Adding virtual nodes to a graph transformer and using gated attention fusion improves drug-target affinity prediction on Davis, Metz, and KIBA by small margins.
The recent progress in proteoch emometric modelling: focusing on target descriptors, cross-term des criptors and application scope[J]
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ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion
Adding virtual nodes to a graph transformer and using gated attention fusion improves drug-target affinity prediction on Davis, Metz, and KIBA by small margins.