CoAff-DTI: Fine-grained drug-target interaction prediction using pre-trained language models and affinity-guided mechanisms

Abstract

Accurate prediction of drug-target interactions (DTI) is essential for drug discovery. Despite the success of pre-trained language models (PLMs) in learning robust molecular and protein representations, a fundamental challenge remains in characterizing the fine-grained, localized biochemical interactions between drug substructures and protein binding sites. Such critical interaction patterns are often underrepresented in conventional global embedding approaches, thereby limiting both predictive accuracy and biological interpretability. To address this challenge, we propose CoAff-DTI, an end-to-end deep learning framework designed to enhance multi-scale interaction modeling for DTI prediction. The model introduces three key components. First, a token-level decomposition strategy is employed to transform global embeddings into pharmacophore- and residue-level representations, facilitating the capture of localized features. Second, an Affinity-Guided Cross-Attention (AGCA) module is designed to explicitly model fine-grained interactions between ligand substructures and protein residues. Third, an Affinity-Gating Fusion (AGF) module is proposed to enhance cross-modal feature integration by dynamically modeling element-wise interactions. Extensive experiments on multiple benchmark datasets demonstrate that CoAff-DTI consistently outperforms state-of-the-art methods. In addition, attention-based visualization results suggest improved interpretability, as the model's learned attention patterns align effectively with experimentally verified binding regions.