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Found 10 papers, 6 papers with code
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Machine learning plays an important and growing role in molecular simulation.
Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond
The development of reliable and extensible molecular mechanics (MM) force fields -- fast, empirical models characterizing the potential energy surface of molecular systems -- is indispensable for biomolecular simulation and computer-aided drug design.
EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment
Our hybrid approach couples a graph neural network to a streamlined charge equilibration approach in order to predict molecule-specific atomic electronegativity and hardness parameters, followed by analytical determination of optimal charge-equilibrated parameters that preserves total molecular charge.
Spatial Attention Kinetic Networks with E(n)-Equivariance
Neural networks that are equivariant to rotations, translations, reflections, and permutations on n-dimensional geometric space have shown promise in physical modeling for tasks such as accurately but inexpensively modeling complex potential energy surfaces to guiding the sampling of complex dynamical systems or forecasting their time evolution.
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials
Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on.
NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic
Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations.
Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks
Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs.
End-to-End Differentiable Molecular Mechanics Force Field Construction
Trained with arbitrary loss functions, it can construct entirely new force fields self-consistently applicable to both biopolymers and small molecules directly from quantum chemical calculations, with superior fidelity than traditional atom or parameter typing schemes.
Best Practices for Alchemical Free Energy Calculations
Alchemical free energy calculations are a useful tool for predicting free energy differences associated with the transfer of molecules from one environment to another.
Bayesian hidden Markov model analysis of single-molecule force spectroscopy: Characterizing kinetics under measurement uncertainty
Single-molecule force spectroscopy has proven to be a powerful tool for studying the kinetic behavior of biomolecules.
Statistical Mechanics Biological Physics Biomolecules
