CAMF-DTI: Enhancing Drug-Target Interaction Prediction via Coordinate Attention and Multi-Scale Feature Fusion.

ABSTRACT: The accurate prediction of drug-target interactions is essential for drug discovery and development. However, current models often struggle with two challenges. First, they fail to model the directional flow and positional sensitivity of protein sequences, which are critical for identifying functional interaction regions. Second, they lack mechanisms to integrate multi-scale information from both local binding sites and broader structural context. To overcome these limitations, we propose CAMF-DTI, a novel framework that incorporates coordinate attention, multi-scale feature fusion, and cross-attention to enhance both the representation and interaction learning of drug and protein features. Drug molecules are represented as molecular graphs and encoded using graph convolutional networks, w

SUBMITTER: Mi J 

PROVIDER: S-EPMC12651166 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature