On LIT-PCBA, supervised ML re-ranking reaches median EF1% of 4.49 while AutoDock-GNINA scores 2.14 and consensus does not exceed the best single scorer.
TB-IECS: an accurate machine learning-based scoring function for virtual screening
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Benchmarking Single-Pose Docking, Consensus Rescoring, and Supervised ML on the LIT-PCBA Library: A Critical Evaluation of DiffDock, AutoDock-GPU, GNINA, and DiffDock-NMDN
On LIT-PCBA, supervised ML re-ranking reaches median EF1% of 4.49 while AutoDock-GNINA scores 2.14 and consensus does not exceed the best single scorer.