Explainable Supervised Machine Learning Model To Predict Solvation Gibbs Energy.

ABSTRACT: Many challenges persist in developing accurate computational models for predicting solvation free energy (ΔG_sol). Despite recent developments in Machine Learning (ML) methodologies that outperformed traditional quantum mechanical models, several issues remain concerning explanatory insights for broad chemical predictions with an acceptable speed-accuracy trade-off. To overcome this, we present a novel supervised ML model to predict the ΔG_sol for an array of solvent-solute pairs. Using two different ensemble regressor algorithms, we made fast and accurate property predictions using open-source chemical features, encoding complex electronic, structural, and surface area descriptors for every solvent and solute. By integrating molecular properties and chemi

SUBMITTER: Ferraz-Caetano J 

PROVIDER: S-EPMC11005042 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature