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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A 100-fold acceleration of docking was observed by docking a fraction of the chemical library in synchrony with an AI/ML ligand-based prediction of the remaining docking scores.
“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-citation“Selective” serotonin 5-HT2A receptor antagonists
Casey, A. B., Cui, M., Booth, R. G., & Canal, C. E. (2022) · Biochemical Pharmacology · doi:10.1016/j.bcp.2022.115028 · Cited by 94 · see citing works
Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation
Yan, X., Yue, T., Winkler, D. A., Yin, Y., Zhu, H., Jiang, G., & Yan, B. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00070 · Cited by 88 · see citing works
Structure and dynamics in drug discovery
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“38 – 42.”
Targeting small GTPases: emerging grasps on previously untamable targets, pioneered by KRAS
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Organic near-infrared optoelectronic materials and devices: an overview
Che, Z., Yan, C., Wang, X., & Liao, L. (2024) · Advanced Photonics · doi:10.1117/1.ap.6.1.014001 · Cited by 78 · see citing works
Structure-based virtual screening of vast chemical space as a starting point for drug discovery
Carlsson, J., & Luttens, A. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102829 · Cited by 78 · see citing works
Rapid traversal of vast chemical space using machine learning-guided docking screens
Luttens, A., de Vaca, I. C., Sparring, L., Brea, J. M., Martínez, A. L., Kahlous, N. A., Radchenko, D. S., Moroz, Y. S., Loza, M. I., Norinder, U., et al. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00777-x · Cited by 79 · see citing works
“Compared with previously developed methods tackling ultralarge Other methods to explore large chemical libraries via molecular docking use a hierarchical approach 38,39 , which is fundamentally different from the machine learning techniques.”
Self-citationViral proteases as therapeutic targets
Majerová, T., & Konvalinka, J. (2022) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2022.101159 · Cited by 73 · see citing works
Active Learning Guided Drug Design Lead Optimization Based on Relative Binding Free Energy Modeling
Gusev, F., Gutkin, E. M., Kurnikova, M. G., & Isayev, O. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01052 · Cited by 73 · see citing works
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“As rational drug design using crystal and cryo–electron microscopy (cryo-EM) structures of GPCRs has proven to be efficient ( 6 , 8 – 10 ), AlphaFold has sparked interest in exploiting computational models in ligand discovery campaigns for the many receptors of unknown structure.”
Navigating large chemical spaces in early-phase drug discovery
Korn, M., Ehrt, C., Ruggiu, F., Gastreich, M., & Rarey, M. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102578 · Cited by 65 · see citing works
The polypharmacology of psychedelics reveals multiple targets for potential therapeutics
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Self-citationNew insights into GPCR coupling and dimerisation from cryo-EM structures
Gusach, A., García‐Nafría, J., & Tate, C. G. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102574 · Cited by 62 · see citing works
Computational drug development for membrane protein targets
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“Another method called V-SYNTHES can increase the speed of docking by more than 5,000 times by using an iterative docking approach, and the hit rate of this method is twice the rate of traditional virtual screening of large compound libraries 110 .”
The impact of library size and scale of testing on virtual screening
Liu, F., Mailhot, O., Glenn, I., Vigneron, S. F., Bassim, V., Xu, X., Fonseca-Valencia, K., Smith, M. S., Radchenko, D. S., Fraser, J. S., et al. (2025) · Nature Chemical Biology · doi:10.1038/s41589-024-01797-w · Cited by 66 · see citing works
“Fourth, we investigate how hit rate is predicted by docking score, and whether we might expect better molecules to be found as libraries expand into the tens of billions of molecules and beyond 5,21 .”
Self-citationZhang, L., Wang, C.-C., Zhang, Y., & Chen, X. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107512 · Cited by 60 · see citing works
Improving de novo molecular design with curriculum learning
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“Existing methods for molecular design include virtual screening (VS), which continues to be a valuable paradigm in identifying candidate molecules 4,5 .”
MethodThompson Sampling─An Efficient Method for Searching Ultralarge Synthesis on Demand Databases
Klarich, K., Goldman, B. B., Kramer, T. J., Riley, P., & Walters, W. P. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01790 · Cited by 57 · see citing works
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New horizons at the interface of artificial intelligence and translational cancer research
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GPCR systems pharmacology: a different perspective on the development of biased therapeutics
Eiger, D. S., Pham, U., Gardner, J., Hicks, C., & Rajagopal, S. (2022) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00449.2021 · Cited by 51 · see citing works
The structure, function, and pharmacology of MRGPRs
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Self-citationIntelligent Biomaterialomics: Molecular Design, Manufacturing, and Biomedical Applications
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Recent advances from computer-aided drug design to artificial intelligence drug design
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Flexible scaffold-based cheminformatics approach for polypharmacological drug design
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Targeting SARS-CoV-2 papain-like protease in the postvaccine era
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“For this, it will be critical to deploy modern DL-based methods like our Deep Docking platform and other methodologies exploiting GPU and cloud computing such as OpenEye’s Orioni, or the virtual synthon-based approach (V-SYNTHES) [71,72].”
