM SCF+TPO 100±0 100±0 100±0 100±0 100±0 100±0 100±0 100±0 100±0 SCF+TPO+LCP4 (100nM) 61.3±4.6 61.6±10.3 57.3±11.4 54.9±8.7 68.9±4.5 54.4±8.0 54.0±8.7 52.6±15.1 2.1±2.1 SCF+TPO+LCP4 (500nM) 53.3±8.1 46.8±7.0 59.1±17.1 42.8±8.0 62.5±7.3 43.6±10.0 44.6±5.7 50.5±12.9 0±0 Wang: The MPN Research Foundation (MPNRF) and the Leukemia & Lymphoma Society (LLS): Research Funding.
A Bright Future for Evolutionary Methods in Drug Design
Publisher: Wiley
Date: 09-06-2015
Abstract: Most medicinal chemists understand that chemical space is extremely large, essentially infinite. Although high-throughput experimental methods allow exploration of drug-like space more rapidly, they are still insufficient to fully exploit the opportunities that such large chemical space offers. Evolutionary methods can synergistically blend automated synthesis and characterization methods with computational design to identify promising regions of chemical space more efficiently. We describe how evolutionary methods are implemented, and provide ex les of published drug development research in which these methods have generated molecules with increased efficacy. We anticipate that evolutionary methods will play an important role in future drug discovery.
ORIGINS OF MOLECULAR-STRUCTURE .1. 3-BODY CALCULATIONS ON THE GROUND-STATES OF SYSTEMS FROM E-2+E- TO H-2+ USING A HYPERSPHERICAL BASIS
Publisher: IOP Publishing
Date: 14-07-1983
Potential COVID-19 therapies from computational repurposing of drugs and natural products against the SARS-CoV-2 helicase
Publisher: American Chemical Society (ACS)
Date: 04-03-2022
DOI: 10.26434/CHEMRXIV-2022-0ZLR3
Abstract: Repurposing of existing drugs is a rapid way to find potential new treatments for SARS-CoV-2. Here we applied a virtual screening approach using Autodock Vina and molecular dynamic simulation in tandem to screen and calculate binding energies of repurposed drugs against the SARS-CoV-2 helicase protein (non-structural protein nsp13). Amongst the top hits from our study were antivirals, antihistamines, and antipsychotics plus a range of other drugs. Approximately 30% of our top 87 hits had published evidence indicating in vivo or in vitro SARS-CoV-2 activity. Top hits not previously reported to have SARS-CoV-2 activity included the antiviral agents, cabotegravir and RSV-604, the NK1 antagonist, aprepitant, the trypanocidal drug, aminoquinuride, the analgesic antrafenine, the anticancer intercalator, epirubicin, the antihistamine, fexofenadine, and the anticoagulant, dicoumarol. These hits from our in silico SARS-CoV-2 helicase screen warrant further testing as potential COVID-19 treatments
Micro topographical instruction of bacterial attachment, biofilm formation andin vivohost response
Publisher: Cold Spring Harbor Laboratory
Date: 10-10-2020
DOI: 10.1101/2020.10.10.328146
Abstract: Bio-instructive materials that prevent bacterial biofilm formation and drive an appropriate host immune response have the potential to significantly reduce the burden of medical device-associated infections. Since bacterial surface attachment is known to be sensitive to surface topography, we experimentally survey 2,176 combinatorially generated shapes using an unbiased high-throughput micro topographical screen on polystyrene. This identifies topographies that reduce colonization in vitro by up to 15-fold compared with a flat surface for both motile and non-motile bacterial pathogens. Equivalent reductions are achieved on polyurethane, a polymer commonly used in medical devices. Using machine learning methods, a set of design rules based on generalisable descriptors is established for predicting bacteria-resistant micro topographies. In a murine foreign body infection model, anti-attachment topographies are shown to be refractory to P. aeruginosa and to recruit a productive host response, highlighting the potential of simple topographical patterning of non-eluting implants for preventing medical device associated infections.
Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation
Publisher: American Chemical Society (ACS)
Date: 06-2023
A systematic comparison of the structural and dynamic properties of commonly used water models for molecular dynamics simulations
Publisher: American Chemical Society (ACS)
Date: 06-07-2021
DOI: 10.26434/CHEMRXIV-2021-0DT3N
Abstract: Water is a unique solvent that is ubiquitous in biology and present in a variety of solutions, mixtures, and materials settings. It therefore forms the basis for all molecular dynamics simulations of biological phenomena, as well as for many chemical, industrial, and materials investigations. Over the years, many water models have been developed, and it remains a challenge to find a single water model that accurately reproduces all experimental properties of water simultaneously. Here, we report a comprehensive comparison of structural and dynamic properties of 30 commonly used 3-point, 4-point, 5-point, and polarizable water models simulated using consistent settings and analysis methods. For the properties of density, coordination number, surface tension, dielectric constant, self-diffusion coefficient, and solvation free energy of methane, models published within the past two decades consistently show better agreement with experimental values compared to models published earlier, albeit with some notable exceptions. However, no single model reproduced all experimental values exactly, highlighting the need to carefully choose a water model for a particular study, depending on the phenomena of interest. Finally, machine learning algorithms quantified the relationship between the water model force field parameters and the resulting bulk properties, providing insight into the parameter-property relationship and illustrating the challenges of developing a water model that can accurately reproduce all properties of water simultaneously.
Chapter 4 - Computational approaches
Publisher: Elsevier
Date: 2017
Introduction and foreword to the special issue on computational drug design and informatics
Publisher: Elsevier BV
Date: 10-2001
ChemInform Abstract: New Insights on the Reaction of Alkyl 3-Oxo-4-(triphenylphosphoranylidene)butanoate and Aldehydes under Concentrated Condensation Conditions. One-Pot Synthesis of Highly Functionalized 6-Oxocyclohex-4-ene-1,3-dicarboxylates.
Publisher: Wiley
Date: 11-05-1999
Effect of data preprocessing and machine learning hyperparameters on mass spectrometry imaging models
Publisher: American Vacuum Society
Date: 20-09-2023
DOI: 10.1116/6.0002788
HYPERFINE INTERACTIONS IN METHANIMINE
Publisher: CSIRO Publishing
Date: 1982
DOI: 10.1071/CH9820667
Abstract: A high-resolution study of hyperfine splitting in the microwave spectrum of methanimine has yielded the following values (in MHz) for 14N hyperfine constants: xaa- 0.898(8), xbb - 2.693(8), xcc 3.591(8), Ca -0.0495(47), Cb - 0*0097(27), Cc -0.0012(24). The localized theory for calculation of the magnetic hyperfine constants appears generally to overestimate Cc by an order of magnitude for molecules structurally similar to methanimine.
Massive in Silico Study of Noble Gas Binding to the Structural Proteome
Publisher: American Chemical Society (ACS)
Date: 15-10-2019
Abstract: Noble gases are chemically inert, and it was therefore thought they would have little effect on biology. Paradoxically, it was found that they do exhibit a wide range of biological effects, many of which are target-specific and potentially useful and some of which have been demonstrated in vivo. The underlying mechanisms by which useful pharmacology, such as tissue and neuroprotection, anti-addiction effects, and analgesia, is elicited are relatively unexplored. Experiments to probe the interactions of noble gases with specific proteins are more difficult with gases than those with other chemicals. It is clearly impractical to conduct the large number of gas-protein experiments required to gain a complete picture of noble gas biology. Given the simplicity of atoms as ligands, in silico methods provide an opportunity to gain insight into which noble gas-protein interactions are worthy of further experimental or advanced computational investigation. Our previous validation studies showed that in silico methods can accurately predict experimentally determined noble gas binding sites in X-ray structures of proteins. Here, we summarize the largest reported in silico reverse docking study involving 127 854 protein structures and the five nonradioactive noble gases. We describe how these computational screening methods are implemented, summarize the main types of interactions that occur between noble gases and target proteins, describe how the massive data set that this study generated can be analyzed (freely available at group18.csiro.au), and provide the NDMA receptor as an ex le of how these data can be used to understand the molecular pharmacology underlying the biology of the noble gases. We encourage chemical biologists to access the data and use them to expand the knowledge base of noble gas pharmacology, and to use this information, together with more efficient delivery systems, to develop "atomic drugs" that can fully exploit their considerable and relatively unexplored potential in medicine.
QSAR without borders
Publisher: Royal Society of Chemistry (RSC)
Date: 2020
DOI: 10.1039/D0CS00098A
Abstract: Word cloud summary of erse topics associated with QSAR modeling that are discussed in this review.
Materials genome in action
Publisher: American Chemical Society (ACS)
Date: 16-03-2017
An Optimal Self-Pruning Neural Network and Nonlinear Descriptor Selection in QSAR
Publisher: Wiley
Date: 22-06-2009
Materials for stem cell factories of the future
Publisher: Springer Science and Business Media LLC
Date: 21-05-2014
DOI: 10.1038/NMAT3972
Abstract: Polymeric substrates are being identified that could permit translation of human pluripotent stem cells from laboratory-based research to industrial-scale biomedicine. Well-defined materials are required to allow cell banking and to provide the raw material for reproducible differentiation into lineages for large-scale drug-screening programs and clinical use. Yet more than 1 billion cells for each patient are needed to replace losses during heart attack, multiple sclerosis and diabetes. Producing this number of cells is challenging, and a rethink of the current predominant cell-derived substrates is needed to provide technology that can be scaled to meet the needs of millions of patients a year. In this Review, we consider the role of materials discovery, an emerging area of materials chemistry that is in large part driven by the challenges posed by biologists to materials scientists.
