As ultra-large-scale virtual screening becomes critical for early-stage drug discovery, highly efficient screening methods are gaining prominence. Deep-learning-based approaches which directly estimate binding affinities without binding conformation have attracted great attention as an alternative solution to molecular docking, but the generalization capability of existing methods in vast chemical space remains uncertain due to restricted training data. Here, we introduce PharmacoNet, the first deep-learning framework for pharmacophore modeling toward ultra-fast virtual screening. PharmacoNet offers fully automated protein-based pharmacophore modeling and evaluates the potency of ligands with a parameterized analytical scoring function, ensuring high generalization ability across unseen targets and ligands. Our benchmark study shows that PharmacoNet is extremely fast yet reasonably accurate compared to traditional docking methods and existing deep learning-based scoring models. We successfully identified selective inhibitors from 187 million compounds against Cannabinoid receptors within 21 hours on a single CPU. This study uncovers the hitherto untapped potential of deep learning in pharmacophore modeling.

This article is Open Access

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