07-12, 15:25–15:55 (America/Chicago), Grand Salon C
The Open Force Field (OpenFF) initiative was formed to build a new generation of force fields for molecular dynamics (MD) simulations using modern data-driven techniques. Openness is one of our fundamental founding principles, and everything we produce is released openly and accessibly so that the community can validate, modify, or extend our work. Here we introduce some flagship packages in our ecosystem and the advances they have enabled in force field science and MD workflows. These include fitting custom functional forms, exploring the addition of off-site charges, and using neural networks to assign charges to protein-ligand systems.
Background
Molecular dynamics (MD) simulations are now critical components in pharmaceutical and biomolecular research. A potential energy function called a ‘force field’ is used to solve the differential equations that describe the particle motion. A vast number of different force fields have now been released, each fit to experimental or quantum chemistry data to reproduce specific properties in a limited region of chemical space. However, the core of most of these date from work published decades ago, and new force field development has primarily taken the form of incremental improvements guided by human chemical intuition rather than systematic, reproducible methods.
Outline
The Open Force Field (OpenFF) initiative was formed to produce open and extensible infrastructure to build a new generation of MD force fields. We have now developed many software packages for constructing, applying, and benchmarking force fields. We have also generated several high-quality quantum chemistry datasets. Everything is available freely on GitHub, Zenodo, and the MolSSI QCArchive server. This work has been successfully used to investigate potential improvements to force fields, as well as simplify many previously difficult aspects of preparing MD systems.
Here we will introduce the OpenFF-Toolkit and OpenFF-Interchange packages. We can use them to quickly assign force field parameters to arbitrary systems of small molecules, and then write these systems out in common MD formats for simulation. We also introduce the OpenFF-Bespokefit package for fitting custom torsion parameters, as well as the OpenFF-QCSubmit package for interacting with QCArchive. We show how to use the datasets we have released on QCArchive.
We will finally show some of the advancements enabled by our work. The OpenFF-Evaluator package was instrumental in investigating the effect of using a custom potential for van der Waals’ parameters. We used OpenFF-Recharge to explore adding off-site charges with virtual sites. Finally, we describe the development of a neural network for quickly assigning conformer-independent partial charges – this also employed OpenFF-Recharge, as well as OpenFF-NAGL.
We hope these examples give a brief overview of how OpenFF can help both common everyday MD tasks as well as larger scientific investigations.
Previous talks
I've previously given keynote talks at the Open Force Field annual meetings and presented at open science meetings convened by the NIH, the NSF, and groups in the scientific computing and molecular simulation communities.
Jeff Wagner is the Technical Lead at Open Force Field, a pre-competitive effort supported by a mix of industry partners and government funding. OpenFF aims to build extensible tools and datasets to advance the state-of-the-art in molecular modeling. He received his PhD in chemistry from UCSD in 2018 and is broadly interested in understanding out how sustainable organizations can exist at the interface of academia, industry, and open source.