Drug target affinity prediction based on multi-scale gated power graph and multi-head linear attention mechanism.

ABSTRACT: For the purpose of developing new drugs and repositioning existing ones, accurate drug-target affinity (DTA) prediction is essential. While graph neural networks are frequently utilized for DTA prediction, it is difficult for existing single-scale graph neural networks to access the global structure of compounds. We propose a novel DTA prediction model in this study, MAPGraphDTA, which uses an approach based on a multi-head linear attention mechanism that aggregates global features based on the attention weights and a multi-scale gated power graph that captures multi-hop connectivity relationships of graph nodes. In order to accurately extract drug target features, we provide a gated skip-connection approach in multiscale graph neural networks, which is used to fuse multiscale features to

SUBMITTER: Hu S 

PROVIDER: S-EPMC11844845 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature