AIMC Topic: Molecular Dynamics Simulation

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Automated workflow for computation of redox potentials, acidity constants, and solvation free energies accelerated by machine learning.

The Journal of chemical physics Jul 14, 2022
Fast evolution of modern society stimulates intense development of new materials with novel functionalities in energy and environmental applications. Due to rapid progress of computer science, computational design of materials with target properties ...
Machine Learning Molecular Dynamics Simulation Thermodynamics Oxidation-Reduction Workflow
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A neural network-assisted open boundary molecular dynamics simulation method.

The Journal of chemical physics May 14, 2022
A neural network-assisted molecular dynamics method is developed to reduce the computational cost of open boundary simulations. Particle influxes and neural network-derived forces are applied at the boundaries of an open domain consisting of explicit...
Molecular Dynamics Simulation Neural Networks, Computer Temperature
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GraphVAMPNet, using graph neural networks and variational approach to Markov processes for dynamical modeling of biomolecules.

The Journal of chemical physics May 14, 2022
Finding a low dimensional representation of data from long-timescale trajectories of biomolecular processes, such as protein folding or ligand-receptor binding, is of fundamental importance, and kinetic models, such as Markov modeling, have proven us...
Neural Networks, Computer Protein Folding Molecular Dynamics Simulation Markov Chains Kinetics
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SPLDExtraTrees: robust machine learning approach for predicting kinase inhibitor resistance.

Briefings in bioinformatics May 13, 2022
Drug resistance is a major threat to the global health and a significant concern throughout the clinical treatment of diseases and drug development. The mutation in proteins that is related to drug binding is a common cause for adaptive drug resistan...
Ligands Molecular Dynamics Simulation Machine Learning Proteins Mutation
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An inductive transfer learning force field (ITLFF) protocol builds protein force fields in seconds.

Briefings in bioinformatics Mar 10, 2022
Accurate simulation of protein folding is a unique challenge in understanding the physical process of protein folding, with important implications for protein design and drug discovery. Molecular dynamics simulation strongly requires advanced force f...
Proteins Molecular Dynamics Simulation Algorithms Neural Networks, Computer Machine Learning
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Identifying nonadditive contributions to the hydrophobicity of chemically heterogeneous surfaces via dual-loop active learning.

The Journal of chemical physics Jan 14, 2022
Hydrophobic interactions drive numerous biological and synthetic processes. The materials used in these processes often possess chemically heterogeneous surfaces that are characterized by diverse chemical groups positioned in close proximity at the n...
Neural Networks, Computer Machine Learning Hydrophobic and Hydrophilic Interactions Proteins Water Molecular Dynamics Simulation
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Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins.

Proceedings of the National Academy of Sciences of the United States of America Jan 4, 2022
RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-at...
Protein Multimerization Proto-Oncogene Proteins p21(ras) Molecular Dynamics Simulation Cell Membrane Lipids Humans Machine Learning Signal Transduction
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Classical and Machine Learning Methods for Protein - Ligand Binding Free Energy Estimation.

Current drug metabolism Jan 1, 2022
Binding free energy estimation of drug candidates to their biomolecular target is one of the best quantitative estimators in computer-aided drug discovery. Accurate binding free energy estimation is still a challengeable task even after decades of re...
Protein Binding Proteins Reproducibility of Results Humans Thermodynamics Molecular Dynamics Simulation Machine Learning Ligands
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Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics.

mAbs Jan 1, 2022
Machine learning has been recently used to predict therapeutic antibody aggregation rates and viscosity at high concentrations (150 mg/ml). These works focused on commercially available antibodies, which may have been optimized for stability. In this...
Viscosity Humans Protein Aggregates Antibodies, Monoclonal Molecular Dynamics Simulation Machine Learning
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Progress in deep Markov state modeling: Coarse graining and experimental data restraints.

The Journal of chemical physics Dec 7, 2021
Recent advances in deep learning frameworks have established valuable tools for analyzing the long-timescale behavior of complex systems, such as proteins. In particular, the inclusion of physical constraints, e.g., time-reversibility, was a crucial ...
Molecular Dynamics Simulation Biophysics Neural Networks, Computer Microfilament Proteins Markov Chains
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