Multi-order graph attention network for water solubility prediction and interpretation.

ABSTRACT: The water solubility of molecules is one of the most important properties in various chemical and medical research fields. Recently, machine learning-based methods for predicting molecular properties, including water solubility, have been extensively studied due to the advantage of effectively reducing computational costs. Although machine learning-based methods have made significant advances in predictive performance, the existing methods were still lacking in interpreting the predicted results. Therefore, we propose a novel multi-order graph attention network (MoGAT) for water solubility prediction to improve the predictive performance and interpret the predicted results. We extracted graph embeddings in every node embedding layer to consider the information of diverse neighboring orders

SUBMITTER: Lee S 

PROVIDER: S-EPMC9981901 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature