1.9k citations · 3.3k across the 25 of their papers we have counts for
25 papers
Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning
Peter Eastman, Benjamin P. Pritchard, John D. Chodera +1
We describe version 2 of the SPICE dataset, a collection of quantum chemistry calculations for training machine learning potentials. It expands on the original dataset by adding mu…
Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials
Francesc Sabanes Zariquiey, Raimondas Galvelis, Emilio Gallicchio +3
This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural ne…
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Peter Eastman, Raimondas Galvelis, Raúl P. Peláez +22
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use…
Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond
Kenichiro Takaba, Iván Pulido, Pavan Kumar Behara +11
The development of reliable and extensible molecular mechanics (MM) force fields -- fast, empirical models characterizing the potential energy surface of molecular systems -- is in…
EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment
Yuanqing Wang, Iván Pulido, Kenichiro Takaba +4
Atomic partial charges are crucial parameters in molecular dynamics (MD) simulation, dictating the electrostatic contributions to intermolecular energies, and thereby the potential…
Spatial Attention Kinetic Networks with E(n)-Equivariance
Yuanqing Wang, John D. Chodera
Neural networks that are equivariant to rotations, translations, reflections, and permutations on n-dimensional geometric space have shown promise in physical modeling for tasks su…