Download MendeleyAn artificial intelligence accelerated virtual screening platform for drug discovery.
Zhou, G., Rusnac, D.V., Park, H., Canzani, D., Nguyen, H.M., Stewart, L., Bush, M.F., Nguyen, P.T., Wulff, H., Yarov-Yarovoy, V., Zheng, N., DiMaio, F.(2024) Nat Commun 15: 7761-7761
- PubMed: 39237523
- DOI: https://doi.org/10.1038/s41467-024-52061-7
- Primary Citation of Related Structures:
8UXS - PubMed 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 Na V 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 V 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.
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
Organizational Affiliation:
