Leveraging Graph Neural Networks for Enhanced Prediction of Molecular Solubility via Transfer Learning

Abstract

In this study, we explore the potential of graph neural networks (GNNs), in combination with transfer learning, for the prediction of molecular solubility, a crucial property in drug discovery and materials science. Our approach begins with the development of a GNN-based model to predict the dipole moment of molecules. The extracted dipole moment, alongside a selected set of molecular descriptors, feeds into a subsequent predictive model for water solubility. This two-step process leverages the inherent correlations between molecular structure and its physical properties, thus enhancing the accuracy and generalizability. Our data showed that GNN models with attention mechanism and those utilize bond properties outperformed other models. Especially, 3D GNN models such as ViSNet exhibited outstanding performance, with an R2 value of 0.9980. For the prediction of water solubility, the inclusion of dipole moments greatly enhanced the predictive power of various machine learning models. Our methodology demonstrates the effectiveness of GNNs in capturing complex molecular features and the power of transfer learning in bridging related predictive tasks, offering a novel approach for computational predictions in chemistry.