Spatial attention kinetic network with E(n) equivariance

Yuanqing Wang and John D. Chodera
preprint: [arXiv] [code]

This work descibes Spatial Attention Kinetic Networks (SAKE), a new E(n)-equivariant architecture that uses spatial attention, enabling the construction of extremely performant but still accurate machine learning potentials, as well as flows capable of prediction dynamics.

Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization at Scale

Horton JT, Boothroyd S, Wagner W, Mitchell JA, Gokey T, Dotson DL, Behara PK, Ramaswamy VK, Mackey M, Chodera JD, Anwar J, Mobley DL, and Cole DJ
Journal of Chemical Informatics and Modeling 62:22, 2022 [DOI]

We describe an automated pipeline for generating tailored force field parameters for small molecules using quantum chemical or quantum machine learning potentials.

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.

Improving force field accuracy by training against condensed-phase mixture properties

Boothroyd S, Madin OC, Mobley DL, Wang L-P, Chodera JD, and Shirts MR
Journal of Chemical Theory and Computation 18:3577, 2022 [DOI] [GitHub]

We use a new automated framework for physical property evaluation and fitting to show how molecular mechanics force fields can be systematically improved by fitting to condensed phase properties.

Antibodies to the SARS-CoV-2 receptor-binding domain that maximize breadth and resistance to viral escape

Tyler N Starr, Nadine Czudnochowski, Fabrizia Zatta, Young-Jun Park, Zhuoming Liu, Amin Addetia, Dora Pinto, Martina Beltramello, Patrick Hernandez, Allison J Greaney, Roberta Marzi, William G Glass, Ivy Zhang, Adam S Dingens, John E Bowen, Jason A Wojcechowskyj, Anna De Marco, Laura E Rosen, Jiayi Zhou, Martin Montiel-Ruiz, Hannah Kaiser, Heather Tucker, Michael P Housley, Julia Di Iulio, Gloria Lombardo, Maria Agostini, Nicole Sprugasci, Katja Culap, Stefano Jaconi, Marcel Meury, Exequiel Dellota, Elisabetta Cameroni, Tristan I Croll, Jay C Nix, Colin Havenar-Daughton, Amalio Telenti, Florian A Lempp, Matteo Samuele Pizzuto, John D Chodera, Christy M Hebner, Sean PJ Whelan, Herbert W Virgin, David Veesler, Davide Corti, Jesse D Bloom, Gyorgy Snell
Nature, in press. [DOI] [bioRxiv] [GitHub]

We comprehensively characterize escape, breadth, and potency across a panel of SARS-CoV-2 antibodies targeting the receptor binding domain, including the parent antibody of the recently approved Vir antibody drug (Sotrovimab), illuminating escape mutations with structural and dynamic insight into their mechanism of action.

Capturing non-local through-bond effects when fragmenting molecules for quantum chemical torsion scans

Chaya D. Stern, Christopher I. Bayly, Daniel G. A. Smith, Josh Fass, Lee-Ping Wang, David L. Mobley, and John D. Chodera
Preprint ahead of submission
[bioRxiv] [GitHub code] [GitHub data] [Metadata] [OSF]

Best practices for alchemical free energy calculations

Mey ASJS, Allen B, Bruce Macdonald HE, Chodera JD, Kuhn M, Michel J, Mobley DL, Naden LN, Prasad S, Rizzi A, Scheen J, Shirts MR, Tresadern G, and Xu H.
Living Journal of Computational Molecular Sciences 2022 [DOI]
[arXiv] [GitHub]

This living review for the Living Journal of Computational Molecular Sciences (LiveCoMS) covers the essential considerations for running alchemical free energy calculations for rational molecular design for drug discovery.

Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentials

Dominic A. Rufa, Hannah E. Bruce Macdonald, Josh Fass, Marcus Wieder, Patrick B. Grinaway, Adrian E. Roitberg, Olexandr Isayev, and John D. Chodera.
Preprint ahead of submission.
[bioRxiv] [GitHub]

In this first use of hybrid machine learning / molecular mechanics (ML/MM) potentials for alchemical free energy calculations, we demonstrate how the improved modeling of intramolecular ligand energetics offered by the quantum machine learning potential ANI-2x can significantly improve the accuracy in predicting kinase inhibitor binding free energy by reducing the error from 0.97~kcal/mol to 0.47~kcal/mol, which could drastically reduce the number of compounds that must be synthesized in lead optimization campaigns for minimal additional computational cost.