A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction
This addresses a bottleneck in drug discovery by improving prediction for novel drugs and targets, though it appears incremental as it builds on existing cross-field fusion strategies.
The paper tackles the problem of predicting interactions between novel drugs and targets by proposing SiamDTI, a method that fuses local and global protein information through a double-channel network structure. Experimental results show SiamDTI achieves higher accuracy than state-of-the-art methods on novel drugs and targets, with comparable performance on known ones.
Drug-target interaction (DTI) prediction is a critical component of the drug discovery process. In the drug development engineering field, predicting novel drug-target interactions is extremely crucial.However, although existing methods have achieved high accuracy levels in predicting known drugs and drug targets, they fail to utilize global protein information during DTI prediction. This leads to an inability to effectively predict interaction the interactions between novel drugs and their targets. As a result, the cross-field information fusion strategy is employed to acquire local and global protein information. Thus, we propose the siamese drug-target interaction SiamDTI prediction method, which utilizes a double channel network structure for cross-field supervised learning.Experimental results on three benchmark datasets demonstrate that SiamDTI achieves higher accuracy levels than other state-of-the-art (SOTA) methods on novel drugs and targets.Additionally, SiamDTI's performance with known drugs and targets is comparable to that of SOTA approachs. The code is available at https://anonymous.4open.science/r/DDDTI-434D.