ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion
This addresses the problem of limited accuracy in drug discovery for pharmaceutical researchers, though it appears incremental as it builds on existing deep learning approaches.
The paper tackled drug-target affinity prediction by proposing ViDTA, which introduces virtual nodes in GNNs to capture global drug information and uses attention-based feature fusion, achieving state-of-the-art results on benchmarks like Davis, Metz, and KIBA.
Drug-target interaction is fundamental in understanding how drugs affect biological systems, and accurately predicting drug-target affinity (DTA) is vital for drug discovery. Recently, deep learning methods have emerged as a significant approach for estimating the binding strength between drugs and target proteins. However, existing methods simply utilize the drug's local information from molecular topology rather than global information. Additionally, the features of drugs and proteins are usually fused with a simple concatenation operation, limiting their effectiveness. To address these challenges, we proposed ViDTA, an enhanced DTA prediction framework. We introduce virtual nodes into the Graph Neural Network (GNN)-based drug feature extraction network, which acts as a global memory to exchange messages more efficiently. By incorporating virtual graph nodes, we seamlessly integrate local and global features of drug molecular structures, expanding the GNN's receptive field. Additionally, we propose an attention-based linear feature fusion network for better capturing the interaction information between drugs and proteins. Experimental results evaluated on various benchmarks including Davis, Metz, and KIBA demonstrate that our proposed ViDTA outperforms the state-of-the-art baselines.