Piperidine analogues of D-galactose as potent inhibitors of alpha-galactosidase: Synthesis by stannane-mediated hydroxymethylation of 5-azido-1,4-lactones. Structural relationships between D-galactosidase and L-rhamnosidase inhibitors
Publisher: Royal Society of Chemistry (RSC)
Date: 1999
DOI: 10.1039/A904145A
High-Throughput Assessment and Modeling of a Polymer Library Regulating Human Dental Pulp-Derived Stem Cell Behavior
Publisher: American Chemical Society (ACS)
Date: 23-10-2018
Abstract: The identification of biomaterials that modulate cell responses is a crucial task for tissue engineering and cell therapy. The identification of novel materials is complicated by the immense number of synthesizable polymers and the time required for testing each material experimentally. In the current study, polymeric biomaterial-cell interactions were assessed rapidly using a microarray format. The attachment, proliferation, and differentiation of human dental pulp stem cells (hDPSCs) were investigated on 141 homopolymers and 400 erse copolymers. The copolymer of isooctyl acrylate and 2-(methacryloyloxy)ethyl acetoacetate achieved the highest attachment and proliferation of hDPSC, whereas high cell attachment and differentiation of hDPSC were observed on the copolymer of isooctyl acrylate and trimethylolpropane ethoxylate triacrylate. Computational models were generated, relating polymer properties to cellular responses. These models could accurately predict cell behavior for up to 95% of materials within a test set. The models identified several functional groups as being important for supporting specific cell responses. In particular, oxygen-containing chemical moieties, including fragments from the acrylate/acrylamide backbone of the polymers, promoted cell attachment. Small hydrocarbon fragments originating from polymer pendant groups promoted cell proliferation and differentiation. These computational models constitute a key tool to direct the discovery of novel materials within the enormous chemical space available to researchers.
Design of potential anti-HIV agents. 1. Mannosidase inhibitors
Publisher: American Chemical Society (ACS)
Date: 09-1989
DOI: 10.1021/JM00129A011
Abstract: A molecular orbital and molecular graphics study of 12 substrates, inhibitors, reaction intermediates, and substrate analogues of alpha-mannosidase was undertaken. The results indicated that potent inhibitors must be good topographical analogues of the mannopyranosyl cation, an intermediate in the reaction catalyzed by the enzyme. Enzyme recognition and strong binding by the inhibitors requires that they contain, as part of their structure, electronegative atoms which are the topographical equivalent of the mannosyl cation C2 and C3 hydroxyl groups and ring heteroatom. The absence of a topographical analogue of the C4 hydroxyl group of the cation appeared to have little effect on the binding and activity of inhibitors. These results have been utilized in the design of potential anti-HIV drugs whose synthesis is now under consideration.
Author Correction: In silico comparison of SARS-CoV-2 spike protein-ACE2 binding affinities across species and implications for virus origin.
Publisher: Springer Science and Business Media LLC
Date: 14-09-2021
Beware of R-2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models
Publisher: American Chemical Society (ACS)
Date: 09-07-2015
TARGETED CYTO-TOXIC CELLS AS A NOVEL FORM OF CANCER-IMMUNOTHERAPY
Publisher: Elsevier BV
Date: 11-1988
DOI: 10.1016/0161-5890(88)90144-7
Abstract: Glucokinase (GK) acts as a glucose sensor in the pancreatic beta-cell and regulates insulin secretion. Heterozygous mutations in the human GK-encoding GCK gene that reduce the activity index increase the glucose-stimulated insulin secretion threshold and cause familial, mild fasting hyperglycaemia, also known as Maturity Onset Diabetes of the Young type 2 (MODY2). Here we describe the biochemical characterization of five missense GK mutations: p.Ile130Thr, p.Asp205His, p.Gly223Ser, p.His416Arg and p.Ala449Thr. The enzymatic analysis of the corresponding bacterially expressed GST-GK mutant proteins show that all of them impair the kinetic characteristics of the enzyme. In keeping with their position within the protein, mutations p.Ile130Thr, p.Asp205His, p.Gly223Ser, and p.His416Arg strongly decrease the activity index of GK, affecting to one or more kinetic parameters. In contrast, the p.Ala449Thr mutation, which is located in the allosteric activator site, does not affect significantly the activity index of GK, but dramatically modifies the main kinetic parameters responsible for the function of this enzyme as a glucose sensor. The reduced Kcat of the mutant (3.21±0.28 s(-1) vs 47.86±2.78 s(-1)) is balanced by an increased glucose affinity (S(0.5) = 1.33±0.08 mM vs 7.86±0.09 mM) and loss of cooperativity for this substrate. We further studied the mechanism by which this mutation impaired GK kinetics by measuring the differential effects of several competitive inhibitors and one allosteric activator on the mutant protein. Our results suggest that this mutation alters the equilibrium between the conformational states of glucokinase and highlights the importance of the fine-tuning of GK and its role in glucose sensing.
