Cited by 418Computational Drug Discovery MethodsChemical Synthesis and AnalysisSynthesis and Characterization of Heterocyclic Compounds

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Synthon-based ligand discovery in virtual libraries of over 11 billion compounds

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Sadybekov, Arman A., Sadybekov, Anastasiia V., Liu, Yongfeng, Iliopoulos-Tsoutsouvas, Christos, Huang, Xi-Ping, Pickett, Julie, Houser, Blake, Patel, Nilkanth, Tran, Ngan K., Tong, Fei, Zvonok, Nikolai, Jain, Manish K., Savych, Olena, Radchenko, Dmytro S., Nikas, Spyros P., Petasis, Nicos A., Moroz, Yurii S., Roth, Bryan L., Makriyannis, Alexandros, Katritch, Vsevolod·2022·Nature

DOI 10.1038/s41586-021-04220-9PMC PMC9763054

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Abstract

Structure-based virtual ligand screening is emerging as a key paradigm for early drug discovery owing to the availability of high-resolution target structures 1-4 and ultra-large libraries of virtual compounds 5,6. However, to keep pace with the rapid growth of virtual libraries, such as readily available for synthesis (REAL) combinatorial libraries 7, new approaches to compound screening are needed 8,9. Here we introduce a modular synthon-based approach-V-SYNTHES-to perform hierarchical structure-based screening of a REAL Space library of more than 11 billion compounds. V-SYNTHES first identifies the best scaffold-synthon combinations as seeds suitable for further growth, and then iteratively elaborates these seeds to select complete molecules with the best docking scores. This hierarchical combinatorial approach enables the rapid detection of the best-scoring compounds in the gigascale chemical space while performing docking of only a small fraction (<0.1%) of the library compounds. Chemical synthesis and experimental testing of novel cannabinoid antagonists predicted by V-SYNTHES demonstrated a 33% hit rate, including 14 submicromolar ligands, substantially improving over a standard virtual screening of the Enamine REAL diversity subset, which required approximately 100 times more computational resources. Synthesis of selected analogues of the best hits further improved potencies and affinities (best inhibitory constant (K i) = 0.9 nM) and CB 2/CB 1 selectivity (50-200-fold). V-SYNTHES was also tested on a kinase target, ROCK1, further supporting its use for lead discovery. The approach is easily scalable for the rapid growth of combinatorial libraries and potentially adaptable to any docking algorithm.

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Background
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Authors: Sadybekov, A. A., Sadybekov, A. V., Liu, Y., Iliopoulos-Tsoutsouvas, C., Huang, X.-P., Pickett, J., Houser, B., Patel, N., Tran, N. K., Tong, F., Zvonok, N., Jain, M. K., Savych, O., Radchenko, D. S., Nikas, S. P., Petasis, N. A., Moroz, Y. S., Roth, B. L., Makriyannis, A., & Katritch, V.
Affiliations: University of Southern California (US)
University of North Carolina at Chapel Hill (US)
Northeastern University (US)
Enamine (Ukraine) (UA)
Taras Shevchenko National University of Kyiv (UA)
Corresponding author: Roth, Bryan L.
Makriyannis, Alexandros
Katritch, Vsevolod
ORCID: Sadybekov, Arman A.
Sadybekov, Anastasiia V.
Liu, Yongfeng
Huang, Xi-Ping
Pickett, Julie
Patel, Nilkanth
Tran, Ngan K.
Zvonok, Nikolai
Jain, Manish K.
Savych, Olena
Radchenko, Dmytro S.
Nikas, Spyros P.
Petasis, Nicos A.
Moroz, Yurii S.
Roth, Bryan L.
Makriyannis, Alexandros
Katritch, Vsevolod
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2021-12-15
Volume: 601
Issue: 7893
Pages: 452-459
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/s41586-021-04220-9
PMC: PMC9763054
OpenAlex: W4200233821
Cited by: 417
References: 43
Citations by year: 2026: 65 · 2025: 96 · 2024: 119 · 2023: 88 · 2022: 46 · 2021: 2
FWCI: 33.74
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
RePEc: Research Papers in Economics (submittedVersion)
UNC Libraries
Funders: National Institute of Mental Health (R01MH112205)
National Institute on Drug Abuse (R01DA041435, R01DA045020, R37DA045657)
National Institute of General Medical Sciences (GM118289, T32GM118289)
National Institute of Diabetes and Digestive and Kidney Diseases (U24DK116195)
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
Keywords: structure-based virtual screening, cannabinoid receptor antagonists, cannabinoid receptor ligands, lead compound identification
MeSH terms: Algorithms, Ligands, Combinatorial Chemistry Techniques, Libraries, Digital, rho-Associated Kinases, Drug Discovery, Molecular Docking Simulation

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Synthon-based ligand discovery in virtual libraries of over 11 billion compounds

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