An artificial intelligence accelerated virtual screening platform for drug discovery
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 Na<sub>V</sub>1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to Na<sub>V</sub>1.7, 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.
Authors
- Hai M. Nguyen (ORCID: https://orcid.org/0000-0002-1422-7041)
- Guangfeng Zhou
- Hahnbeom Park (ORCID: https://orcid.org/0000-0002-7129-1912)
- Domnița-Valeria Rusnac (ORCID: https://orcid.org/0000-0001-8828-7701)
- Matthew F. Bush (ORCID: https://orcid.org/0000-0003-3526-4973)
- Heike Wulff (ORCID: https://orcid.org/0000-0003-4437-5763)
- Daniele Canzani (ORCID: https://orcid.org/0000-0002-9390-0571)
- Lance Stewart (ORCID: https://orcid.org/0000-0003-4264-5125)
- Ning Zheng (ORCID: https://orcid.org/0000-0002-1039-1581)
- Phuong T. Nguyen (ORCID: https://orcid.org/0000-0002-9461-7807)
- Vladimir M. Yarov-Yarovoy (ORCID: https://orcid.org/0000-0002-2325-4834)
- Frank DiMaio (ORCID: https://orcid.org/0000-0002-7524-8938)
Institutions
- Howard Hughes Medical Institute (US)
- University of Washington (US)
- Korea Institute of Brain Science (KR)
- Korean Association Of Science and Technology Studies (KR)
- Korea Institute of Science and Technology (KR)
- University of California, Davis (US)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Nature Communications
- Published
- 2024-09-05
- DOI
- https://doi.org/10.1038/s41467-024-52061-7
- Citations
- 153
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 55.30
Funders
- National Science Foundation
- Howard Hughes Medical Institute
- U.S. Department of Energy
- Washington State University
- National Research Foundation
- National Research Foundation of Korea
- National Institutes of Health
- Defense Advanced Research Projects Agency
- Defense Threat Reduction Agency
- Office of Science
- National Institute of General Medical Sciences
- Division of Chemistry
- Division of Molecular and Cellular Biosciences
- Argonne National Laboratory