AIMC Topic: Molecular Dynamics Simulation

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Machine Learning of Allosteric Effects: The Analysis of Ligand-Induced Dynamics to Predict Functional Effects in TRAP1.

The journal of physical chemistry. B Dec 28, 2020
Allosteric molecules provide a powerful means to modulate protein function. However, the effect of such ligands on distal orthosteric sites cannot be easily described by classical docking methods. Here, we applied machine learning (ML) approaches to ...
Allosteric Site Allosteric Regulation Machine Learning Molecular Dynamics Simulation Ligands Molecular Chaperones
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Electron-Passing Neural Networks for Atomic Charge Prediction in Systems with Arbitrary Molecular Charge.

Journal of chemical information and modeling Dec 16, 2020
Atomic charges are critical quantities in molecular mechanics and molecular dynamics, but obtaining these quantities requires heuristic choices based on atom typing or relatively expensive quantum mechanical computations to generate a density to be p...
Machine Learning Molecular Dynamics Simulation Electrons Cheminformatics Neural Networks, Computer
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An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V Dec 7, 2020
Drugs in solid dispersion (SD) take advantage of fast and extended dissolution, thus attains a higher bioavailability than the crystal form. However, current development of SD relies on a random large-scale formulation screening method with low effic...
Models, Biological Molecular Dynamics Simulation Administration, Oral Solubility Machine Learning Drug Design Data Science Intestinal Absorption Drug Liberation Biological Availability Datasets as Topic
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Predicting cryptic ligand binding sites based on normal modes guided conformational sampling.

Proteins Dec 5, 2020
To greatly expand the druggable genome, fast and accurate predictions of cryptic sites for small molecules binding in target proteins are in high demand. In this study, we have developed a fast and simple conformational sampling scheme guided by norm...
Molecular Dynamics Simulation Hepacivirus Ligands Elasticity Regression Analysis Area Under Curve Antigens, CD Reproducibility of Results Humans Protein Conformation Computational Biology Coronavirus Papain-Like Proteases SARS-CoV-2 Machine Learning Receptors, Cytoplasmic and Nuclear Antigens, Neoplasm Karyopherins ROC Curve Algorithms Binding Sites Interleukin-2 Protein Tyrosine Phosphatase, Non-Receptor Type 1 Coronavirus 3C Proteases Exportin 1 Protein Models, Statistical
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Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis.

Scientific reports Nov 23, 2020
COVID-19 caused by the SARS-CoV-2 is a current global challenge and urgent discovery of potential drugs to combat this pandemic is a need of the hour. 3-chymotrypsin-like cysteine protease (3CLpro) enzyme is the vital molecular target against the SAR...
Coronavirus 3C Proteases Molecular Docking Simulation Infectious bronchitis virus Deep Learning Anti-HIV Agents HIV-1 Antiviral Agents Drug Repositioning COVID-19 Drug Treatment Humans COVID-19 Protease Inhibitors Molecular Dynamics Simulation SARS-CoV-2 Cheminformatics Drug Evaluation, Preclinical
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Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation.

Chembiochem : a European journal of chemical biology Nov 17, 2020
Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved h...
Protein Folding Protein Multimerization Epoxide Hydrolases Molecular Dynamics Simulation Rhodococcus Machine Learning Limonene Mutation Protein Engineering
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AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks.

International journal of molecular sciences Nov 10, 2020
Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning tech...
Drug Discovery Deep Learning User-Computer Interface Protein Binding Proteins Databases, Protein Molecular Dynamics Simulation Neural Networks, Computer Molecular Docking Simulation Computer-Aided Design Drug Design Humans Ligands
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DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning.

eLife Nov 3, 2020
Single-molecule Förster Resonance energy transfer (smFRET) is an adaptable method for studying the structure and dynamics of biomolecules. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the...
Normal Distribution Signal Processing, Computer-Assisted Kinetics Reproducibility of Results Molecular Dynamics Simulation Markov Chains Software Deep Learning Fluorescence Resonance Energy Transfer Nanotechnology Algorithms User-Computer Interface False Positive Reactions Fluorescent Dyes Single Molecule Imaging
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Profiling SARS-CoV-2 Main Protease (M) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields.

ACS combinatorial science Oct 29, 2020
The current COVID-19 pandemic caused by a novel coronavirus SARS-CoV-2 urgently calls for a working therapeutic. Here, we report a computation-based workflow for efficiently selecting a subset of FDA-approved drugs that can potentially bind to the SA...
Catalytic Domain Protein Binding Neural Networks, Computer Coronavirus 3C Proteases Drug Repositioning Drug Discovery Coronavirus Protease Inhibitors Workflow Antiviral Agents Molecular Dynamics Simulation
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Integrated Variational Approach to Conformational Dynamics: A Robust Strategy for Identifying Eigenfunctions of Dynamical Operators.

The journal of physical chemistry. B Oct 9, 2020
One approach to analyzing the dynamics of a physical system is to search for long-lived patterns in its motions. This approach has been particularly successful for molecular dynamics data, where slowly decorrelating patterns can indicate large-scale ...
Neural Networks, Computer Molecular Dynamics Simulation Nonlinear Dynamics Molecular Conformation
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