NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics

Galvelis R, Varela-Rial A, Doerr S, Fino R, Eastman P, Markland TE,  Chodera JD, and de Fabritiis G
Journal of Chemical Information and Modeling 63:5701, 2023 [DOI] [arXiv]

We demonstrate that a new generation of quantum machine learning (QML) potentials based on neural networks---which can achieve quantum chemical accuracy at a fraction of the cost---can be implemented efficiently in the OpenMM molecular dynamics simulation engine as part of hybrid machine learning / molecular mechanics (ML/MM) potentials that promise to deliver superior accuracy for modeling protein-ligand interactions.

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.

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.

Teaching free energy calculations to learn from experimental data

Marcus Wieder, Josh Fass, and John Chodera
[bioRxiv] [code] [data]

We show, for the first time, how alchemical free energy calculations can be used to not only compute free energy differences between small molecules involving covalent bond rearrangements in systems treated entirely with quantum machine learning potentials, but that these calculations have the capacity to learn to efficiently generalize from conditioning on experimental free energy data.

Discovery of SARS-CoV-2 main protease inhibitors using a synthesis-directed de novo design model

Aaron Morris, William McCorkindale, the COVID Moonshot Consortium, Nir Drayman, John D Chodera, Savaş Tay, Nir London, and Alpha A. Lee.
Chemical Communications 57:5909, 2021
[DOI]

We show how a machine learning models of ligand affinity can be coupled to synthetic enumeration models to rapidly generate potent inhibitors of the SARS-CoV-2 main viral protease.

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.

Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems

Gkeka P, Stoltz G, Farimani AB, Belkacemi Z, Ceriotti M, Chodera JD, Dinner AR, Ferguson A, Maillet JB, Minoux H, Peter C, Pietrucci F, Silveira A, Tkatchenko A, Trstanova Z, Wiewiora R, Leliévre T.
Journal of Chemical Theory and Computation 60:6211, 2020. [DOI] [arXiv]

We review the state of the art in applying machine learning to coarse grain force fields in space and time to study mutliscale dynamics.