MFCADTI: improving drug-target interaction prediction by integrating multiple feature through cross attention mechanism.

ABSTRACT: Accurately identifying potential drug-target interactions (DTIs) is a critical step in drug discovery. Multiple heterogeneous biological data provide abundant features for DTI prediction. Many computational methods have been proposed based on these data. However, most of these methods either extract features from sequences or from networks, utilizing only one aspect of the characteristics of drugs and targets, neglecting the complementary information between these two types of features. In fact, integrating different types of features will provide more valuable information for DTI prediction. In this article, we propose a novel method to improve the predictive capability for DTIs, named MFCADTI, by integrating multi-source feature through cross-attention mechanisms. The method extracts net

SUBMITTER: Quan N 

PROVIDER: S-EPMC11834641 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature