Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

Takaba K, Pulido I, Behara PK, Henry M, MacDermott-Opeskin H, Chodera JD, Wang Y
preprint: [arXiv]

We present a new self-consistent MM force field trained on $>$1.1M quantum chemical calculations that uses graph nets to achieve high accuracy and produce accurate protein-ligand binding free energies.

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.

The Open Force Field Evaluator: An automated, efficient, and scalable framework for the estimation of physical properties from molecular simulation

Simon Boothroyd, Lee-Ping Wang, David L. Mobley, John D. Chodera, and Michael R. Shirts

Preprint ahead of submission: [ChemRxiv]

We describe a new software framework for automated evaluation of physical properties for the benchmarking and optimization of small molecule force fields according to best practices.

Bayesian inference-driven model parameterization and model selection for 2CLJQ fluid models

Owen C Madin, Simon Boothroyd, Richard A Messerly, John D Chodera, Josh Fass, and Michael R Shirts
Preprint ahead of publication: [arXiv]

Here, we show how Bayesian inference can be used to automatically perform model selection and fit parameters for a molecular mechanics force field.