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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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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 .”
Method
Cited by
Sorted by citations
Items 101–200 of 418
Correy, G. J., Rachman, M. M., Togo, T., Gahbauer, S., Doruk, Y. U., Stevens, M. G., Jaishankar, P., Kelley, B. P., Goldman, B. B., Schmidt, M., et al. (2025) · Science Advances · doi:10.1126/sciadv.ads7187 · Cited by 26
“Similar approaches have searched ultralarge libraries by computationally docking individual building blocks and only expanding those with the highest scores ( 10 , 32 ).”
Self-citationYu, L., He, X. D., Fang, X., Liu, L., & Liu, J. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01371 · Cited by 26
Linkers in fragment-based drug design: an overview of the literature
Grenier, D., Audebert, S., Preto, J., Guichou, J., & Krimm, I. (2023) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2023.2234285 · Cited by 26
“This is the case of the recent virtual synthon hierarchical enumeration screening (V-SYNTHES) approach [96] which allows the screening of the Enamine REAL Space [97] (11 billion compounds) in a reasonable amount time, taking advantage of the building-block architecture of the library.”
MethodRegression-Based Active Learning for Accessible Acceleration of Ultra-Large Library Docking
Marin, E., Kovaleva, M. I., Kadukova, M., Mustafin, K., Khorn, P. A., Rogachev, A. V., Mishin, A., Guskov, A., & Borshchevskiy, V. I. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01661 · Cited by 25
SARS-CoV2 billion-compound docking
Rogers, D. O. W., Agarwal, R., Vermaas, J. V., Smith, M. D., Tatikonda, R. R., Cooper, C. J., Sedova, A., Boehm, S., Baker, M. B., Gläser, J., et al. (2023) · Scientific Data · doi:10.1038/s41597-023-01984-9 · Cited by 25
How to Find a Fragment: Methods for Screening and Validation in Fragment‐Based Drug Discovery
Kirkman, T., Silva, C. D. S., Tosin, M., & Dias, M. V. B. (2024) · ChemMedChem · doi:10.1002/cmdc.202400342 · Cited by 29
Lam, J. H., & Katritch, V. (2025) · npj Drug Discovery. · doi:10.1038/s44386-025-00031-4 · Cited by 30
“This allows on-demand spaces to explore billions and trillions of compounds, with >80% success rates in synthesis, competitive prices, and delivery in 4-5 weeks, underscoring their utility in real-world drug discovery campaigns 5,11,242 .”
Self-citationGrotsch, K., Sadybekov, A., Hiller, S., Zaidi, S. A., Eremin, D. B., Le, A. D., Liu, Y., Smith, E. C., Iliopoulos‐Tsoutsouvas, C., Thomas, J., et al. (2024) · ACS Chemical Biology · doi:10.1021/acschembio.3c00602 · Cited by 27
Self-citationLarge-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening
Cao, Z., Sciabola, S., & Wang, Y. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01938 · Cited by 28
Deepak, R. N. V. K., Verma, R. K., Hartono, Y. D., Yew, W. S., & Fan, H. (2021) · Pharmaceuticals · doi:10.3390/ph15010012 · Cited by 24
Michino, M., Beautrait, A., Boyles, N. A., Nadupalli, A., Dementiev, A., Sun, S., Ginn, J. D., Baxt, L. A., Suto, R. K., Bryk, R., et al. (2023) · ACS Bio & Med Chem Au · doi:10.1021/acsbiomedchemau.3c00046 · Cited by 24
A flexible data-free framework for structure-based de novo drug design with reinforcement learning
Du, H., Jiang, D., Zhang, O., Wu, Z., Gao, J., Zhang, X., Wang, X., Deng, Y., Kang, Y., Li, D., et al. (2023) · Chemical Science · doi:10.1039/d3sc04091g · Cited by 24
GraphsformerCPI: Graph Transformer for Compound–Protein Interaction Prediction
Ma, J., Zhao, Z., Li, T., Liu, Y., Ma, J., & Zhang, R. (2024) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-024-00609-y · Cited by 24
