An artificial intelligence accelerated virtual screening platform for drug discovery

Guangfeng Zhou, Domnita-Valeria Rusnac, Hahnbeom Park, Daniele Canzani, Hai Minh Nguyen, Lance Stewart, Matthew F. Bush, Phuong Tran Nguyen, Heike Wulff, Vladimir Yarov-Yarovoy, Ning Zheng () and Frank DiMaio ()
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Guangfeng Zhou: University of Washington
Domnita-Valeria Rusnac: University of Washington
Hahnbeom Park: Korea Institute of Science and Technology
Daniele Canzani: University of Washington
Hai Minh Nguyen: University of California Davis
Lance Stewart: University of Washington
Matthew F. Bush: University of Washington
Phuong Tran Nguyen: University of California Davis
Heike Wulff: University of California Davis
Vladimir Yarov-Yarovoy: University of California Davis
Ning Zheng: University of Washington
Frank DiMaio: University of Washington

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel NaV1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to NaV1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.

Date: 2024
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DOI: 10.1038/s41467-024-52061-7

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