Hybrid Alchemical Free Energy/Machine-Learning Methodology for the Computation of Hydration Free Energies

Jenke Scheen, Wilson Wu, Antonia S. J. S. Mey, Paolo Tosco, Mark Mackey, Julien Michel

Research output: Contribution to journalArticlepeer-review

Abstract

A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration free energies. The hybrid FEP/ML methodology was trained on a subset of the FreeSolv database and retrospectively shown to outperform most submissions from the SAMPL4 competition. Compared to pure machine-learning approaches, FEP/ML yields more precise estimates of free energies of hydration and requires a fraction of the training set size to outperform standalone FEP calculations. The ML-derived correction terms are further shown to be transferable to a range of related FEP simulation protocols. The approach may be used to inexpensively improve the accuracy of FEP calculations and to flag molecules which will benefit the most from bespoke force field parametrization efforts.
Original languageEnglish
JournalJournal of Chemical Information and Modeling
Early online date4 Aug 2020
DOIs
Publication statusE-pub ahead of print - 4 Aug 2020

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