PGDTA: Predicting Drug-Target Affinity Using Three-Dimensional Structure of Protein Pocket and Graph Neural Network

Drug-Target Affinity (DTA) prediction plays a crucial role in drug discovery, and accurate DTA prediction can significantly reduce the cost of drug development. While most studies focus on the entire protein structure, they often overlook the local structure of protein pockets which play a vital role in DTA due to their direct interaction with drugs. At the methodological level, numerous deep learning approaches have been developed to predict DTA using protein and drug sequences or structures, yet the effective utilization of protein and drug features remains a pressing challenge. Our study proposes leveraging pre-trained models to represent sequence features of protein and drug separately. Subsequently, we construct a geometric graph neural network module capable of parallelizing diverse spatial structural information. We conducted experiments on three public datasets and compared our approach with current state-of-the-art (SOTA) methods, validating the effectiveness of our method. Furthermore, we compared the impact of entire proteins versus protein pockets on DTA, further affirming the reliability of our approach. Consequently, our method (called PGDTA) enhances the accuracy of DTA prediction, thereby aiding in improving the efficiency of the drug discovery process.