Stanley, M., & Segler, M. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102658 · Cited by 48 · see citing works
Large library docking identifies positive allosteric modulators of the calcium-sensing receptor
Liu, F., Wu, C.-G., Tu, C., Glenn, I., Meyerowitz, J., Levit, A., Lyu, J., Cheng, Z., Tarkhanova, O. O., Moroz, Y. S., et al. (2024) · Science · doi:10.1126/science.ado1868 · Cited by 49 · see citing works
“Although such a comparison merits fur-therstudy,certainlywithmoremoleculesbeing testedtopowerthestatistics,itisconsistent withtheoreticalstudies( 33 ) and supports the continued expansion of readily testable libraries for drug discovery ( 27 , 30 ).”
BackgroundSelf-citationAdvancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
Duran-Frigola, M., Cigler, M., & Winter, G. E. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11098 · Cited by 50 · see citing works
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Fragment-to-Lead Medicinal Chemistry Publications in 2022
Woodhead, A., Erlanson, D. A., de Esch, I. J. P., Holvey, R. S., Jahnke, W., & Pathuri, P. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.3c02070 · Cited by 47 · see citing works
The role of artificial intelligence in drug screening, drug design, and clinical trials
Wu, Y., Ma, L., Li, X., Yang, J., Rao, X., Hu, Y., Xi, J., Tao, L., Wang, J., Du, L., et al. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2024.1459954 · Cited by 48 · see citing works
Artificial Intelligence‐Driven Approaches in Semiconductor Research
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Ligand recognition and allosteric modulation of the human MRGPRX1 receptor
Liu, Y., Cao, C., Huang, X., Gumpper, R. H., Rachman, M. M., Shih, S. L., Krumm, B. E., Zhang, S., Shoichet, B. K., Fay, J. F., et al. (2022) · Nature Chemical Biology · doi:10.1038/s41589-022-01173-6 · Cited by 46 · see citing works
Self-citationStructure-Based Drug Design with a Deep Hierarchical Generative Model
Weller, J. A., & Rohs, R. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01193 · Cited by 47 · see citing works
Relevance of the Trillion-Sized Chemical Space “eXplore” as a Source for Drug Discovery
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Defining Levels of Automated Chemical Design
Goldman, B. B., Kearnes, S. M., Kramer, T. J., Riley, P., & Walters, W. P. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00334 · Cited by 44 · see citing works
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Recent Developments in Ultralarge and Structure-Based Virtual Screening Approaches
Gorgulla, C. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020222-025013 · Cited by 50 · see citing works
Müller, J., Klein, R., Tarkhanova, O. O., Gryniukova, A., Borysko, P., Merkl, S., Ruf, M., Neumann, A., Gastreich, M., Moroz, Y. S., et al. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00813 · Cited by 43 · see citing works
Self-citationLarge library docking for novel SARS‐CoV ‐2 main protease non‐covalent and covalent inhibitors
Fink, E. A., Bardine, C., Gahbauer, S., Singh, I., Detomasi, T. C., White, K. M., Gu, S., Wan, X., Chen, J. S., Ary, B. E., et al. (2023) · Protein Science · doi:10.1002/pro.4712 · Cited by 43 · see citing works
“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 44 · see citing works
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 43 · see citing works
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 46 · see citing works
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 44 · see citing works
“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 43 · see citing works
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 43 · see citing works
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 40 · see citing works
“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 43 · see citing works
“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 42 · see citing works
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 · see citing works
The Science and Art of Structure-Based Virtual Screening
Zhao, H. (2024) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.4c00093 · Cited by 36 · see citing works
Pei, Z. (2024) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2024.102334 · Cited by 36 · see citing works
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 34 · see citing works
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 34 · see citing works
“…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 33 · see citing works
“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 33 · see citing works
AI molecular catalysis: where are we now?
Tan, Z., Yang, Q., & Luo, S. (2025) · Organic Chemistry Frontiers · doi:10.1039/d4qo02363c · Cited by 33 · see citing works
“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 31 · see citing works
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 30 · see citing works
“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 31 · see citing works
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 · see citing works
“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 31 · see citing works
Grasso, D., Galderisi, S., Santucci, A., & Bernini, A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24065819 · Cited by 29 · see citing works
“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 29 · see citing works
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 · see citing works
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 29 · see citing works
“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 29 · see citing works
“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 34 · see citing works
“…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 27 · see citing works
“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 28 · see citing works
Gridnev, A., Maity, S., & Misra, J. R. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1021823 · Cited by 27 · see citing works
“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 27 · see citing works
“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 26 · see citing works
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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