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A Hybrid Alchemical Free Energy and Machine Learning Methodology for the Calculation of Absolute Hydration Free Energies of Small Molecules

preprint
submitted on 28.05.2020 and posted on 29.05.2020 by Jenke Scheen, Wilson Wu, Antonia S. J. S. Mey, Paolo Tosco, Mark Mackey, Julien Michel
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 forcefield parameterisation efforts.

History

Email Address of Submitting Author

mail@julienmichel.net

Institution

University of Edinburgh

Country

United Kingdom

ORCID For Submitting Author

0000-0003-0360-1760

Declaration of Conflict of Interest

No conflict of interest

Licence

Exports