MLSolvA: solvation free energy prediction from pairwise atomistic interactions by machine learning.

ABSTRACT: Recent advances in machine learning technologies and their applications have led to the development of diverse structure-property relationship models for crucial chemical properties. The solvation free energy is one of them. Here, we introduce a novel ML-based solvation model, which calculates the solvation energy from pairwise atomistic interactions. The novelty of the proposed model consists of a simple architecture: two encoding functions extract atomic feature vectors from the given chemical structure, while the inner product between the two atomistic feature vectors calculates their interactions. The results of 6239 experimental measurements achieve outstanding performance and transferability for enlarging training data owing to its solvent-non-specific nature. An analysis of the inte

SUBMITTER: Lim H 

PROVIDER: S-EPMC8325294 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature