PocketDTA: A pocket-based multimodal deep learning model for drug-target affinity prediction

Drug-target affinity prediction is a fundamental task in the field of drug discovery. Extracting and integrating structural information from proteins effectively is crucial to enhance the accuracy and generalization of prediction, which remains a substantial challenge. This paper proposes a pocket-based multimodal deep learning model named PocketDTA for drug-target affinity prediction, based on the principle of "structure determines function". PocketDTA introduces the pocket graph structure that encodes protein residue features pretrained using a biological language model as nodes, while edges represent different protein sequences and spatial distances. This approach overcomes the limitations of lack of spatial information in traditional prediction models with only protein sequence input. Furthermore, PocketDTA employs relational graph convolutional networks at both atomic and residue levels to extract structural features from drugs and proteins. By integrating multimodal information through deep neural networks, PocketDTA combines sequence and structural data to improve affinity prediction accuracy. Experimental results demonstrate that PocketDTA outperforms state-ofthe-art prediction models across multiple benchmark datasets by showing strong generalization under more realistic data splits and confirming the effectiveness of pocket-based methods for affinity prediction.