Predicting drug solubility in binary solvent mixtures using graph convolutional networks: a comprehensive deep learning approach.

ABSTRACT: The prediction of drug solubility represents a fundamental challenge in pharmaceutical development, with traditional experimental methods proving both time-intensive and resource-demanding. This study presents a comprehensive evaluation of Graph Convolutional Networks (GCNs) for predicting drug solubility in binary solvent mixtures across diverse temperature ranges. We used an extensive dataset comprising 27,000 solubility measurements encompassing 123 small-molecule solutes, 44 solvents, and 110 binary solvent combinations measured across varied temperatures (273-373 K). Our GCN architecture incorporates multi-head attention mechanisms, hierarchical molecular representation learning, and sophisticated pooling strategies to capture complex molecular interactions. The proposed GCN model ach

SUBMITTER: Amiri M 

PROVIDER: S-EPMC12753786 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature