58 citations · 95 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials
Peter Eastman, Pavan Kumar Behara, David L. Dotson +9
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. We describe…
A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning
Raimondas Galvelis, Stefan Doerr, Joao M. Damas +2
Fast and accurate molecular force field (FF) parameterization is still an unsolved problem. Accurate FFs are not generally available for all molecules, like novel druglike molecule…