Accurate and Efficient Multilevel Free Energy Simulations with Neural Network-Assisted Enhanced Sampling.

ABSTRACT: Free energy differences (ΔF) are essential to quantitative characterization and understanding of chemical and biological processes. Their direct estimation with an accurate quantum mechanical potential is of great interest and yet impractical due to high computational cost and incompatibility with typical alchemical free energy protocols. One promising solution is the multilevel free energy simulation in which the estimate of ΔF at an inexpensive low level of theory is combined with the correction toward a higher level of theory. The poor configurational overlap generally expected between the two levels of theory, however, presents a major challenge. We overcome this challenge by using a deep neural network model and enhanced sampling simulations. An adversarial autoencoder i

SUBMITTER: Yuan Y 

PROVIDER: S-EPMC10810721 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature