Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations. Issue 7 (3rd February 2023)

• Record Type: Journal Article
• Authors: Yao, Songyuan; Van, Richard; Pan, Xiaoliang; Park, Ji Hwan; Mao, Yuezhi; Pu, Jingzhi; Mei, Ye; Shao, Yihan
• Is Part Of: RSC advances. Volume 13:Issue 7(2023)
• Page Start: 4565
• Page End: 4577
• Publication Date: 2023-02-03
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/d2ra08180f
• Languages: English
• ISSNs: 2046-2069

Abstract

Abstract : Here we investigated the use of machine learning (ML) techniques to "derive" an implicit solvent model based on the average solvent environment configurations from explicit solvent molecular dynamics (MD) simulations. Abstract : Inspired by the recent work from Noé and coworkers on the development of machine learning based implicit solvent model for the simulation of solvated peptides [Chen et al., J. Chem. Phys., 2021, 155, 084101], here we report another investigation of the possibility of using machine learning (ML) techniques to "derive" an implicit solvent model directly from explicit solvent molecular dynamics (MD) simulations. For alanine dipeptide, a machine learning potential (MLP) based on the DeepPot-SE representation of the molecule was trained to capture its interactions with its average solvent environment configuration (ASEC). The predicted forces on the solute deviated only by an RMSD of 0.4 kcal mol −1 Å −1 from the reference values, and the MLP-based free energy surface differed from that obtained from explicit solvent MD simulations by an RMSD of less than 0.9 kcal mol −1 . Our MLP training protocol could also accurately reproduce combined quantum mechanical molecular mechanical (QM/MM) forces on the quantum mechanical (QM) solute in ASEC environment, thus enabling the development of accurate ML-based implicit solvent models for ab initio -QM MD simulations. Such ML-based implicit solvent models for QM calculations are cost-effective in both the training stage, where the use of ASEC reduces the number of data points to be labelled, and the inference stage, where the MLP can be evaluated at a relatively small additional cost on top of the QM calculation of the solute.