Self-citationA Database for Large-Scale Docking and Experimental Results
Hall, B. W., Tummino, T. A., Tang, K. G., Mailhot, O., Castanon, M., Irwin, J. J., & Shoichet, B. K. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00394 · Cited by 27
AI meets physics in computational structure-based drug discovery for GPCRs
Michino, M., Vendôme, J., & Kufareva, I. (2025) · npj Drug Discovery. · doi:10.1038/s44386-025-00019-0 · Cited by 27
Gou, R., Yang, J., Guo, M., Chen, Y., & Xue, W. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00504 · Cited by 21
Molecular dynamics-driven drug discovery
Yan, D., Ma, Y., Chen, X., Shi, D., & Wang, Q. (2025) · Physical Chemistry Chemical Physics · doi:10.1039/d5cp00380f · Cited by 22
Wang, J., Lou, C., Liu, G., Li, W., Wu, Z., & Tang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac351 · Cited by 20
Identifying Artifacts from Large Library Docking
Wu, Y., Liu, F., Glenn, I., Fonseca-Valencia, K., Paris, L., Xiong, Y., Jerome, S. V., Brooks, C. L., & Shoichet, B. K. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c01632 · Cited by 23
Cheng, A., Ser, C. T., Skreta, M., Guzmán-Cordero, A., Thiede, L., Burger, A., Aldossary, A., Leong, S. X., Pablo‐García, S., Strieth‐Kalthoff, F., et al. (2024) · Faraday Discussions · doi:10.1039/d4fd00153b · Cited by 22
Hugo, C., Asante, I., Sadybekov, A., Katritch, V., & Yassine, H. N. (2024) · Antioxidants and Redox Signaling · doi:10.1089/ars.2024.0794 · Cited by 18
Self-citationVirtual fragment screening for DNA repair inhibitors in vast chemical space
Luttens, A., Vo, D. D., Scaletti, E. R., Wiita, E., Almlöf, I., Wallner, O. A., Davies, J. R., Košenina, S. P., Meng, L., Long, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56893-9 · Cited by 21
“The rapid growth of make-on-demand chemical libraries has stimulated the development of several novel virtual screening strategies 20,52 – 55 .”
Self-citationRecent advances in the screening methods of NPC1L1 inhibitors
Zhang, R., Liu, W., Zeng, J., Meng, J., Shi, L., Yang, S., Chang, J., Wang, C., Xing, K., Wen, J., et al. (2022) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2022.113732 · Cited by 19
Targeting in silico GPCR conformations with ultra-large library screening for hit discovery
Sala, D., Batebi, H., Ledwitch, K. V., Hildebrand, P. W., & Meiler, J. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.12.006 · Cited by 20
Developing novel antifungals: lessons from G protein-coupled receptors
Velazhahan, V., McCann, B. L., Bignell, E., & Tate, C. G. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.12.002 · Cited by 20
“computational platforms can screen >11 billion compounds in a matter of months and have been effective at identifying multiple hits that have nanomolar affinity for GPCRs [13,14].”
BackgroundBalius, T. E., Tan, Y.-H., & Chakrabarti, M. (2023) · Journal of Computational Chemistry · doi:10.1002/jcc.27218 · Cited by 28
The evolution of computational research in a data-centric world
Deshpande, D., Chhugani, K., Ramesh, T., Pellegrini, M. A., Shifman, S., Abedalthagafi, M., Alqahtani, S. A., Ye, C., Liu, X. S., Leek, J. T., et al. (2024) · Cell · doi:10.1016/j.cell.2024.07.045 · Cited by 23
Self-citationOn the Difficulty to Rescore Hits from Ultralarge Docking Screens
Sindt, F., Bret, G., & Rognan, D. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00730 · Cited by 20
Zhu, Z., Deng, Z., Wang, Q., Wang, Y., Zhang, D., Xu, R., Guo, L., & Wen, H. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.939555 · Cited by 18
“, 2020), and the improvement in searching efficiency in a fragment-based manner with the idea partial similar with dynamic programming (Sadybekov et al., 2022).”
