Molecular Merged Hypergraph Neural Network for Explainable Solvation Gibbs Free Energy Prediction
Wenjie Du, Shuai Zhang, Zhaohui Cai, Zhiyuan Liu, Zhiyuan Liu, Junfeng Fang, Jianmin Wang, Xiang Wang, Yang Wang
Abstract
Solvation free energies play a fundamental role in various fields of chemistry and biology. Accurately determining the solvation Gibbs free energy ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="normal">Δ</mml:mi> <mml:msub> <mml:mi>G</mml:mi> <mml:mtext>solv</mml:mtext> </mml:msub> </mml:math> ) 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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="normal">Δ</mml:mi> <mml:msub> <mml:mi>G</mml:mi> <mml:mtext>solv</mml:mtext> </mml:msub> </mml:math> 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 mechanism tailored to specific solute–solvent pairs. However, its sharply increasing computational complexity limits its scalability and broader applicability. Here, we introduce an improved framework, molecular merged hypergraph neural network (MMHNN), which leverages a predefined subgraph set and replaces subgraphs with supernodes to construct a hypergraph representation. This design effectively mitigates model complexity while preserving key molecular interactions. Furthermore, to handle noninteractive or repulsive atomic interactions, MMHNN incorporates an interpretation mechanism for nodes and edges within the merged graph, leveraging the graph information bottleneck theory to enhance model explainability. Extensive experimental validation demonstrates the efficiency of MMHNN and its improved interpretability in capturing solute–solvent interactions.