Geometry-Enhanced Multiscale Joint Representation Learning for Drug-Target Interaction Prediction

Drug-target interactions (DTIs) are the basis of the therapeutic effect of drugs, whose accurate prediction helps reduce the cost and time of experimental screening in drug development process. Present methods for DTIs prediction often focus on the study of molecular topological structure, which weakens spatial information such as the relative position of atoms and bond angle, and fail to effectively integrate molecular information with association network information. To address this issue, we propose a novel Geometry-enhanced Multiscale Joint Representation Learning method for drug-target interaction prediction (GMJRL). GMJRL not only considers the global information in the drug-target network from the macro-scale, but also extracts the geometric structure information on the drug and the target from the microscale, including the bond angle information on the drug and the atomic coordinate information on the target. To effectively fuse different scale representations, we develop a joint representation learning method with self-attention, which can capture correlations within the same scale and consider the interscale relationships, thus achieving effective fusion of the macro-scale and microscale representations. Finally, this study introduces a negative sampling algorithm to select reliable negative samples from unlabeled drug-target pairs. Extensive experiments validate that GMJRL yields promising outcomes in predicting drug-target interactions.