BackgroundVirtual Screening in the Cloud Identifies Potent and Selective ROS1 Kinase Inhibitors
Petrović, D., Scott, J. S., Bodnarchuk, M. S., Lorthioir, O., Boyd, S., Hughes, G. M., Lane, J. S., Wu, A. J., Hargreaves, D., Robinson, J. D., et al. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00644 · Cited by 18
Targeting protein conformations with small molecules to control protein complexes
Zacharioudakis, E., & Gavathiotis, E. (2022) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2022.07.002 · Cited by 19
Abookleesh, F. L., Mosa, F. E. S., Barakat, K., & Ullah, A. (2022) · Polymers · doi:10.3390/polym14173690 · Cited by 18
“It can also be used to inform subsequent validation of conventional in vivo and in vitro models, thus reducing research time and the costs of drug discovery efforts [25,28].”
BackgroundChemical Space Virtual Screening against Hard-to-Drug RNA Methyltransferases DNMT2 and NSUN6
Zimmermann, R. A., Fischer, T. R., Schwickert, M., Nidoieva, Z., Schirmeister, T., & Kersten, C. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24076109 · Cited by 18
“Another approach is based on fragment-based drug design (FBDD), either physically by generating a chemical space upon crystallographically known fragment substructures and corresponding building blocks [46,47] or starting with pure fragment docking [48,49].”
Guo, J. R. (2024) · PLoS ONE · doi:10.1371/journal.pone.0296676 · Cited by 18
Exploring the combinatorial explosion of amine–acid reaction space via graph editing
Zhang, R., Mahjour, B. A., Outlaw, A., McGrath, A., Hopper, T., Kelley, B. P., Walters, W. P., & Cernak, T. (2024) · Communications Chemistry · doi:10.1038/s42004-024-01101-w · Cited by 20
“Among the modern ultra-large virtual libraries that have been reported, those that are generated through coupling of diverse building blocks employ only a small subset of curated reactions that are experimentally robust 24,25,36 – 44 .”
Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces
Sindt, F., Seyller, A., Eguida, M., & Rognan, D. (2024) · ACS Central Science · doi:10.1021/acscentsci.3c01521 · Cited by 18
Exploring Fragment Adding Strategies to Enhance Molecule Pretraining in AI-Driven Drug Discovery
Meng, Z., Chen, C., Zhang, X., Zhao, W. H., & Cui, X. (2024) · Big Data Mining and Analytics · doi:10.26599/bdma.2024.9020003 · Cited by 18
Emerging structure-based computational methods to screen the exploding accessible chemical space
Bedart, C., Simoben, C. V., & Schapira, M. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102812 · Cited by 19
Perspectives on current approaches to virtual screening in drug discovery
Muegge, I., Bentzien, J., & Ge, Y. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2390511 · Cited by 20
“Earlier prospective ULL VS campaigns against AmpC β-lactamase [12], KEAP1 [110], melatonin receptor [111], and cannabinoid receptors [112] also succeeded against targets previously proven druggable.”
AlphaFold2 structures template ligand discoveryPreprint
Lyu, J., Kapolka, N. J., Gumpper, R. H., Alon, A., Wang, L., Jain, M. K., Barros-Álvarez, X., Sakamoto, K., Kim, Y., DiBerto, J. F., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.20.572662 · Cited by 18
Self-citationReifs, A., Fernandez‐Calvo, A., Alonso-Lerma, B., Schönfelder, J., Franco, D., Ortega‐Muñoz, M., Casares, S., Jiménez-López, C., Saá, L., Cortajarena, A. L., et al. (2024) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2024.107133 · Cited by 17
“Additionally, the implementation of computational methods for structure-based virtual screening and molecular docking has boosted the capability of screening methods (35 – 39).”
MethodApplying Spatiotemporal Modeling of Cell Dynamics to Accelerate Drug Development
Chen, X., Xu, S., Chu, B., Guo, J., Zhang, H.-K., Sun, S.-Y., Song, L., & Feng, X. (2024) · ACS Nano · doi:10.1021/acsnano.4c12599 · Cited by 16
Wei, L., Xu, M., Liu, Z.-Q., Jiang, C., Lin, X., Hu, Y., Wen, X., Zou, R., Peng, C., Lin, H., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00543 · Cited by 17
Computational methods to assist in the discovery of pharmacological chaperones for rare diseases
Scafuri, B., Verdino, A., D’Arminio, N., & Marabotti, A. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac198 · Cited by 17
Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries
Alnammi, M., Liu, S., Ericksen, S. S., Ananiev, G. E., Voter, A. F., Guo, S., Keck, J. L., Hoffmann, F. M., Wildman, S. A., & Gitter, A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00912 · Cited by 17
Mfgnn: Multi‐Scale Feature‐Attentive Graph Neural Networks for Molecular Property Prediction
Ye, W., Li, J., & Cai, X. (2025) · Journal of Computational Chemistry · doi:10.1002/jcc.70011 · Cited by 19
Molecular Docking: State-of-the-Art Scoring Functions and Search Algorithms
de Angelo, R. M., de Sousa, D. S., da Silva, A. P., Chiari, L. P. A., da Silva, A. B. F., & Honório, K. M. (2024) · Computer-aided drug discovery and design · doi:10.1007/978-3-031-76718-0_7 · Cited by 17
Uzundurukan, A., Nelson, M., Teske, C., Islam, M. S., Elzagheid, M. I., Christy, J. V., Martin, H., Muratov, E. N., Glover, S., & Fuoco, D. (2025) · The Pharmacogenomics Journal · doi:10.1038/s41397-025-00368-z · Cited by 16
“Certain academic teams are even proposing virtual libraries in the range of 11 billion chemical entities [19, 20].”
ROCK inhibitor: Focus on recent updates
You, Y., Zhu, K., Wang, J., Liang, Q., Li, W., Wang, L., Guo, B., Zhou, J., Feng, X., & Shi, J. (2023) · Chinese Chemical Letters · doi:10.1016/j.cclet.2023.108336 · Cited by 16
Virtual library docking for cannabinoid-1 receptor agonists with reduced side effects
Tummino, T. A., Iliopoulos‐Tsoutsouvas, C., Braz, J. M., O’Brien, E. S., Stein, R. M., Craik, V., Tran, N. K., Ganapathy, S., Liu, F., Shiimura, Y., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57136-7 · Cited by 25
Self-citationSequence-based virtual screening using transformers
Zhang, S., Huo, D., Horne, R. I., Qi, Y., Ojeda, S. P., Yan, A., & Vendruscolo, M. (2025) · Nature Communications · doi:10.1038/s41467-025-61833-8 · Cited by 17
“These limitations have driven the development of computational methods for drug design in the last several decades 23 – 26 .”
Discovery of Covalent Ligands with AlphaFold3
Shamir, Y., Gabizon, R., Rogel, A., Lin, D. Y.-W., Andreotti, A. H., & London, N. (2026) · Journal of the American Chemical Society · doi:10.1021/jacs.5c22222 · Cited by 18
Illuminating the understudied GPCR-ome
Majumdar, S., Chiu, Y.-T., Pickett, J. E., & Roth, B. L. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103848 · Cited by 15
Self-citationZhai, S., Tan, Y., Zhu, C., Zhang, C., Gao, Y., Mao, Q., Zhang, Y., Duan, H., & Yin, Y. (2024) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2024.116628 · Cited by 17
Accurate Protein-Ligand Complex Structure Prediction using Geometric Deep LearningPreprint
Zhang, J., He, K., Dong, T., & Wu, J. (2022) · Research Square · doi:10.21203/rs.3.rs-1454132/v1 · Cited by 15
Trudeau, S. J., Hwang, H., Mathur, D., Begum, K., Petrey, D., Murray, D., & Honig, B. (2023) · Protein Science · doi:10.1002/pro.4594 · Cited by 16
“Updated versions of the protein class datasets compiled by Yamanishi et al. (2008) and updated in Liu et al. (2022) were obtained from https://github.com/ intelligence-csd-auth-gr/DTI_MDMF2A/tree/main/ datasets_mv.”
BackgroundMethodInfluentialWang, S., Lin, T., Peng, T., Xing, E., Chen, S., Kara, L. B., & Cheng, X. (2025) · Chemical Science · doi:10.1039/d4sc05211k · Cited by 14
“23,24 In such situations, these generative models o ff er no signi cant advantage in terms of chemical space exploration.”
Ultra-large library screening with an evolutionary algorithm in Rosetta (REvoLd)
Eisenhuth, P., Ließmann, F., Moretti, R., & Meiler, J. (2025) · Communications Chemistry · doi:10.1038/s42004-025-01758-x · Cited by 19
“Galileoismoreof aproofof conceptandisnotcombinedwith adocking protocol, butaccepts any score as fi tness and relies on external protocols.”
InfluentialScreening ultra-large virtual libraries
Crunkhorn, S. (2022) · Nature Reviews Drug Discovery · doi:10.1038/d41573-022-00002-8 · Cited by 14
A Simple Way to Incorporate Target Structural Information in Molecular Generative Models
Zhang, W., Zhang, K., & Huang, J. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00293 · Cited by 14
Structural diversity of leukotriene G-protein coupled receptors
Luginina, A. P., Gusach, A., Lyapina, E., Khorn, P. A., Safronova, N. A., Shevtsov, M. B., Dmitrieva, D. A., Dashevskii, D. E., Kotova, T., Smirnova, E., et al. (2023) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2023.105247 · Cited by 13
“The recent high-resolution structures of LT receptors should facilitate large-scale virtual ligand screening (88, 89) to identify new compound scaffolds and further optimize them for specific purposes.”
BackgroundA virtual drug-screening approach to conquer huge chemical libraries
Deane, C. M., & Mokaya, M. (2021) · Nature · doi:10.1038/d41586-021-03682-1 · Cited by 14
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Meqbil, Y. J., & van Rijn, R. M. (2022) · Pharmaceuticals · doi:10.3390/ph15070873 · Cited by 14
“Efforts in virtual screening of chemical libraries have used structure-based drug discovery to expand the available chemical space for various GPCR targets [72,76].”
BackgroundOverlap of On‐demand Ultra‐large Combinatorial Spaces with On‐the‐shelf Drug‐like Libraries
Perebyinis, M., & Rognan, D. (2022) · Molecular Informatics · doi:10.1002/minf.202200163 · Cited by 14
Zhou, H., Shan, M., Qin, L., & Cheng, G. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106379 · Cited by 13
Mészáros, B., Park, E., Malinverni, D., Sejdiu, B. I., Immadisetty, K., Sandhu, M., Lang, B., & Babu, M. M. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102608 · Cited by 13
Do Molecular Fingerprints Identify Diverse Active Drugs in Large-Scale Virtual Screening? (No)
Venkatraman, V., Gaiser, J., Demekas, D., Roy, A., Xiong, R., & Wheeler, T. J. (2024) · Pharmaceuticals · doi:10.3390/ph17080992 · Cited by 13
Gu, J., Peng, R.-K., Guo, C., Zhang, M., Yang, J., Yan, X. H., Zhou, Q., Li, H., Wang, N., Zhu, J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34598-7 · Cited by 15
“Compound libraries could bemainly classified into four types: (1) million-level entity libraries, which are large, accessible but costly, and usually have low structural complexity; (2) DNA-coding libraries, whose diversities are limited due to the limitation of reaction types; (3) virtual combinatorial libraries, whi…”
BackgroundUltra-large virtual screening unveils potent agonists of the neuromodulatory orphan receptor GPR139
de Vaca, I. C., Trapkov, B., Shen, L., Vo, D. D., Zhang, X., Yang, Y., Pezeshki, M., Zhang, X., Bällgren, F., Saleh, A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-66845-y · Cited by 15
Self-citationSong, K., Shi, D., Zhao, W., Gu, Y., Liu, D., & Chu, P. K. (2025) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2025.217195 · Cited by 13
Machine Learning for De Novo Molecular Generation: A Comprehensive Review
Chen, Y., & Xue, W. (2026) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.5c00861 · Cited by 13
Shape-Aware Synthon Search (SASS) for Virtual Screening of Synthon-Based Chemical Spaces
Cheng, C. Y., & Beroza, P. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01865 · Cited by 13
Hazemann, J., Kimmerlin, T., Lange, R., Sweeney, A. M., Bourquin, G., Ritz, D., & Czodrowski, P. (2024) · RSC Medicinal Chemistry · doi:10.1039/d4md00106k · Cited by 14
SpaceHASTEN: A Structure-Based Virtual Screening Tool for Nonenumerated Virtual Chemical Libraries
Kalliokoski, T., Turku, A., & Käsnänen, H. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01790 · Cited by 13
The Need for Continuing Blinded Pose- and Activity Prediction Benchmarks
Krämer, C., Chodera, J. D., Damm‐Ganamet, K. L., Gilson, M. K., Günther, J., Lessel, U. F., Lewis, R. A., Mobley, D. L., Nittinger, E., Pecina, A., et al. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02296 · Cited by 14
Shen, D., Yu, H., Wang, L., Wang, Y., Hong, Y., & Li, C. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.3c06483 · Cited by 12
Developments and challenges in hit progression within fragment-based drug discovery
Grosjean, H., & Biggin, P. C. (2026) · Nature Communications · doi:10.1038/s41467-026-68941-z · Cited by 15
“This enables to score large chemical space to optimise fragments at a fraction of the computational cost required by exhaustive structure-based screening approachces 210 .”
