Hierarchical Attention Graph Learning with LLMs Enhancement for Molecular Solubility Prediction

Abstract

Solubility quantifies the concentration of a molecule that can dissolve in a given solvent. Accurate prediction of solubility is essential for optimizing drug efficacy, improving chemical and separation processes, waste management, among many other industrial and research applications. Predicting solubility from first principles remains a complex and computationally intensive physicochemical challenge. Recent successes of graph neural networks for molecular learning tasks inspire us to develop HASolGNN, a hierarchical-attention graph neural network for solubility prediction. (1) HASolGNN adopts a three-level hierarchical attention framework to leverage atom-bond, molecular, and interaction-graph level features. This allows a more comprehensive modeling of both intra-molecular and inter-molecular interactions for solute-solvent dissolution as a complex system. (2) To mitigate the impact of small amounts of annotated data, we also investigate the role of Large Language Models (LLMs), and introduce HASolGNN-LLMs, an LLM-enhanced predictive framework that leverages LLMs to infer annotated features and embeddings to improve representation learning. Our experiments verified that (1) HASolGNN outperforms the state-of-the-art methods in solubility prediction; and (2) HASolGNN-LLMs effectively exploits LLMs to enhance sparsely annotated data, and further improves overall accuracy.