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Synthon-based ligand discovery in virtual libraries of over 11 billion compounds
Open accessAbstract
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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“For example, a 100-fold acceleration of the process was observed by docking a fraction of the chemical library in synchrony with an AI/ML ligand-based prediction of the remaining docking scores.144”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [144]
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Self-citationLarge library docking for novel SARS‐CoV ‐2 main protease non‐covalent and covalent inhibitors
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“Finally, substitutions at Q192 have been found to confer resistance to nirmatrelvir, including Q192T (Hu et al., 2022; Sasi et al., 2022) ( n = 227 occurrences) and Q192R (Heilmann et al., 2023) ( n = 40 occurrences), though Q192 Δ is the most prevalent ( n = 1,304 occurrences, nearly all Omicron).”
Self-citationSivula, T., Yetukuri, L., Kalliokoski, T., Käsnänen, H., Poso, A., & Pöhner, I. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01239 · Cited by 47
Current Status of Computational Approaches for Small Molecule Drug Discovery
Xu, W. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c02462 · Cited by 49
Modeling protein–ligand interactions for drug discovery in the era of deep learning
Wang, Y. X., Li, Y., Chen, J., & Lai, L. (2025) · Chemical Society Reviews · doi:10.1039/d5cs00415b · Cited by 53
Tang, S., Ding, J., Zhu, X., Wang, Z., Zhao, H., & Wu, J. (2024) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2024.3467127 · Cited by 49
“In virtual screening, the identification of promising compounds stands as the primary objective, and enhancing screening accuracy or minimizing the loss of best-hit compounds is of paramount importance [8].”
BackgroundGenerative AI for navigating synthesizable chemical space
Gao, W., Luo, S., & Coley, C. W. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2415665122 · Cited by 94
Docking for EP4R antagonists active against inflammatory pain
Gahbauer, S., DeLeon, C., Braz, J. M., Craik, V., Kang, H. J., Wan, X., Huang, X., Billesbølle, C. B., Liu, Y., Che, T., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43506-6 · Cited by 44
Self-citationYuan, Y., Yu, L.-L., Bi, C., Huang, L., Su, B., Nie, J., Dou, Z., Yang, S., & Li, Y. (2025) · Chinese Medicine · doi:10.1186/s13020-025-01075-4 · Cited by 42
“Among them, RBVS, also known as structure based virtual screening (SBVS), rapidly and effectively searches and discovers bioactive molecules in a large virtual compound library based on protein crystal structure or homology modeling [50, 51].”
Keeping pace with the explosive growth of chemical libraries with structure‐based virtual screening
Kuan, J., Radaeva, M., Avenido, A., Cherkasov, A. R., & Gentile, F. (2023) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1678 · Cited by 44
“42 These fragments are then docked into the target pocket and the top scoring fragments get consequently expanded into full compounds, providing an efficient way to significantly reduce the amount of docking.”
BackgroundMethodStructure-based discovery of CFTR potentiators and inhibitors
Liu, F., Kaplan, A. L., Levring, J., Einsiedel, J., Tiedt, S., Distler, K., Omattage, N. S., Kondratov, I. S., Moroz, Y. S., Pietz, H. L., et al. (2024) · Cell · doi:10.1016/j.cell.2024.04.046 · Cited by 45
Self-citationGenome-wide pan-GPCR cell libraries accelerate drug discovery
Yang, H., Wang, Y., Liu, W., He, T., Liao, J., Qian, Z., Zhao, J., Cong, Z., Sun, D., Liu, Z., et al. (2024) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2024.06.023 · Cited by 37
The Science and Art of Structure-Based Virtual Screening
Zhao, H. (2024) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.4c00093 · Cited by 41
Pei, Z. (2024) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2024.102334 · Cited by 37
Gryniukova, A., Kaiser, F., Myziuk, I., Alieksieieva, D. O., Leberecht, C., Heym, P. P., Tarkhanova, O. O., Moroz, Y. S., Borysko, P., & Haupt, V. J. (2023) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.3c00128 · Cited by 36
Self-citationWang, M., Li, S., Wang, J., Zhang, O., Du, H., Jiang, D., Wu, Z., Deng, Y., Kang, Y., Pan, P., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54456-y · Cited by 42
“…combinatorial chemistry techniques, they suffers from a few notable limitations 32 : (1) Dif fi culty in actually synthesizing generated molecules 33,34 : While these combinatorial-based generative methods suggest how to piece together a molecule via reaction rules, they often do not account for…”
DART.2: bidirectional synaptic pharmacology with thousandfold cellular specificity
Shields, B. C., Yan, H., Lim, S. S. X., Burwell, S., Cammarata, C. M., Fleming, E. A., Yousefzadeh, S. A., Goldenshtein, V. Z., Kahuno, E. W., Vagadia, P. P., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02292-9 · Cited by 34
“Using a synthon-based approach 15 , we designed a series of HTL candidates that combine features from two structurally distinct ligands known to have affinity for exterior 16 and interior regions of HTP 17 .”
Self-Focusing Virtual Screening with Active Design Space Pruning
Graff, D., Aldeghi, M., Morrone, J. A., Jordan, K. E., Pyzer‐Knapp, E. O., & Coley, C. W. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00554 · Cited by 37
AI molecular catalysis: where are we now?
Tan, Z., Yang, Q., & Luo, S. (2025) · Organic Chemistry Frontiers · doi:10.1039/d4qo02363c · Cited by 36
“74,75 Given the structural parallels, organocatalysis, which similarly relies on “ pocket ” architectures, stands to benefit greatly from AI-driven strategies.”
Large-Scale Docking in the Cloud
Tingle, B. I., & Irwin, J. J. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00031 · Cited by 33
A divergent intermediate strategy yields biologically diverse pseudo-natural products
Bag, S., Liu, J., Patil, S., Bonowski, J., Koska, S., Schölermann, B., Zhang, R., Wang, L., Pahl, A., Sievers, S., et al. (2024) · Nature Chemistry · doi:10.1038/s41557-024-01458-4 · Cited by 31
“Recently, virtual screenings of ultra-large make on-demand compound collections have been used to expand the exploration of biologically relevant chemical space 81,82 .”
Yang, K., Xie, Z., Li, Z., Qian, X., Sun, N., He, T., Xu, Z., Jiang, J., Mei, Q., Wang, J., et al. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01979 · Cited by 32
Graph neural networks driven acceleration in drug discovery
Wang, R., & Zhuang, C. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.10.011 · Cited by 33
“Comparing with the chemical space that containing as many as 10 60 drug-like molecules that adhere to Lipinski’s rule of five, there is a large blank 5,6 .”
Assessing Boltz-2 Performance for the Binding Classification of Docking Hits
Bret, G., Sindt, F., & Rognan, D. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02630 · Cited by 39
Grasso, D., Galderisi, S., Santucci, A., & Bernini, A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24065819 · Cited by 31
“A synthon-based approach, working in tight conjunction with REAL libraries [161], has been proposed in V-SYNTHES [209].”
MethodPopov, K. I., Wellnitz, J., Maxfield, T., & Tropsha, A. (2023) · Molecular Informatics · doi:10.1002/minf.202300207 · Cited by 31
Discovery of Crystallizable Organic Semiconductors with Machine Learning
Johnson, H. M., Gusev, F., Dull, J. T., Seo, Y., Priestley, R. D., Isayev, O., & Rand, B. P. (2024) · Journal of the American Chemical Society · doi:10.1021/jacs.4c05245 · Cited by 29
PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening
Seo, S., & Kim, W. Y. (2024) · Chemical Science · doi:10.1039/d4sc04854g · Cited by 38
“Recent work reported that increasing the chemical library size from 115 million to 11 billion significantly improved the hit rate from 15% to 30% [6].”
BackgroundProtein language models are performant in structure-free virtual screening
Lam, H. Y. I., Guan, J. S., Ong, X. E., Pincket, R., & Mu, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae480 · Cited by 31
“Previous work has delivered very accurate DTA predictors using PLMs such as SSM-DTA [16].”
MethodThe changing landscape of medicinal chemistry optimization
Rácz, A., Mihalovits, L. M., Beckers, M., Fechner, N., Stiefl, N., Sirockin, F., McCoull, W., Evertsson, E., Lemurell, M., Makara, G. M., et al. (2025) · Nature Reviews Drug Discovery · doi:10.1038/s41573-025-01225-1 · Cited by 42
“…or cryo-electron microscopy, high-performance computing capacity, in silico tools that enable virtual screening of billions of compounds 24,25 and the continuously growing commercial libraries (such as Enamine’s REAL 26 and WuXi AppTec GalaXi) and proprietary virtual libraries in recent…”
EasyDock: customizable and scalable docking tool
Minibaeva, G., Ivanová, A., & Полищук, П. Г. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00772-2 · Cited by 34
“These approaches have demonstrated high efficacy in hit discovery, particularly in libraries containing over 10 10 compounds [10].”
BackgroundAugmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning
Guo, J., & Schwaller, P. (2024) · JACS Au · doi:10.1021/jacsau.4c00066 · Cited by 32
Gridnev, A., Maity, S., & Misra, J. R. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1021823 · Cited by 30
“Recent developments in structure-based virtual ligand screening (VLS) allows one to conduct in silico screening of large libraries of small molecules to predict the ones, which have high probability of binding to a given site in a target protein (36-38).”
BackgroundAdvances in the discovery of new chemotypes through ultra-large library docking
Potlitz, F., Link, A., & Schulig, L. (2023) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2023.2171984 · Cited by 30
“To utilize this potential successfully, the development of new techniques for computer-aided drug design and efficient use of the available computing hardware are necessary [98,99].”
MethodIn silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations
Gutkin, E. M., Gusev, F., Gentile, F., Ban, F., Koby, S. B., Narangoda, C., Isayev, O., Cherkasov, A. R., & Kurnikova, M. G. (2024) · Chemical Science · doi:10.1039/d3sc06880c · Cited by 28
Metadata
- 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
- Venue
- Nature
- Published
- 2021-12-15
- Volume
- 601
- Issue
- 7893
- Pages
- 452-459
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- 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
- 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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