AIMC Topic: Molecular Dynamics Simulation

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Finding Reactive Configurations: A Machine Learning Approach for Estimating Energy Barriers Applied to Sirtuin 5.

Journal of chemical theory and computation Nov 25, 2019
Sirtuin 5 is a class III histone deacetylase that, unlike its classification, mainly catalyzes desuccinylation and demanoylation reactions. It is an interesting drug target that we use here to test new ideas for calculating reaction pathways of large...
Humans Machine Learning Protein Conformation Thermodynamics Sirtuins Molecular Dynamics Simulation Quantum Theory
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Graph Classification of Molecules Using Force Field Atom and Bond Types.

Molecular informatics Oct 7, 2019
Classification of the biological activities of chemical substances is important for developing new medicines efficiently. Various machine learning methods are often employed to screen large libraries of compounds and predict the activities of new sub...
Machine Learning Molecular Structure Benzenesulfonates Molecular Dynamics Simulation Azepines Quantitative Structure-Activity Relationship
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Unsupervised Machine Learning for Analysis of Phase Separation in Ternary Lipid Mixture.

Journal of chemical theory and computation Oct 7, 2019
Phase separation in mixed lipid systems has been extensively studied both experimentally and theoretically because of its biological importance. A detailed description of such complex systems undoubtedly requires novel mathematical frameworks that ar...
1,2-Dipalmitoylphosphatidylcholine Lipids Lipid Bilayers Unsupervised Machine Learning Molecular Dynamics Simulation Phosphatidylcholines Cholesterol
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Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields.

Journal of chemical information and modeling Sep 27, 2019
We present a machine learning approach to automated force field development in dissipative particle dynamics (DPD). The approach employs Bayesian optimization to parametrize a DPD force field against experimentally determined partition coefficients. ...
Bayes Theorem Molecular Dynamics Simulation Chemical Engineering Thermodynamics Machine Learning Algorithms
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Identification of potential histone deacetylase1 (HDAC1) inhibitors using multistep virtual screening approach including SVM model, pharmacophore modeling, molecular docking and biological evaluation.

Journal of biomolecular structure & dynamics Sep 9, 2019
Histone Deacetylases (HDACs) play a significant role in the regulation of gene expression by modifying histones and non-histone substrates. Since they are key regulators in the reversible epigenetic mechanism, they are considered as promising drug ta...
Quantitative Structure-Activity Relationship Histones Support Vector Machine Molecular Dynamics Simulation Histone Deacetylases Histone Deacetylase Inhibitors Small Molecule Libraries Molecular Docking Simulation
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Past-future information bottleneck for sampling molecular reaction coordinate simultaneously with thermodynamics and kinetics.

Nature communications Aug 8, 2019
The ability to rapidly learn from high-dimensional data to make reliable bets about the future is crucial in many contexts. This could be a fly avoiding predators, or the retina processing gigabytes of data to guide human actions. In this work we dra...
Molecular Dynamics Simulation Thermodynamics Deep Learning Kinetics Forecasting
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Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1.

Molecules (Basel, Switzerland) Jul 29, 2019
The human carboxylesterase 1 (CES1), responsible for the biotransformation of many diverse therapeutic agents, may contribute to the occurrence of adverse drug reactions and therapeutic failure through drug interactions. The present study is designed...
Small Molecule Libraries Enzyme Activation Protease Inhibitors Drug Discovery Reproducibility of Results Carboxylic Ester Hydrolases Humans Molecular Docking Simulation ROC Curve Quantitative Structure-Activity Relationship Machine Learning Molecular Dynamics Simulation
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Time-Domain Analysis of Molecular Dynamics Trajectories Using Deep Neural Networks: Application to Activity Ranking of Tankyrase Inhibitors.

Journal of chemical information and modeling Jul 19, 2019
Molecular dynamics simulations provide valuable insights into the behavior of molecular systems. Extending the recent trend of using machine learning techniques to predict physicochemical properties from molecular dynamics data, we propose to conside...
Enzyme Inhibitors Tankyrases Computational Biology Molecular Dynamics Simulation Deep Learning Time Factors
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Analysis of crystallization phenomenon in Indian honey using molecular dynamics simulations and artificial neural network.

Food chemistry Jul 15, 2019
Molecular dynamics simulation was performed on sugar profile and moisture content-based mixture systems of six Indian honey samples. Comparative studies were performed to understand the interactive effects of fructose, glucose, sucrose, maltose and w...
Neural Networks, Computer Fructose Maltose Crystallization Glucose Honey Molecular Dynamics Simulation Water Sucrose India
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Artificial Intelligence: A Novel Approach for Drug Discovery.

Trends in pharmacological sciences Jul 3, 2019
Molecular dynamics (MD) simulations can mechanistically explain receptor function. However, the enormous data sets that they may imply can be a hurdle. Plante and colleagues (Molecules, 2019) recently described a machine learning approach to the anal...
Artificial Intelligence Machine Learning Molecular Dynamics Simulation Ligands Drug Discovery
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