DataDTA: a multi-feature and dual-interaction aggregation framework for drug-target binding affinity prediction

Yan Zhu, Lingling Zhao, Naifeng Wen, Junjie Wang, Chunyu Wang

Abstract: Accurate prediction of drug–target binding affinity (DTA) is crucial for drug discovery. The increase in the publication of large-scale DTA datasets enables the development of various computational methods for DTA prediction. Numerous deep learning-based methods have been proposed to predict affinities, some of which only utilize original sequence information or complex structures, but the effective combination of various information and protein-binding pockets have not been fully mined. Therefore, a new method that integrates available key information is urgently needed to predict DTA and accelerate the drug discovery process.