AIMC Topic: Molecular Dynamics Simulation

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Extended DeepILST for Various Thermodynamic States and Applications in Coarse-Graining.

The journal of physical chemistry. A Feb 24, 2022
Molecular dynamics (MD) simulations are widely used to obtain the microscopic properties of atomistic systems when the interatomic potential or the coarse-grained potential is known. In many practical situations, however, it is necessary to predict t...
Thermodynamics Entropy Deep Learning Molecular Dynamics Simulation
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GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling.

Journal of chemical theory and computation Feb 24, 2022
We introduce a Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and free energy profiling workflow (GLOW) to predict molecular determinants and map free energy landscapes of biomolecules. All-atom GaMD-enhanced sampling simulations...
Molecular Dynamics Simulation Thermodynamics Workflow Entropy Deep Learning
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Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics.

Journal of chemical theory and computation Feb 23, 2022
The determination of drug residence times, which define the time an inhibitor is in complex with its target, is a fundamental part of the drug discovery process. Synthesis and experimental measurements of kinetic rate constants are, however, expensiv...
Machine Learning Protein Binding Molecular Dynamics Simulation Ligands Kinetics
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Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides.

Journal of chemical theory and computation Feb 18, 2022
The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has b...
Molecular Conformation Oligopeptides Neural Networks, Computer Molecular Dynamics Simulation Machine Learning
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TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics.

Journal of chemical information and modeling Feb 4, 2022
Fast and accurate assessment of small-molecule dihedral energetics is crucial for molecular design and optimization in medicinal chemistry. Yet, accurate prediction of torsion energy profiles remains challenging as the current molecular mechanics (MM...
Neural Networks, Computer Ligands Thermodynamics Quantum Theory Molecular Dynamics Simulation
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Machine learning classification of trajectories from molecular dynamics simulations of chromosome segregation.

PloS one Jan 21, 2022
In contrast to the well characterized mitotic machinery in eukaryotes it seems as if there is no universal mechanism organizing chromosome segregation in all bacteria. Apparently, some bacteria even use combinations of different segregation mechanism...
Chromosome Segregation Microscopy, Fluorescence Bacteria DNA Replication Molecular Dynamics Simulation Machine Learning
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MLCV: Bridging Machine-Learning-Based Dimensionality Reduction and Free-Energy Calculation.

Journal of chemical information and modeling Dec 23, 2021
Importance-sampling algorithms leaning on the definition of a model reaction coordinate (RC) are widely employed to probe processes relevant to chemistry and biology alike, spanning time scales not amenable to common, brute-force molecular dynamics (...
Molecular Dynamics Simulation Entropy Algorithms Neural Networks, Computer Machine Learning
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Significance of the Chemical Environment of an Element in Nonadiabatic Molecular Dynamics: Feature Selection and Dimensionality Reduction with Machine Learning.

The journal of physical chemistry letters Dec 13, 2021
Using supervised and unsupervised machine learning (ML) on features generated from nonadiabatic (NA) molecular dynamics (MD) trajectories under the classical path approximation, we demonstrate that mutual information with the NA Hamiltonian can be us...
Machine Learning Titanium Oxides Calcium Compounds Molecular Dynamics Simulation
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Charge-Transfer Landscape Manifesting the Structure-Rate Relationship in the Condensed Phase Machine Learning.

The journal of physical chemistry. B Nov 26, 2021
In this work, we develop a machine learning (ML) strategy to map the molecular structure to condensed phase charge-transfer (CT) properties including CT rate constants, energy levels, electronic couplings, energy gaps, reorganization energies, and re...
Solvents Molecular Structure Machine Learning Molecular Dynamics Simulation Entropy
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Dynamics-Based Peptide-MHC Binding Optimization by a Convolutional Variational Autoencoder: A Use-Case Model for CASTELO.

Journal of chemical theory and computation Nov 18, 2021
An unsolved challenge in the development of antigen-specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen-major histocompatibility complex (MHC) binding is paramount toward achieving this goal. Here, we appl...
Peptides Machine Learning Algorithms Molecular Dynamics Simulation Protein Binding
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