The X-ray Structure of a Hemipteran Ecdysone Receptor Ligand-binding Domain: COMPARISON WITH A LEPIDOPTERAN ECDYSONE RECEPTOR LIGAND-BINDING DOMAIN AND IMPLICATIONS FOR INSECTICIDE DESIGN
Publisher: Elsevier BV
Date: 06-2005
Quantitative structure - Activity relationship studies using gaussian processes
Publisher: American Chemical Society (ACS)
Date: 26-04-2001
DOI: 10.1021/CI000459C
A thrombopoietin receptor antagonist is capable of depleting myelofibrosis hematopoietic stem and progenitor cells
Publisher: American Society of Hematology
Date: 30-06-2016
DOI: 10.1182/BLOOD-2015-10-674465
Abstract: Treatment of MF CD34+ cells with a TPO receptor antagonist selectively depletes MF HSCs and HPCs. Agents that target the TPO receptor represent potentially new approaches for the treatment of MF patients.
Classification of emergence and its relation to self-organization
Publisher: Wiley
Date: 05-2008
DOI: 10.1002/CPLX.20216
Overcoming roadblocks in computational roadmaps to the future for safe nanotechnology
Publisher: IOP Publishing
Date: 06-2021
Chemistry Space-Time
Publisher: Elsevier BV
Date: 12-2015
Momentum distributions and molecular property information for trans 1,3 butadiene: An electron momentum spectroscopy and density functional theory investigation
Publisher: AIP Publishing
Date: 02-1998
DOI: 10.1063/1.475564
Abstract: The technique of electron momentum spectroscopy (EMS) has been used to measure orbital momentum distributions (MDs) for the complete valence electronic structure of trans 1,3 butadiene. The corresponding theoretical MDs were calculated using a plane wave impulse approximation (PWIA) model for the reaction mechanism and density functional theory (DFT) for the wave function. Seven basis sets, at the local density approximation (LDA) level and, additionally, incorporating nonlocal correlation functional corrections, were studied. The sensitivity of the level of agreement between the experimental and theoretical MDs to the nonlocal corrections is considered. A critical comparison between the experimental and theoretical MDs allows us to determine the “optimum” wave function from our basis sets. This wave function is then used to derive butadiene’s chemically interesting molecular properties, which are subsequently compared to the results of other workers. The sensitivity of the derived molecular property information to the nonlocal correlation functional corrections is also examined.
Modeling biological activities of nanoparticles
Publisher: American Chemical Society (ACS)
Date: 09-10-2012
DOI: 10.1021/NL303144K
Abstract: Products are increasingly incorporating nanomaterials, but we have a poor understanding of their adverse effects. To assess risk, regulatory authorities need more experimental testing of nanoparticles. Computational models play a complementary role in allowing rapid prediction of potential toxicities of new and modified nanomaterials. We generated quantitative, predictive models of cellular uptake and apoptosis induced by nanoparticles for several cell types. We illustrate the potential of computational methods to make a contribution to nanosafety.
A METHODOLOGY FOR APPLYING ENERGY HARVESTING TO EXTEND WILDLIFE TAG LIFETIME
Publisher: ASMEDC
Date: 2009
Abstract: Wildlife monitoring tags are a widely used technique for studying animals in their natural habitats. At present, these devices are energy limited, based on the mass of the electrochemical battery that can be carried by the animal. Flying animals are particularly restricted, based on a requirement for minimal excess loading. This requirement causes tag lifetimes to be far shorter than would be useful from an ecological perspective, particularly for smaller animals. Energy harvesting is being widely adopted in applications where access to permanent power is limited. If applied to wildlife tags, this approach offers the possibility of extending functional lifetimes indefinitely however, it presents unique challenges. Practical applications on flying animals are extremely mass limited, subject to environmental stress, and operate at very low frequencies. This paper is meant to address the critical issues in the design task, and makes attempts to place bounds on unknown design parameters, based on literature research where applicable, and on experiment when no data exists. We discuss candidate harvester materials, novel data acquisition tools, and a prototype harvester design.
Tripeptide motifs in biology: targets for peptidomimetic design.
