Solvation Free Energy Calculations with Quantum Mechanics/Molecular Mechanics and Machine Learning Models.

Published

Journal Article

For exploration of chemical and biological systems, the combined quantum mechanics and molecular mechanics (QM/MM) and machine learning (ML) models have been developed recently to achieve high accuracy and efficiency for molecular dynamics (MD) simulations. Despite its success on reaction free energy calculations, how to identify new configurations on insufficiently sampled regions during MD and how to update the current ML models with the growing database on the fly are both very important but still challenging. In this article, we apply the QM/MM ML method to solvation free energy calculations and address these two challenges. We employ three approaches to detect new data points and introduce the gradient boosting algorithm to reoptimize efficiently the ML model during ML-based MD sampling. The solvation free energy calculations on several typical organic molecules demonstrate that our developed method provides a systematic, robust, and efficient way to explore new chemistry using ML-based QM/MM MD simulations.

Full Text

Duke Authors

Cited Authors

  • Zhang, P; Shen, L; Yang, W

Published Date

  • January 15, 2019

Published In

Volume / Issue

  • 123 / 4

Start / End Page

  • 901 - 908

PubMed ID

  • 30557020

Pubmed Central ID

  • 30557020

Electronic International Standard Serial Number (EISSN)

  • 1520-5207

International Standard Serial Number (ISSN)

  • 1520-6106

Digital Object Identifier (DOI)

  • 10.1021/acs.jpcb.8b11905

Language

  • eng