AIMC Topic: Molecular Dynamics Simulation

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Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation.

Journal of chemical information and modeling Mar 30, 2022
Conformational sampling of protein structures is essential for understanding biochemical functions and for predicting thermodynamic properties such as free energies. Where previous approaches rely on sequential sampling procedures, recent development...
Deep Learning Proteins Thermodynamics Macromolecular Substances Molecular Conformation Molecular Dynamics Simulation
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Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events.

Journal of chemical theory and computation Mar 11, 2022
Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to ...
Machine Learning Molecular Dynamics Simulation Neural Networks, Computer Entropy Algorithms
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An Interpretable Convolutional Neural Network Framework for Analyzing Molecular Dynamics Trajectories: a Case Study on Functional States for G-Protein-Coupled Receptors.

Journal of chemical information and modeling Mar 8, 2022
Molecular dynamics (MD) simulations have made great contribution to revealing structural and functional mechanisms for many biomolecular systems. However, how to identify functional states and important residues from vast conformation space generated...
Receptors, G-Protein-Coupled Software Molecular Dynamics Simulation Neural Networks, Computer
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Design of SARS-CoV-2 Mpro, PLpro dual-target inhibitors based on deep reinforcement learning and virtual screening.

Future medicinal chemistry Feb 27, 2022
Since December 2019, SARS-CoV-2 has continued to spread rapidly around the world. The effective drugs may provide a long-term strategy to combat this virus. The main protease (Mpro) and papain-like protease (PLpro) are two important targets for the ...
Protease Inhibitors Humans SARS-CoV-2 Deep Learning Drug Evaluation, Preclinical Antiviral Agents Molecular Dynamics Simulation Molecular Docking Simulation
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PathDetect-SOM: A Neural Network Approach for the Identification of Pathways in Ligand Binding Simulations.

Journal of chemical theory and computation Feb 25, 2022
Understanding the process of ligand-protein recognition is important to unveil biological mechanisms and to guide drug discovery and design. Enhanced-sampling molecular dynamics is now routinely used to simulate the ligand binding process, resulting ...
Neural Networks, Computer Molecular Dynamics Simulation Cluster Analysis Algorithms Ligands
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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...
Entropy Molecular Dynamics Simulation Thermodynamics Deep Learning
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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...
Workflow Deep Learning Thermodynamics Entropy Molecular Dynamics Simulation
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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...
Protein Binding Ligands Machine Learning Molecular Dynamics Simulation 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 Dynamics Simulation Molecular Conformation Oligopeptides Neural Networks, Computer 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...
Molecular Dynamics Simulation Quantum Theory Neural Networks, Computer Thermodynamics Ligands
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