Publisher: American Chemical Society (ACS)
Date: 28-01-2011
DOI: 10.1021/JM1012984
Zinc Is Not Required for Activity of TPO Agonists Acting at the c-Mpl Receptor Transmembrane Domain
Publisher: American Chemical Society (ACS)
Date: 16-06-2010
DOI: 10.1021/CB100100U
Abstract: Molecules that mimic the cytokine thrombopoietin that act by an atypical mechanism of binding to a receptor transmembrane (TM) domain are widely understood to require zinc for their biological activity. We investigated potent thrombopoietin mimetics from three chemical classes including the recently registered drug Eltrombopag, which operate via this novel mechanism, to determine whether zinc is essential for inducing cell proliferation. Using addition of zinc and a potent metal chelator, we show that the existing paradigm is incorrect and the compounds exhibit excellent thrombopoietin-mimetic activity even in the presence of high concentrations of EDTA. The implications of these findings for the mechanism of action are discussed.
Probing enzyme-nanoparticle interactions using combinatorial gold nanoparticle libraries
Publisher: Springer Science and Business Media LLC
Date: 28-11-2014
MAPPING ANALYTIC-FUNCTIONS USING NEURAL NETWORKS
Publisher: American Chemical Society (ACS)
Date: 11-1994
DOI: 10.1021/CI00022A001
Two-Dimensional and Three-Dimensional Time-of-Flight Secondary Ion Mass Spectrometry Image Feature Extraction Using a Spatially Aware Convolutional Autoencoder
Publisher: American Chemical Society (ACS)
Date: 26-05-2022
DOI: 10.1021/ACS.ANALCHEM.1C05453
Abstract: Feature extraction algorithms are an important class of unsupervised methods used to reduce data dimensionality. They have been applied extensively for time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging─commonly, matrix factorization (MF) techniques such as principal component analysis have been used. A limitation of MF is the assumption of linearity, which is generally not accurate for ToF-SIMS data. Recently, nonlinear autoencoders have been shown to outperform MF techniques for ToF-SIMS image feature extraction. However, another limitation of most feature extraction methods (including autoencoders) that is particularly important for hyperspectral data is that they do not consider spatial information. To address this limitation, we describe the application of the convolutional autoencoder (CNNAE) to hyperspectral ToF-SIMS imaging data. The CNNAE is an artificial neural network developed specifically for hyperspectral data that uses convolutional layers for image encoding, thereby explicitly incorporating pixel neighborhood information. We compared the performance of the CNNAE with other common feature extraction algorithms for two biological ToF-SIMS imaging data sets. We investigated the extracted features and used the dimensionality-reduced data to train additional ML algorithms. By converting two-dimensional convolutional layers to three-dimensional (3D), we also showed how the CNNAE can be extended to 3D ToF-SIMS images. In general, the CNNAE produced features with significantly higher contrast and autocorrelation than other techniques. Furthermore, histologically recognizable features in the data were more accurately represented. The extension of the CNNAE to 3D data also provided an important proof of principle for the analysis of more complex 3D data sets.
Surface-water interface induces conformational changes critical for protein adsorption: Implications for monolayer formation of EAS hydrophobin
Publisher: Frontiers Media SA
Date: 16-11-2015
Glossary of terms used in computational drug design, part II (IUPAC Recommendations 2015)
Publisher: Walter de Gruyter GmbH
Date: 03-2016
Abstract: Computational drug design is a rapidly changing field that plays an increasingly important role in medicinal chemistry. Since the publication of the first glossary in 1997, substantial changes have occurred in both medicinal chemistry and computational drug design. This has resulted in the use of many new terms and the consequent necessity to update the previous glossary. For this purpose a Working Party of eight experts was assembled. They produced explanatory definitions of more than 150 new and revised terms.
Use of metamodels for rapid discovery of narrow bandgap oxide photocatalysts
Publisher: Elsevier BV
Date: 09-2021
Aqueous solubility prediction: do crystal lattice interactions help?
Publisher: American Chemical Society (ACS)
Date: 13-06-2013
DOI: 10.1021/MP4001958
Abstract: Aqueous solubility is a very important physical property of small molecule drugs and drug candidates but also one of the most difficult to predict accurately. Aqueous solubility plays a major role in drug delivery and pharmacokinetics. It is believed that crystal lattice interactions are important in solubility and that including them in solubility models should improve the accuracy of the models. We used calculated values for lattice energy and sublimation enthalpy of organic molecules as descriptors to determine whether these would improve the accuracy of the aqueous solubility models. Multiple linear regression employing an expectation maximization algorithm and a sparse prior (MLREM) method and a nonlinear Bayesian regularized artificial neural network with a Laplacian prior (BRANNLP) were used to derive optimal predictive models of aqueous solubility of a large and highly erse data set of 4558 organic compounds over a normal ambient temperature range of 20-30 °C (293-303 K). A randomly selected test set and compounds from a solubility challenge were used to estimate the predictive ability of the models. The BRANNLP method showed the best statistical results with squared correlation coefficients of 0.90 and standard errors of 0.645-0.665 log(S) for training and test sets. Surprisingly, including descriptors that captured crystal lattice interactions did not significantly improve the quality of these aqueous solubility models.
