A Drug-Target Interaction Prediction Method Based on Attention Perception and Modality Fusion
Year: 2025
Type: article
Abstract: [Purposes] Predicting drug-target interactions is a critical step in drug discovery. The existing prediction methods based on graph neural networks have achieved good results, but there are still two challenges: first, how to extract the deep features and rich semantic information in drugs and targets more efficiently; and second, how to explicitly model and learn the interactions between drugs and targets for better prediction and interpretation. In order to solve the above problems, a drug-target interaction prediction method based on attention perception and modality fusion is proposed in this paper. [Methods] For drug branches, Graph Transformer and Graph Convolutional Neural Network were used to jointly characterize the global structures and biochemical information of drug molecules. For protein branches, a separate encoding module was employed to extract the global and local features of the protein. In order to model the relationship between drugs and targets more accurately, a Transformer-based module was designed to fuse drug and target features. [Results] Finally, the experimental results on the three public datasets show that the main evaluation indicators have been improved, and the method can predict the interaction between drugs and targets more accurately.
Authors: PENG Yang, ZHU Xiaofei, HU Dongdong
Institution: Chongqing University of Technology
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6887510100
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