SP-DTI: subpocket-informed transformer for drug-target interaction prediction.

ABSTRACT:

Motivation

Drug-target interaction (DTI) prediction is crucial for drug discovery, significantly reducing costs and time in experimental searches across vast drug compound spaces. While deep learning has advanced DTI prediction accuracy, challenges remain: (i) existing methods often lack generalizability, with performance dropping significantly on unseen proteins and cross-domain settings; and (ii) current molecular relational learning often overlooks subpocket-level interactions, which are vital for a detailed understanding of binding sites.

Results

We introduce SP-DTI, a subpocket-informed transformer model designed to address these challenges through: (i) detailed subpocket analysis using the Cavity Identification and Analysis Routine for interaction modeling at both glob

SUBMITTER: Liu S 

PROVIDER: S-EPMC11886779 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature