AI models improve ligand classification accuracy in pharmacological applications, indicating better virtual screening efficiency.
AI foundational models for predicting protein-ligand interactions and binding affinities have started to emerge. We challenged Boltz-2 on a difficult data set constructed on ten ultralarge 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 et al. (2026) studied this question.
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