GSF-DTA: An Innovative Graph-Sequence Fusion Framework for Drug-Target Affinity Prediction

Drug development is a lengthy and intricate process, where predicting drug-target affinity (DTA) is a vital step. Although traditional experimental techniques yield accurate and reliable results, their high cost and limited throughput render them impractical for large-scale applications. In contrast, computational approaches offer notable advantages in terms of scalability and operational efficiency. However, most existing models focus solely on either sequence information or molecular graph structure, limiting their capacity to capture the multifaceted nature of drug-target interactions. In the present work, we propose GSF-DTA, a novel graph-sequence fusion framework for DTA prediction. GSF-DTA integrates graph-based structural features and sequence-derived semantic representations to capture the interplay between drugs and targets. Quantitative evaluations demonstrate that GSF-DTA achieves superior predictive accuracy and exhibits strong generalization capabilities on the large-scale BindingDB dataset. Notably, GSF-DTA demonstrates robust performance in cold-start scenarios, enabling effective prediction for previously unseen drugs or targets. Extensive ablation studies and interpretability analyses further validate the effectiveness and transparency of our approach. Overall, GSF-DTA provides a promising and generalizable strategy for improving DTA prediction accuracy, contributing to the acceleration of drug design and discovery.