Distinguishing Chemically Similar Polyamide Materials with ToF-SIMS Using Self-Organizing Maps and a Universal Data Matrix
Publisher: American Chemical Society (ACS)
Date: 27-09-2018
DOI: 10.1021/ACS.ANALCHEM.8B01951
Abstract: Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is advancing rapidly, providing instruments with growing capabilities and resolution. The data sets generated by these instruments are likewise increasing dramatically in size and complexity. Paradoxically, methods for efficient analysis of these large, rich data sets have not improved at the same rate. Clearly, more effective computational methods for analysis of ToF-SIMS data are becoming essential. Several research groups are customizing standard multivariate analytical tools to decrease computational demands, provide user-friendly interfaces, and simplify identification of trends and features in large ToF-SIMS data sets. We previously applied mass segmented peak lists to data from PMMA, PTFE, PET, and LDPE. Self-organizing maps (SOMs), a type of artificial neural network (ANN), classified the polymers based on their molecular composition and primary ion probe type more effectively than simple PCA. The effectiveness of this approach led us to question whether it would be useful in distinguishing polymers that were very similar. How sensitive is the technique to changes in polymer chemical structure and composition? To address this question, we generated ToF-SIMS ion peak signatures for seven nylon polymers with similar chemistries and used our up-binning and SOM approach to classify and cluster the polymers. The widely used linear PCA method failed to separate the s les. Supervised and unsupervised training of SOMs using positive or negative ion mass spectra resulted in effective classification and separation of the seven nylon polymers. Our SOM classification method has proven to be tolerant of minor s le irregularities, s le-to-s le variations, and inherent data limitations including spectral resolution and noise. We have demonstrated the potential of machine learning methods to analyze ToF-SIMS data more effectively than traditional methods. Such methods are critically important for future complex data analysis and provide a pipeline for rapid classification and identification of features and similarities in large data sets.
Potential COVID-19 Therapies from Computational Repurposing of Drugs and Natural Products against the SARS-CoV-2 Helicase
Publisher: MDPI AG
Date: 12-07-2022
DOI: 10.3390/IJMS23147704
Abstract: Repurposing of existing drugs is a rapid way to find potential new treatments for SARS-CoV-2. Here, we applied a virtual screening approach using Autodock Vina and molecular dynamic simulation in tandem to screen and calculate binding energies of repurposed drugs against the SARS-CoV-2 helicase protein (non-structural protein nsp13). Amongst the top hits from our study were antivirals, antihistamines, and antipsychotics, plus a range of other drugs. Approximately 30% of our top 87 hits had published evidence indicating in vivo or in vitro SARS-CoV-2 activity. Top hits not previously reported to have SARS-CoV-2 activity included the antiviral agents, cabotegravir and RSV-604 the NK1 antagonist, aprepitant the trypanocidal drug, aminoquinuride the analgesic, antrafenine the anticancer intercalator, epirubicin the antihistamine, fexofenadine and the anticoagulant, dicoumarol. These hits from our in silico SARS-CoV-2 helicase screen warrant further testing as potential COVID-19 treatments.
Effect of mass segment size on polymer ToF-SIMS multivariate analysis using a universal data matrix
Publisher: Elsevier BV
Date: 06-2019
Immune Modulation by Design: Using Topography to Control Human Monocyte Attachment and Macrophage Differentiation
Publisher: Wiley
Date: 28-04-2020
Information content of ToF-SIMS data: effect of spectral binning
Publisher: Elsevier BV
Date: 11-2019
Broad-Based Quantitative Structure−Activity Relationship Modeling of Potency and Selectivity of Farnesyltransferase Inhibitors Using a Bayesian Regularized Neural Network
Publisher: American Chemical Society (ACS)
Date: 30-10-2004
DOI: 10.1021/JM049621J
Abstract: Inhibitors of the enzyme farnesyltransferase show potential as novel anticancer agents. There are many known inhibitors, but efforts to build predictive SAR models have been h ered by the structural ersity and flexibility of inhibitors. We have undertaken for the first time a QSAR study of the potency and selectivity of a large, erse data set of farnesyltransferase inhibitors. We used novel molecular descriptors based on binned atomic properties and invariants of molecular matrices and a robust, nonlinear QSAR mapping paradigm, the Bayesian regularized neural network. We have built robust QSAR models of farnesyltransferase inhibition, geranylgeranyltransferase inhibition, and in vivo data. We have derived a novel selectivity index that allows us to model potency and selectivity simultaneously and have built robust QSAR models using this index that have the potential to discover new potent and selective inhibitors.
In silico screening of modulators of magnesium dissolution
Publisher: Elsevier BV
Date: 02-2020
High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications
Publisher: Wiley
Date: 09-09-2020
Feature importance in machine learning models: A fuzzy information fusion approach
Publisher: Elsevier BV
Date: 10-2022
In silico comparison of SARS-CoV-2 spike protein-ACE2 binding affinities across species and implications for virus origin
Publisher: Springer Science and Business Media LLC
Date: 24-06-2021
DOI: 10.1038/S41598-021-92388-5
Abstract: The devastating impact of the COVID-19 pandemic caused by SARS–coronavirus 2 (SARS-CoV-2) has raised important questions about its origins and the mechanism of its transfer to humans. A further question was whether companion or commercial animals could act as SARS-CoV-2 vectors, with early data suggesting susceptibility is species specific. To better understand SARS-CoV-2 species susceptibility, we undertook an in silico structural homology modelling, protein–protein docking, and molecular dynamics simulation study of SARS-CoV-2 spike protein’s ability to bind angiotensin converting enzyme 2 (ACE2) from relevant species. Spike protein exhibited the highest binding to human (h)ACE2 of all the species tested, forming the highest number of hydrogen bonds with hACE2. Interestingly, pangolin ACE2 showed the next highest binding affinity despite having a relatively low sequence homology, whereas the affinity of monkey ACE2 was much lower despite its high sequence similarity to hACE2. These differences highlight the power of a structural versus a sequence-based approach to cross-species analyses. ACE2 species in the upper half of the predicted affinity range (monkey, hamster, dog, ferret, cat) have been shown to be permissive to SARS-CoV-2 infection, supporting a correlation between binding affinity and infection susceptibility. These findings show that the earliest known SARS-CoV-2 isolates were surprisingly well adapted to bind strongly to human ACE2, helping explain its efficient human to human respiratory transmission. This study highlights how in silico structural modelling methods can be used to rapidly generate information on novel viruses to help predict their behaviour and aid in countermeasure development.
Computational Modelling of Magnetic Nanoparticle Properties and In Vivo Responses
Publisher: Bentham Science Publishers Ltd.
Date: 21-03-2017
A MOLECULAR GRAPHICS STUDY OF FACTORS INFLUENCING HERBICIDAL ACTIVITY OF OXIMES OF 3-ACYL-TETRAHYDRO-2H-PYRAN-2,4-DIONES
Publisher: Wiley
Date: 1989
Modeling the Influence of Fatty Acid Incorporation on Mesophase Formation in Amphiphilic Therapeutic Delivery Systems (vol 13, pg 996, 2016)
Publisher: American Chemical Society (ACS)
Date: 12-2018
Self-organizing circuitry and emergent computation in mouse embryonic stem cells
Publisher: Elsevier BV
Date: 03-2012
DOI: 10.1016/J.SCR.2011.11.001
Abstract: Pluripotency is a cellular state of multiple options. Here, we highlight the potential for self-organization to contribute to stem cell fate computation. A new way of considering regulatory circuitry is presented that describes the expression of each transcription factor (TF) as a branching process that propagates through time, interacting and competing with others. In a single cell, the interactions between multiple branching processes generate a collective process called 'critical-like self-organization'. We explain how this phenomenon provides a valid description of whole genome regulatory circuit dynamics. The hypothesis of exploratory stem cell decision-making proposes that critical-like self-organization (also called rapid self-organized criticality) provides the backbone for cell fate computation in regulative embryos and pluripotent stem cells. Unspecific lification of TF expression is predicted to initiate this self-organizing circuitry, where cascades of gene expression propagate and may interact either synergistically or antagonistically. The emergent and highly dynamic circuitry is affected by various sources of selection pressure, such as the expression of TFs with disproportionate influence over other genes, and extrinsic biological and physical stimuli that differentially modulate particular gene expression cascades. Extrinsic conditions continuously trigger waves of transcription that ripple throughout regulatory networks on multiple spatiotemporal scales, providing the context within which circuitry self-organizes. In this framework, a distinction between instructive and selective mechanisms of fate determination is misleading because it is the 'interference pattern', rather than any single instructing or selecting factor, that is ultimately responsible for computing and directing cell fate. Using this framework, we consider whether the idea of a naïve ground state of pluripotency and that of a fluctuating transcriptome are compatible, and whether a ground state like that captured in vitro could exist in vivo.
Synthesis, biological activity, and QSAR studies of antimicrobial agents containing biguanide isosteres
Publisher: CSIRO Publishing
Date: 2004
DOI: 10.1071/CH03146
Abstract: Analogues of chlorhexidine and chemically related antimicrobial compounds were synthesized, based on a model in which the bisbiguanide moieties were replaced by conformationally restricted cyclic isosteres. This model was tested by measuring the antimicrobial activities of the compounds. Quantitative structure–activity relationship (QSAR) studies showed a parabolic dependence of antimicrobial activity on the lipophilicity of the compounds. The basicity of the functional groups in the molecules was also very important, as uncharged molecules were not able to disrupt the microbial phospholipid bilayer and cause an antimicrobial effect. We compared our QSAR results to those reported in other studies of antimicrobials of erse structure. We found very similar QSAR models for all compounds studies with a log P (octanol/water partition constant) optimum at 5.5 (neutral log P value). The form of the QSAR equations were similar, suggesting a common mode of action for these agents.
Machine learning at the (nano)materials-biology interface
Publisher: The Royal Society of Chemistry
Date: 21-07-2020
DOI: 10.1039/9781839160233-00206
Abstract: Machine learning has a long history of success in the pharmaceutical sector, helping discover and optimize new drugs and predicting useful physicochemical properties like aqueous solubility. Materials science has embraced similar approaches and transferred useful technologies from the pharmaceutical sector. Although materials are more complex than small organic molecules, ML approaches have shown impressive results in predicting the properties of materials for application in erse fields like 2D photonics, porous materials for energy and environmental applications, and in the development of biomaterials and regenerative medicine therapies. Here, we summarize some of the challenges in ML modelling of materials and highlight some exciting recent applications.
Predictive human intestinal absorption QSAR models using Bayesian regularized neural networks
Publisher: CSIRO Publishing
Date: 2005
DOI: 10.1071/CH05202
Abstract: An oral dosage form is generally the most popular with patients. Many drug candidates fail in late development because of unfavourable absorption and pharmacokinetic profiles, or toxicity, among other factors (ADMET properties). This contributes to the fall in the efficiency of the pharmaceutical industry and to the rise in health costs. The ability to predict ADMET properties of drug leads can contribute to overcoming this problem. We have modelled intestinal absorption using several types of molecular descriptors and a non-linear Bayesian regularized neural network. Our models show very good predictive properties and are able to account for essentially all of the variance in the data that is not due to experimental error.
Computational Repurposing of Drugs and Natural Products Against SARS-CoV-2 Main Protease (M-pro) as Potential COVID-19 Therapies
Publisher: Frontiers Media SA
Date: 14-03-2022
DOI: 10.3389/FMOLB.2022.781039
Abstract: We urgently need to identify drugs to treat patients suffering from COVID-19 infection. Drugs rarely act at single molecular targets. Off-target effects are responsible for undesirable side effects and beneficial synergy between targets for specific illnesses. They have provided blockbuster drugs, e.g., Viagra for erectile dysfunction and Minoxidil for male pattern baldness. Existing drugs, those in clinical trials, and approved natural products constitute a rich resource of therapeutic agents that can be quickly repurposed, as they have already been assessed for safety in man. A key question is how to screen such compounds rapidly and efficiently for activity against new pandemic pathogens such as SARS-CoV-2. Here, we show how a fast and robust computational process can be used to screen large libraries of drugs and natural compounds to identify those that may inhibit the main protease of SARS-CoV-2. We show that the shortlist of 84 candidates with the strongest predicted binding affinities is highly enriched (≥25%) in compounds experimentally validated in vivo or in vitro to have activity in SARS-CoV-2. The top candidates also include drugs and natural products not previously identified as having COVID-19 activity, thereby providing leads for experimental validation. This predictive in silico screening pipeline will be valuable for repurposing existing drugs and discovering new drug candidates against other medically important pathogens relevant to future pandemics.
Predicting the effect of lipid structure on mesophase formation during in meso crystallization
Publisher: American Chemical Society (ACS)
Date: 05-06-2013
DOI: 10.1021/CG400513Y
Predicting Thermal Properties of Crystals Using Machine Learning
Publisher: Wiley
Date: 17-12-2019
Related Organisations
Commonwealth Scientific And Industrial Research Organisation
Location: Australia
University Of Nottingham
Location: United Kingdom of Great Britain and Northern Ireland
CSIRO Australian Manufacturing And Materials Precinct
Location: Australia
Related Funding Activities
Discovery Projects - Grant ID: DP200101217
Start Date: 03-2020
End Date: 04-2024
Amount: $660,000.00
Funder: Australian Research Council
View Funded ActivitySpecial Research Initiatives - Grant ID: SR0354636
Start Date: 2004
End Date: 12-2004
Amount: $30,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Infrastructure, Equipment And Facilities - Grant ID: LE230100153
Start Date: 2023
End Date: 12-2023
Amount: $984,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Infrastructure, Equipment And Facilities - Grant ID: LE210100100
Start Date: 04-2021
End Date: 12-2023
Amount: $975,934.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP160101301
Start Date: 2016
End Date: 12-2019
Amount: $479,300.00
Funder: Australian Research Council
View Funded ActivityResearch Networks - Grant ID: RN0460256
Start Date: 02-2005
End Date: 02-2010
Amount: $1,500,000.00
Funder: Australian Research Council
View Funded Activity