Molecular Merged Hypergraph Neural Network for Explainable Solvation Gibbs Free Energy Prediction.

ABSTRACT: Solvation free energies play a fundamental role in various fields of chemistry and biology. Accurately determining the solvation Gibbs free energy ( ΔGsolv ) of a molecule in a given solvent requires a deep understanding of the intrinsic relationships between solute and solvent molecules. While deep learning methods have been developed for ΔGsolv prediction, few explicitly model intermolecular interactions between solute and solvent molecules. The molecular modeling graph neural network more closely aligns with real-world chemical processes by explicitly capturing atomic-level interactions, such as hydrogen bonding. It achieves this by initially establishing indiscriminate connections between intermolecular atoms, which are subsequently refined using an attention-based aggregation mechanis

SUBMITTER: Du W 

PROVIDER: S-EPMC12355008 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature