A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction

TL;DR

This paper introduces SiamDTI, a novel cross-field fusion method using a double channel network to improve drug-target interaction prediction, especially for novel drugs and targets, outperforming existing methods.

Contribution

The paper proposes SiamDTI, a new cross-field fusion strategy with a double channel network for enhanced DTI prediction, particularly for unseen drugs and targets.

Findings

SiamDTI outperforms SOTA methods on novel drugs and targets.

SiamDTI's performance on known drugs and targets is comparable to SOTA.

The method effectively utilizes global protein information for better predictions.

Abstract

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.