HelixDTA: Dual-Branch Sequence–Structure Learning with Complete Target Structures for Robust and Interpretable Drug–Target Affinity Prediction

HelixDTA achieved strong and competitive performance on the Davis and KIBA benchmarks and maintained clear advantages across most similarity-based cold-start settings, demonstrating robust generalization to compounds with unseen scaffolds and novel protein targets. Furthermore, its built-in attention mechanism enhances interpretability by highlighting molecular regions that contribute to affinity prediction. Finally, a WRN case study demonstrates the potential use of HelixDTA for structure-aware allosteric candidate prioritization by integrating complete target-level structural context with docking-based pose inspection.

In conclusion, HelixDTA offers a highly accurate, interpretable, and robust solution for DTA prediction. It highlights the value of integrating structural context with sequence-derived information and demonstrates significant potential to empower precision drug design and accelerate the drug discovery pipeline.