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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Nature Machine Intelligence 7(10), 1713– 1725 (2025) https://doi.org/10.1038/s42256-025-01124-5
1Pith papers citing it
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q-bio.BMtop field · 1 papers
CONDITIONALtop verdict bucket · 1 papers
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Pith has found this work in 1 reviewed paper. Its strongest current cluster is q-bio.BM (1 papers). The largest review-status bucket among citing papers is CONDITIONAL (1 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
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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.