Boltz-2 and fine-tuned DrugFormDTA lead ML-based binding prediction while GNINA leads docking tools on a cleaned antiviral dataset, with performance varying by viral protein.
Data leakage and redundancy in the LIT-PCBA benchmark
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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.
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Benchmarking open-source tools for in silico antiviral drug discovery
Boltz-2 and fine-tuned DrugFormDTA lead ML-based binding prediction while GNINA leads docking tools on a cleaned antiviral dataset, with performance varying by viral protein.
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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.