DVNDTA: a dual virtual node based heterogeneous interaction model for accurate prediction of drug-target affinity

Efficient prediction of drug-target affinity (DTA) is crucial for accelerating drug discovery. Recently, deep learning approaches leveraging 3D complexes have shown strong potential for drug-target affinity prediction. However, most of these approaches focus solely on local structural information at the binding site to learn non-covalent interactions, which may limit their ability to capture complex binding mechanisms. This paper introduces a dual virtual node-based heterogeneous interaction model for DTA (DVNDTA), which incorporates global contextual information of both the drug and the target pocket into the affinity prediction by utilizing two virtual nodes. This dynamically weighted global-local cooperative perception mechanism enhances DVNDTA's ability to capture drug-target binding patterns. Moreover, the states of the virtual nodes are dynamically updated as the network layers deepen, enabling a bidirectional feedback mechanism between global and local information. This iterative refinement of feature representations serves as an abstract computational proxy for the mutual adjustments in the drug-target binding process. Experimental results across multiple benchmark datasets demonstrate that DVNDTA outperforms the state-of-the-art affinity prediction models, exhibiting good performance and generalization ability.