Open AccessDevelopment and Benchmarking of Open Force Field v1.0.0—the Parsley Small-Molecule Force Field
Author(s) -
Yudong Qiu,
Daniel G. A. Smith,
Simon Boothroyd,
Hyesu Jang,
David F. Hahn,
Jeffrey Wagner,
Caitlin C. Bannan,
Trevor Gokey,
Victoria T. Lim,
Chaya Stern,
Andrea Rizzi,
Bryon Tjanaka,
Gary Tresadern,
Xavier Lucas,
Michael R. Shirts,
Michael K. Gilson,
John D. Chodera,
C Bayly,
David L. Mobley,
LeePing Wang
Publication year - 2021
Publication title -
journal of chemical theory and computation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.001
H-Index - 185
eISSN - 1549-9626
pISSN - 1549-9618
DOI - 10.1021/acs.jctc.1c00571
Subject(s) - force field (fiction) , benchmarking , quantum , potential energy , computer science , virtual screening , molecular dynamics , torsion (gastropod) , statistical physics , force constant , physics , molecule , computational chemistry , chemistry , classical mechanics , quantum mechanics , medicine , surgery , marketing , business
We present a methodology for defining and optimizing a general force field for classical molecular simulations, and we describe its use to derive the Open Force Field 1.0.0 small-molecule force field, codenamed Parsley. Rather than using traditional atom typing, our approach is built on the SMIRKS-native Open Force Field (SMIRNOFF) parameter assignment formalism, which handles increases in the diversity and specificity of the force field definition without needlessly increasing the complexity of the specification. Parameters are optimized with the ForceBalance tool, based on reference quantum chemical data that include torsion potential energy profiles, optimized gas-phase structures, and vibrational frequencies. These quantum reference data are computed and are maintained with QCArchive, an open-source and freely available distributed computing and database software ecosystem. In this initial application of the method, we present essentially a full optimization of all valence parameters and report tests of the resulting force field against compounds and data types outside the training set. These tests show improvements in optimized geometries and conformational energetics and demonstrate that Parsley's accuracy for liquid properties is similar to that of other general force fields, as is accuracy on binding free energies. We find that this initial Parsley force field affords accuracy similar to that of other general force fields when used to calculate relative binding free energies spanning 199 protein-ligand systems. Additionally, the resulting infrastructure allows us to rapidly optimize an entirely new force field with minimal human intervention.