Drug Discovery On Lit Pcba Kat2A

AUC

Results

Performance results of various models on this benchmark

Model Name	AUC	Paper Title
DGraphDTA	0.633	Drug–target affinity prediction using graph neural network and contact maps
GLAM	0.709	An adaptive graph learning method for automated molecular interactions and properties predictions
EGT+TGT-At-DP	0.746	Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
TransformerCPI	0.650	TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments

0 of 4 row(s) selected.