Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

5 code implementations • 13 Jul 2023 • Kenichiro Takaba, Iván Pulido, Pavan Kumar Behara, Chapin E. Cavender, Anika J. Friedman, Michael M. Henry, Hugo MacDermott Opeskin, Christopher R. Iacovella, Arnav M. Nagle, Alexander Matthew Payne, Michael R. Shirts, David L. Mobley, John D. Chodera, Yuanqing Wang

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.

Drug Discovery

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials

no code implementations • 21 Sep 2022 • Peter Eastman, Pavan Kumar Behara, David L. Dotson, Raimondas Galvelis, John E. Herr, Josh T. Horton, Yuezhi Mao, John D. Chodera, Benjamin P. Pritchard, Yuanqing Wang, Gianni de Fabritiis, Thomas E. Markland

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.