CKDTA: A chemical knowledge-enhanced framework for drug–target affinity prediction

Accurate drug–target affinity (DTA) prediction is a cornerstone of efficient drug discovery, as it directly accelerates the screening of potential therapeutic candidates, reduces the cost of preclinical experiments, and shortens the development cycle of new drugs. However, existing deep learning-based methods face two main challenges: (I) Purely data-driven approaches struggle to capture the functional semantics of molecules, such as the role of specific functional regions and chemical element properties in binding interactions, due to the lack of integration with chemical prior knowledge, leading to unreliable predictions; (II) the integration of topological structure from graphs and long-range dependencies from sequences is insufficient, often failing to capture complementary features, limiting the model’s generalization ability, especially for novel drugs or targets commonly encountered in early drug discovery . To address these issues, we propose CKDTA, a Chemical Knowledge Enhanced framework for Drug-Target Affinity prediction. Our framework introduces two key innovations: (1) a chemical knowledge-enhanced molecular modeling approach, which constructs a multi-layer molecular graph incorporating atom-level features, chemical element information, and functional regions, enabling the capture of functional semantics through a hierarchical attention mechanism, while leveraging chemical prior knowledge; (2) a co-attention module designed to optimize sequence interaction information by leveraging graph-based interaction data, compensating for the lack of spatial structural information in sequence data. This module fully exploits the topological structure of graphs and the long-range dependencies in sequences, capturing complementary features. Extensive experiments on benchmark datasets demonstrate that CKDTA outperforms state-of-the-art methods. Furthermore, cold-start experiments validate its generalizability, highlighting its potential for drug discovery applications.