An interpretable geometric graph neural network for enhancing the generalizability of drug-target interaction prediction.

ABSTRACT:

Background

Accurate prediction of drug-target interactions (DTIs) is essential for advancing drug discovery. Although numerous computational methods have been proposed, many exhibit limited generalization, particularly when dealing with unseen drugs or targets.

Results

To address this challenge, we introduce GPS-DTI, a deep learning framework designed to capture both local and global features of drugs and proteins, thereby enhancing predictive robustness. Specifically, GPS-DTI employs a graph isomorphism network with edge features (GINE)-based graph neural network, combined with a multi-head attention mechanism (MHAM), to effectively model the structural characteristics of drug molecules. For proteins, representations are derived from the pre-trained Evolutionary Scale Model

SUBMITTER: Xiong A 

PROVIDER: S-EPMC12659342 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature