The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr777 · Cited by 4,382
Baell, J. B., & Holloway, G. A. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm901137j · Cited by 4,196 · cited 3×
“The remaining compounds were filtered with PAINS score, drug-likeness properties, chemical diversity as well as ligand interaction diversity to sample different binding modes in the pocket.”
InfluentialStructure-Function of the G Protein–Coupled Receptor Superfamily
Katritch, V., Cherezov, V., & Stevens, R. C. (2012) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-032112-135923 · Cited by 1,096
Self-citationUltra-large library docking for discovering new chemotypes
Lyu, J., Wang, S., Balius, T. E., Singh, I., Levit, A., Moroz, Y. S., O’Meara, M. J., Che, T., Algaa, E., Tolmachova, K., et al. (2019) · Nature · doi:10.1038/s41586-019-0917-9 · Cited by 998 · cited 2×
“Recently, ultra-large libraries of more than 100 million readily accessible (REAL) compounds have been developed and used in docking-based VLS, yielding high-quality hits for lead discovery 5,6 .”
MethodInfluentialSelf-citationComprehensive assay of kinase catalytic activity reveals features of kinase inhibitor selectivity
Anastassiadis, T., Deacon, S. W., Devarajan, K., Ma, H., & Peterson, J. R. (2011) · Nature Biotechnology · doi:10.1038/nbt.2017 · Cited by 919 · cited 1×
“The HotSpot radiometric assay (Reaction Biology Corporation) measures inhibition of ROCK1 catalytic activity towards a specific peptide substrate (KEAKEKRQEQIAKRRRLSSLRASTSKSGGSQK), which is monitored by P81 filter-binding methods 45 .”
MethodPRESTO-Tango as an open-source resource for interrogation of the druggable human GPCRome
Kroeze, W. K., Sassano, M. F., Huang, X.-P., Lansu, K., McCorvy, J. D., Giguère, P. M., Sciaky, N., & Roth, B. L. (2015) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.3014 · Cited by 755 · cited 4×
“The Tango arrestin recruitment assays were performed as previously described 23 .”
MethodInfluentialSelf-citationCannabinoids: potential anticancer agents
Guzmán, M. (2003) · Nature reviews. Cancer · doi:10.1038/nrc1188 · Cited by 721 · cited 1×
“The approach is applied here to cannabinoid receptors, which are class A G-protein-coupled receptors (GPCRs), and are key targets in drug discovery for inflammatory disorders, neurodegenerative diseases and cancer 17–19 .”
BackgroundCrystal Structure of the Human Cannabinoid Receptor CB1
Hua, T., Vemuri, K. V., Pu, M., Qu, L., Han, G. W., Wu, Y., Zhao, S., Shui, W., Li, S., Korde, A., et al. (2016) · Cell · doi:10.1016/j.cell.2016.10.004 · Cited by 702
Self-citationAn open-source drug discovery platform enables ultra-large virtual screens
Gorgulla, C., Boeszoermenyi, A., Wang, Z., Fischer, P. D., Coote, P. W., Das, K. M., Malets, Y. S., Radchenko, D. S., Moroz, Y. S., Scott, D. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2117-z · Cited by 677 · cited 2×
“Two binding modes for the CB 2 receptor binding pocket were prepared: one guided by 20 known antagonists and another by 20 agonists, selected from ChEMBL high-affinity ligands for CB 2 (CHEMBL253, affinity p Kd > 8).”
MethodInfluentialSelf-citationCryo-EM in drug discovery: achievements, limitations and prospects
Renaud, J., Chari, A., Ciferri, C., Liu, W.-T., Rémigy, H., Stark, H., & Wiesmann, C. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2018.77 · Cited by 473 · cited 1×
“The affinities ( K i ) of the new compounds for rat CB 1 receptors and human CB 2 receptors were obtained using membrane preparations from rat brain or HEK293 cells, respectively, and [ 3 H]CP-55,940 as the radioligand, as previously described 42,43 .”
MethodGeneral methods for the construction of complex molecules
Corey, E. J. (1967) · Pure and Applied Chemistry · doi:10.1351/pac196714010019 · Cited by 470 · cited 1×
“Each reaction/scaffold in the library is presented in the form of a Markush scheme with two or more R groups representing synthons 7,20 .”
MethodCrystal Structure of the Human Cannabinoid Receptor CB2
Li, X., Hua, T., Vemuri, K. V., Ho, J., Wu, Y., Wu, L., Popov, P. A., Benchama, O., Zvonok, N., Locke, K., et al. (2019) · Cell · doi:10.1016/j.cell.2018.12.011 · Cited by 410 · cited 2×
“Both V-SYNTHES and standard VLS used a structural model based on the CB 2 R crystal structure with an antagonist AM10257 at a resolution of 2.8 Å (PDB: 5ZTY) 22 .”
MethodInfluentialSelf-citationImpact of GPCR Structures on Drug Discovery
Congreve, M., de Graaf, C., Swain, N. A., & Tate, C. G. (2020) · Cell · doi:10.1016/j.cell.2020.03.003 · Cited by 393
High-throughput docking for lead generation
Abagyan, R. A., & Totrov, M. M. (2001) · Current Opinion in Chemical Biology · doi:10.1016/s1367-5931(00)00217-9 · Cited by 347
Accelerating high-throughput virtual screening through molecular pool-based active learning
Graff, D., Shakhnovich, E. I., & Coley, C. W. (2021) · Chemical Science · doi:10.1039/d0sc06805e · Cited by 331 · cited 1×
“Recently, an iteration of docking and machine learning steps 9 , or step-wise filtering of the whole enumerated library using docking algorithms of increasing accuracy 8 , were suggested to tackle ultra-large libraries of 138 million and 1.4 billion compounds, respectively.”
MethodInfluentialVirtual discovery of melatonin receptor ligands to modulate circadian rhythms
Stein, R. M., Kang, H. J., McCorvy, J. D., Glatfelter, G. C., Jones, A., Che, T., Slocum, S. T., Huang, X., Savych, O., Moroz, Y. S., et al. (2020) · Nature · doi:10.1038/s41586-020-2027-0 · Cited by 312 · cited 1×
“Recently, ultra-large libraries of more than 100 million readily accessible (REAL) compounds have been developed and used in docking-based VLS, yielding high-quality hits for lead discovery 5,6 .”
MethodSelf-citationStructure-based drug screening for G-protein-coupled receptors
Shoichet, B. K., & Kobilka, B. K. (2012) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2012.03.007 · Cited by 306
Ertl, P. (2002) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0255782 · Cited by 294 · cited 1×
“Standard libraries for high-throughput screening (HTS) 10 and virtual ligand screening (VLS) 11–13 have been historically limited to fewer than 10 million available compounds, which is a small fraction of the enormous chemical space, estimated to be 10 20 to 10 60 drug-like compounds 14,15 .”
BackgroundDocking Screens for Novel Ligands Conferring New Biology
Irwin, J. J., & Shoichet, B. K. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b02008 · Cited by 284 · cited 1×
“Standard libraries for high-throughput screening (HTS) 10 and virtual ligand screening (VLS) 11–13 have been historically limited to fewer than 10 million available compounds, which is a small fraction of the enormous chemical space, estimated to be 10 20 to 10 60 drug-like compounds 14,15 .”
BackgroundThe Structure of Dimeric ROCK I Reveals the Mechanism for Ligand Selectivity
Jacobs, M. D., Hayakawa, K., Swenson, L. L., Bellon, S. F., Fleming, M. A., Taslimi, P. M., & Doran, J. (2005) · Journal of Biological Chemistry · doi:10.1074/jbc.m508847200 · Cited by 266 · cited 1×
“The MEL library was docked into the ROCK1 crystal structure (PDB: 2ETR) 44 prepared in ICM-Pro.”
MethodMolecular docking and ligand specificity in fragment-based inhibitor discovery
Chen, Y., & Shoichet, B. K. (2009) · Nature Chemical Biology · doi:10.1038/nchembio.155 · Cited by 261
Structure-Based Discovery of Novel Chemotypes for Adenosine A2AReceptor Antagonists
Katritch, V., Jaakola, V., Lane, J. R., Lin, J., IJzerman, A. P., Yeager, M. J., Kufareva, I., Stevens, R. C., & Abagyan, R. A. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm901647p · Cited by 247
Self-citationCryo-EM Structure of the Human Cannabinoid Receptor CB2-Gi Signaling Complex
Xing, C., Zhuang, Y., Xu, T.-H., Feng, Z., Zhou, X. E., Chen, M., Wang, L., Meng, X. M., Xue, Y., Wang, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.007 · Cited by 247
Generating Multibillion Chemical Space of Readily Accessible Screening Compounds
Grygorenko, O. O., Radchenko, D. S., Dziuba, I., Chuprina, A., Gubina, K. E., & Moroz, Y. S. (2020) · iScience · doi:10.1016/j.isci.2020.101681 · Cited by 238 · cited 1×
“Each reaction/scaffold in the library is presented in the form of a Markush scheme with two or more R groups representing synthons 7,20 .”
MethodSelf-citationFragment-based lead discovery grows up
Baker, M. (2012) · Nature Reviews Drug Discovery · doi:10.1038/nrd3926 · Cited by 223 · cited 2×
“Similarly, application of V-SYNTHES to the kinase target ROCK1 yielded a 28.5% hit rate, including ligands with nanomolar affinity and potency.”
MethodInfluentialde Graaf, C., Kooistra, A. J., Vischer, H. F., Katritch, V., Kuijer, M., Shiroishi, M., Iwata, S., Shimamura, T., Stevens, R. C., de Esch, I. J. P., et al. (2011) · Journal of Medicinal Chemistry · doi:10.1021/jm2011589 · Cited by 212
Self-citationNovel Insights into the Roles of Rho Kinase in Cancer
Wei, L., Surma, M. L., Shi, S., Lambert-Cheatham, N. A., & Shi, J. (2016) · Archivum Immunologiae et Therapiae Experimentalis · doi:10.1007/s00005-015-0382-6 · Cited by 206 · cited 1×
“To assess the more broad applicability of the V-SYNTHES approach, we tested its performance on the Rho-associated coiled-coil containing protein kinase 1 (ROCK1), which is an important and challenging target in cancer drug discovery 26,27 .”
MethodBottegoni, G., Kufareva, I., Totrov, M. M., & Abagyan, R. A. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm8009958 · Cited by 179 · cited 1×
“The two LiBERO models, along with the crystal structure model, were combined into one 4D model as described previously 40 .”
MethodStructure-Based Virtual Ligand Screening: Recent Success Stories
Villoutreix, B. O., Eudes, R., & Miteva, M. A. (2009) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/138620709789824682 · Cited by 139 · cited 1×
“Standard libraries for high-throughput screening (HTS) 10 and virtual ligand screening (VLS) 11–13 have been historically limited to fewer than 10 million available compounds, which is a small fraction of the enormous chemical space, estimated to be 10 20 to 10 60 drug-like compounds 14,15 .”
BackgroundRecent progress in fragment-based lead discovery
Schulz, M. N., & Hubbard, R. E. (2009) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2009.04.009 · Cited by 136
Biology and therapeutic potential of cannabinoid CB2receptor inverse agonists
Lunn, C. A., Reich, E., Fine, J. S., Lavey, B. J., Kozlowski, J. A., Hipkin, R. W., Lundell, D. J., & Bober, L. (2007) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0707480 · Cited by 120 · cited 1×
“The approach is applied here to cannabinoid receptors, which are class A G-protein-coupled receptors (GPCRs), and are key targets in drug discovery for inflammatory disorders, neurodegenerative diseases and cancer 17–19 .”
BackgroundLigand and Decoy Sets for Docking to G Protein-Coupled Receptors
Gatica, E. A., & Cavasotto, C. N. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200412p · Cited by 111 · cited 1×
“These compounds, along with 200 decoy molecules that were selected from the CB 2 receptor decoy database (GDD) 39 were docked into the refined conformers.”
MethodChin, V. T., Nagrial, A. M., Chou, A. S. Y., Biankin, A. V., Gill, A. J., Timpson, P., & Pajic, M. (2015) · Expert Reviews in Molecular Medicine · doi:10.1017/erm.2015.17 · Cited by 71 · cited 1×
“To assess the more broad applicability of the V-SYNTHES approach, we tested its performance on the Rho-associated coiled-coil containing protein kinase 1 (ROCK1), which is an important and challenging target in cancer drug discovery 26,27 .”
MethodNikas, S. P., Alapafuja, S. O., Papanastasiou, I. P., Paronis, C. A., Shukla, V. G., Papahatjis, D. P., Bowman, A. L., Aneetha, H., Han, X., & Makriyannis, A. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm100641g · Cited by 59 · cited 1×
“The affinities ( K i ) of the new compounds for rat CB 1 receptors and human CB 2 receptors were obtained using membrane preparations from rat brain or HEK293 cells, respectively, and [ 3 H]CP-55,940 as the radioligand, as previously described 42,43 .”
MethodSelf-citationZheng, Z., Huang, X., Mangano, T. J., Zou, R., Chen, X., Zaidi, S. A., Roth, B. L., Stevens, R. C., & Katritch, V. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.7b00109 · Cited by 59
Self-citationSmart screening: approaches to efficient HTS.
Engels, M. F., & Venkatarangan, P. (2001) · PubMed · Cited by 56
Ligand-guided receptor optimization.
Katritch, V., Rueda, M., & Abagyan, R. (2012) · Methods in molecular biology · doi:10.1007/978-1-61779-588-6_8 · Cited by 54 · cited 1×
“These refined models were generated in a ligand-guided receptor optimization procedure (LiBERO) 38 , which refined the sidechains and water molecules within the 8 Å radius from the orthosteric binding pocket.”
MethodSelf-citationProbing the Carboxyester Side Chain in Controlled Deactivation (−)-Δ8-Tetrahydrocannabinols
Nikas, S. P., Sharma, R., Paronis, C. A., Kulkarni, S., Thakur, G. A., Hurst, D. P., Wood, J. T., Gifford, R. S., Rajarshi, G., Liu, Y., et al. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm501165d · Cited by 31 · cited 1×
“The affinities ( K i ) of the new compounds for rat CB 1 receptors and human CB 2 receptors were obtained using membrane preparations from rat brain or HEK293 cells, respectively, and [ 3 H]CP-55,940 as the radioligand, as previously described 42,43 .”
MethodSelf-citationContino, M., Capparelli, E., Colabufo, N. A., & Bush, A. I. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00196 · Cited by 31
A One-Pot Parallel Reductive Amination of Aldehydes with Heteroaromatic Amines
Bogolubsky, A. V., Moroz, Y. S., Mykhailiuk, P. K., Panov, D. M., Pipko, S. E., Konovets, A. I., & Tolmachev, A. A. (2014) · ACS Combinatorial Science · doi:10.1021/co5000568 · Cited by 21 · cited 1×
“An example of a two-reagent reaction is the one-pot reductive amination of aldehydes with heter-oaromatic amines 36 , as shown in Extended Data Fig.”
MethodSelf-citationOne-Pot Parallel Synthesis of 5-(Dialkylamino)tetrazoles
Savych, O., Kuchkovska, Y. O., Bogolyubsky, A. V., Konovets, A. I., Gubina, K. E., Pipko, S. E., Zhemera, A. V., Grishchenko, A. V., Khomenko, D. M., Броварец, В. С., et al. (2019) · ACS Combinatorial Science · doi:10.1021/acscombsci.9b00120 · Cited by 13 · cited 1×
“An example of a three-reagent reaction is the one-pot formation of thiazoles through asymmetrical thioureas 37 , shown in Extended Data Fig.”
MethodSelf-citationFragment‐Based Ligand Discovery
Davis, B. J., & Hubbard, R. E. (2020) · Wiley eBooks · doi:10.1002/9781118681121.ch4 · Cited by 6
Cited by
Sorted by citations
Scientific discovery in the age of artificial intelligence.
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823
Computational approaches streamlining drug discovery
Sadybekov, A., & Katritch, V. (2023) · Nature · doi:10.1038/s41586-023-05905-z · Cited by 1,424
“The recently developed virtual synthon hierarchical enumeration screening (V-SYNTHES) 26 technology applies fragment-based design to on-demand chemical spaces, thus avoiding the challenges of custom synthesis (Fig.”
BackgroundMethodInfluentialSelf-citationSelf-Driving Laboratories for Chemistry and Materials Science
Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707
The Art and Science of Molecular Docking
Paggi, J. M., Pandit, A., & Dror, R. O. (2024) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-030222-120000 · Cited by 546
The neural basis of psychedelic action
Kwan, A. C., Olson, D. E., Preller, K. H., & Roth, B. L. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01177-4 · Cited by 330
Self-citation
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
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 ›
Are you an author, or do you see a problem with this page? Request a review
