An artificial intelligence accelerated virtual screening platform for drug discovery

被引:14
|
作者
Zhou, Guangfeng [1 ,2 ]
Rusnac, Domnita-Valeria [3 ]
Park, Hahnbeom [4 ,5 ]
Canzani, Daniele [6 ]
Nguyen, Hai Minh [7 ]
Stewart, Lance [2 ]
Bush, Matthew F. [6 ]
Nguyen, Phuong Tran [8 ]
Wulff, Heike [7 ]
Yarov-Yarovoy, Vladimir [8 ,9 ]
Zheng, Ning [3 ]
Dimaio, Frank [1 ,2 ]
机构
[1] Univ Washington, Dept Biochem, Seattle, WA 98195 USA
[2] Univ Washington, Inst Prot Design, Seattle, WA 98195 USA
[3] Univ Washington, Howard Hughes Med Inst, Dept Pharmacol, Seattle, WA 98195 USA
[4] Korea Inst Sci & Technol, Brain Sci Inst, Seoul, South Korea
[5] Sungkyunkwan Univ, SKKU Inst Convergence, KIST SKKU Brain Res Ctr, Suwon, South Korea
[6] Univ Washington, Dept Chem, Seattle, WA USA
[7] Univ Calif Davis, Dept Pharmacol, Davis, CA USA
[8] Univ Calif Davis, Dept Physiol & Membrane Biol, Davis, CA USA
[9] Univ Calif Davis, Dept Anesthesiol & Pain Med, Sacramento, CA USA
基金
美国国家卫生研究院; 美国国家科学基金会; 新加坡国家研究基金会;
关键词
FAST INACTIVATION; ACCURATE DOCKING; POSE PREDICTION; SMALL MOLECULES; SODIUM-CHANNEL; DOMAIN; PROTEIN; BINDING; GLIDE; OPTIMIZATION;
D O I
10.1038/s41467-024-52061-7
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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. The authors in this work introduce RosettaVS, an AI-accelerated open-source drug discovery platform. They apply this tool to multi-billion compound libraries, where it was able to identify compounds that bind important targets KLHDC2 and NaV1.7.
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页数:14
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