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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…
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…
Towards Automated Benchmarking of Atomistic Forcefields: Neat Liquid Densities and Static Dielectric Constants from the ThermoML Data Archive
Kyle A. Beauchamp, Julie M. Behr, Ariën S. Rustenburg +3
Atomistic molecular simulations are a powerful way to make quantitative predictions, but the accuracy of these predictions depends entirely on the quality of the forcefield employe…