End-to-end differentiable molecular mechanics force field construction

Yuanqing Wang, Josh Fass, and John D. Chodera
Chemical Science 13:12016, 2022 [DOI] [arXiv] [pytorch code] [JAX code]

Molecular mechanics force fields have been a workhorse for computational chemistry and drug discovery. Here, we propose a new approach to force field parameterization in which graph convolutional networks are used to perceive chemical environments and assign molecular mechanics (MM) force field parameters. The entire process of chemical perception and parameter assignment is differentiable end-to-end with respect to model parameters, allowing new force fields to be easily constructed from MM or QM force fields, extended, and applied to arbitrary biomolecules.

Escaping atom types in force fields using direct chemical perception

David L. Mobley, Caitlin C. Bannan, Andrea Rizzi, Christopher I. Bayly, John D. Chodera, Victoria T Lim, Nathan M. Lim, Kyle A. Beauchamp, Michael R. Shirts, Michael K. Gilson, Peter K. Eastman.
Journal of Chemical Theory and Computation 14:6076, 2018 [DOI] [bioRxiv]

We describe the philosophy behind a modern approach to molecular mechanics forcefield parameterization, and present initial results for the first SMIRNOFF-encoded forcefield: SMIRNOFF99Frosst.

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, Christopher I. Bayly, Kenneth Kroenlein, and John D. Chodera.
J. Phys. Chem. B 119:12912, 2015. [DOI] [PDF] // code: [GitHub] // preprint: [arXiv] 

Progress in forcefield validation and parameterization has been hindered by the availability of high-quality machine-readable physical property data for small organic molecules. We show how the NIST ThermoML dataset provides a solution to this problem, and demonstrate its utility in benchmarking the GAFF/AM1-BCC small molecule forcefield on neat liquid densities and static dielectric constants to uncover problems in the representation of low-dielectric environments.

Systematic improvement of a classical molecular model of water

Lee-Ping Wang, Teresa L. Head-Gordon, Jay W. Ponder, Pengyu Ren, John D. Chodera, Peter K. Eastman, Todd J. Martinez, and Vijay S. Pande.
J. Phys. Chem. B 117:9956, 2013. [DOI] [PDF]

A new inexpensive polarizable model of liquid water for next-generation forcefields is derived using an automated parameterization engine.

Current status of the AMOEBA polarizable force field

Jay W. Ponder, Chuanjie Wu, Pengyu Ren, Vijay S. Pande, John D. Chodera, David L. Mobley, Michael J. Schnieders, Imran Haque, David S. Lambrecht, Robert A. DiStasio Jr., Martin Head-Gordon, Gary N. I. Clark, Margaret E. Johnson, and Teresa Head-Gordon.
J. Phys. Chem. B 114:2549, 2010. [DOI] [PDF]

A report on the status of the AMOEBA polarizable force field and its ability to reproduce a diverse set of physical chemical phenomenon to high accuracy.