CGPDTA: An Explainable Transfer Learning-Based Predictor With Molecule Substructure Graph for Drug-Target Binding Affinity

Abstract

Identifying interactions between drugs and targets is crucial for drug discovery and development. Nevertheless, the determination of drug-target binding affinities (DTAs) through traditional experimental methods is a time-consuming process. Conventional approaches to predicting drug-target interactions (DTIs) frequently prove inadequate due to an insufficient representation of drugs and targets, resulting in ineffective feature capture and questionable interpretability of results. To address these challenges, we introduce CGPDTA, a novel deep learning framework empowered by transfer learning, designed explicitly for the accurate prediction of DTAs. CGPDTA leverages the complementarity of drug–drug and protein–protein interaction knowledge through advanced drug and protein language models. It further enhances predictive capability and interpretability by incorporating molecular substructure graphs and protein pocket sequences to represent local features of drugs and targets effectively. Our findings demonstrate that CGPDTA not only outperforms existing methods in accuracy but also provides meaningful insights into the predictive process, marking a significant advancement in the field of drug discovery.