An artificial intelligence accelerated virtual screening platform for drug discovery
University of Washington · Howard Hughes Medical Institute · +5 more institutions
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…
Citation impact
- FWCI
- 58.35
- Percentile
- 100%
- References
- 79
Authors
12- GZGuangfeng ZhouCorresponding
University of Washington
- DRDomnița-Valeria Rusnac
Howard Hughes Medical Institute, University of Washington
- HPHahnbeom Park
Korea Institute of Brain Science, Korean Association Of Science and Technology Studies, Korea Institute of Science and Technology, Sungkyunkwan University
- DCDaniele Canzani
University of Washington
- HMHai M. Nguyen
University of California, Davis
Topics & keywords
- Virtual screening
- Drug discovery
- Binding affinities
- Docking (animal)
- Computer science
- Computational biology
- Ubiquitin ligase
- Bioinformatics
- Good health and well-being
Funding
- NSNational Science FoundationAwards: 1807382, 2203513, DE-AC02-05CH11231, DE-AC02-06CH11357, P30 GM124169
- HHHoward Hughes Medical Institute
- UDU.S. Department of EnergyAwards: -AC02-05CH11231, AC02-06CH11357, 05CH11231, AC02-05CH11231, DE-AC02, 06CH11357, DE-AC02-05CH11231, DE-AC02-06CH11357, GM124169, DE-AC02-
- WSWashington State UniversityAward: DE-AC02-06CH11357
- NRNational Research Foundation
- NRNational Research Foundation of KoreaAwards: DE-AC02-06CH11357, 2022R1C1C1007817
- NINational Institutes of HealthAwards: GM124169, P30 GM124169-01, P30 GM124169, DE-AC02-06CH11357, DE-AC02-05CH11231
- DADefense Advanced Research Projects AgencyAwards: HR0011-21-2-0012, HR001120S0052, DE-AC02-05CH11231
- DTDefense Threat Reduction AgencyAwards: HDTRA1-22-1-0012, GRANT13030960, DE-AC02-05CH11231, HDTRA1
- OOOffice of ScienceAwards: DE-AC02-06CH11357, AC02-05CH11231, -AC02-05CH11231, DE-AC02, 06CH11357, AC02-06CH11357
- NINational Institute of General Medical SciencesAwards: DE-AC02-05CH11231, DE-AC02-06CH11357, P30 GM124169, P30 GM124169-01, GM124169-01, GM124169
- DODivision of ChemistryAwards: 2203513, DE-AC02-05CH11231, 1807382
- DODivision of Molecular and Cellular Biosciences
- ANArgonne National LaboratoryAwards: DE-AC02, DE-AC02-05CH11231, 06CH11357, AC02-06CH11357