TransGNN-DTA: A Framework for Drug-Target Affinity Prediction Based on a Chunked Transformer-GNN

Abstract

Accurate prediction of drug-target binding affinity (DTA) is a core challenge in computer-aided drug design. In this study, we propose the TransGNN-DTA framework to construct a multi-scale feature representation and dynamic fusion mechanism by hierarchically integrating Transformer and graph neural network (GNN): 1) byte-pair encoding (BPE) based unified semantic characterization of drug SMILES and protein sequences, which preserves the atomic-level chemical structure and residue-level functional motifs; 2) hierarchical Transformer-GNN encoding architecture to capture sequence global dependencies (e.g., protein functional domain interactions) and molecular local structures (e.g., drug-functional group interactions); 3) chunked adaptive training strategy, hybrid accuracy, and the combination of an optimizer and scheduler to effectively reduce the hardware resource requirements for training. On the DAVIS and KIBA datasets, the model improves the CI metrics by an average of 3.23% over the existing optimal methods (DGraphDTA, GraphDTA). The computational efficiency is significantly optimized by systematic chunking, providing a scalable end-to-end solution for large-scale DTA prediction. The source code and datasets are publicly accessible at https://github.com/Quietpeng/TransGNN_DTA.