Abstract
Drug-target binding affinity (DTA) prediction is vital for drug repositioning. The accuracy and generalizability of DTA models remain a major challenge. Here, we develop a model composed of BERT-Trans Block, Multi-Trans Block, and DTI Learning modules, referred to as Molecular Representation Encoder-based DTA prediction (MREDTA). MREDTA has three advantages: (1) extraction of both local and global molecular features simultaneously through skip connections; (2) improved sensitivity to molecular structures through the Multi-Trans Block; (3) enhanced generalizability through the introduction of BERT. Compared with 12 advanced models, benchmark testing of KIBA and Davis datasets demonstrated optimal performance of MREDTA. In case study, we applied MREDTA to 2034 FDA-approved drugs for treating non-small-cell lung cancer (NSCLC), all of which act on mutant EGFRT790M protein. The corresponding molecular docking results demonstrated the robustness of MREDTA.
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