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.
MSGNN-DTA: Multi-Scale Top ological Feature Fusion Based on Graph Neural Networks for Drug–Targ et Bind- ing Affinity Prediction[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.