PfgPDI: Pocket feature-enabled graph neural network for protein-drug interaction prediction

Biomolecular interaction recognition between ligands and proteins is an essential task, which largely enhances the safety and efficacy in drug discovery and development stage. Studying the interaction between proteins and ligands can improve the understanding of disease pathogenesis and lead to more effective drug targets. Additionally, it can aid in determining drug parameters, ensuring proper absorption, distribution, and metabolism within the body. Due to incomplete feature representation or the model's inadequate adaptation to protein-ligand complexes, the existing methodologies suffer from suboptimal predictive accuracy. To address these pitfalls, in this study, we designed a new deep learning method based on transformer and GCN. We first utilized the transformer network to grasp crucial information of the original protein sequences within the smile sequences and connected them to prevent falling into a local optimum. Furthermore, a series of dilation convolutions are performed to obtain the pocket features and smile features, subsequently subjected to graphical convolution to optimize the connections. The combined representations are fed into the proposed model for classification prediction. Experiments conducted on various protein-ligand binding prediction methods prove the effectiveness of our proposed method. It is expected that the PfgPDI can contribute to drug prediction and accelerate the development of new drugs, while also serving as a valuable partner for drug testing and Research and Development engineers.