Abstract:In structure-based virtual screening (SBVS), it is critical for machine-learning scoring functions (MLSFs) to capture protein-ligand atomic interaction patterns. We generated a cross-target generalization ability benchmark for protein-ligand binding affinity prediction to assess whether MLSFs could capture these interactions. By focusing on the local domains in protein-ligand binding pockets, we developed standardized pocket Pfam-based clustering (Pfam-cluster) approach for the generalization ability benchmark… Show more
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