Deep Drug-Target Binding Affinity Prediction Base on Multiple Feature Extraction and Fusion.

ABSTRACT: Accurate drug-target binding affinity (DTA) prediction is crucial in drug discovery. Recently, deep learning methods for DTA prediction have made significant progress. However, there are still two challenges: (1) recent models always ignore the correlations in drug and target data in the drug/target representation process and (2) the interaction learning of drug-target pairs always is by simple concatenation, which is insufficient to explore their fusion. To overcome these challenges, we propose an end-to-end sequence-based model called BTDHDTA. In the feature extraction process, the bidirectional gated recurrent unit (GRU), transformer encoder, and dilated convolution are employed to extract global, local, and their correlation patterns of drug and target input. Additionally, a module com

SUBMITTER: Li Z 

PROVIDER: S-EPMC11755178 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature