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bioRxiv · 10.64898/2026.01.14.699425

On the rise of AI technologies for virtual screening

Cecchini, M.·Sinenka, H.
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Abstract

AI foundational models for predicting protein-ligand interactions and binding affinities have started to emerge. We challenged Boltz-2 on a difficult dataset constructed on ten ultra-large virtual screening hit lists of pharmacologically relevant targets with in vitro binding assays. We show that Boltz-2 is the best classifier, with a success rate twice that of any other rescoring strategy. Ligand classifications by Boltz-2 are straightforward, accurate, efficient and robust, opening to million-compound accurate rankings on commodity resources.

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Cecchini, M., Sinenka, H.. 2026-01-14. On the rise of AI technologies for virtual screening. https://doi.org/10.64898/2026.01.14.699425

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