Yao, H., Wang, X., Chi, J., Chen, H., Liu, Y., Jiayi, Y., Yu, J., Ruan, Y., Xiang, X., Pi, J., et al. (2024) · Molecules · doi:10.3390/molecules29050964 · Cited by 11
Self-citationLin, T. E., Yen, D., HuangFu, W., Wu, Y.-W., Hsu, J., Yen, S., Sung, T.-Y., Hsieh, J., Pan, S., Yang, C., et al. (2024) · Protein Science · doi:10.1002/pro.5007 · Cited by 11
“Chemical fragments, which can serve as a starting scaffold for synthesizing molecules represented within the larger chemical library, can function as starting points (Sadybekov & Katritch, 2023; Sadybekov et al., 2022; Andrianov et al., 2021).”
BackgroundBuilding Block-Centric Approach to DNA-Encoded Library Design
Fitzgerald, P. R., Dixit, A., Zhang, C., Mobley, D. L., & Paegel, B. M. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00232 · Cited by 11
Yin, X., Wang, J., Ge, M., Feng, X., & Zhang, G. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c00347 · Cited by 11
He, B., Hu, Y., Liu, D., Zang, X., He, X., Chen, W., Yang, J., Feng, M., Chen, P., Wei, L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60489-8 · Cited by 11
“However, innovative approaches, including structure-based iterative learning 12 , peptidomimetics 13,14 , and virtual synthon hierarchical enumeration screening (V-SYNTHES) 15,16 , offer promising pathways forward.”
An online GPCR drug discovery resource
Caroli, J., Andreassen, S. N., Lorente, J. S., Xiao, B., Pándy‐Szekeres, G., & Gloriam, D. E. I. (2025) · npj Drug Discovery. · doi:10.1038/s44386-025-00010-9 · Cited by 11
Towards a Truly General Intermolecular Binding Affinity Calculator for Drug Discovery & DesignPreprint
Li, W., & Vottevor, G. (2023) · Preprints.org · doi:10.20944/preprints202208.0213.v2 · Cited by 12
Bedart, C., Shimokura, G. K., West, F. G., Wood, T. E., Batey, R. A., Irwin, J. J., & Schapira, M. (2024) · Scientific Data · doi:10.1038/s41597-024-03443-5 · Cited by 13
“One example is the hierarchical structure-based screening, introduced by Zhou et al . in 2009 45 , and made popular by the V-SYNTHES software developed by Sadybekov et al . in 2021 46 .”
MethodVigneron, S. F., Ohno, S., Braz, J. M., Kim, J. Y., Kweon, O. S., Webb, C. M., Billesbølle, C. B., Srinivasan, K., Bhardwaj, K., Irwin, J. J., et al. (2025) · ACS Central Science · doi:10.1021/acscentsci.5c00052 · Cited by 15
Early-stage structure-based drug discovery for small GTPases by NMR spectroscopy
Yin, G., Lv, G., Zhang, J., Jiang, H., Lai, T., Yang, Y., Ren, Y., Wang, J., Yi, C., Chen, H., et al. (2022) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2022.108110 · Cited by 11
Engineering Supramolecular Hybrid Architectures with Directional Organofluorine Bonds
Kotei, P. A., Paley⧓, D. W., Oklejas, V., Moreau, D., Schriber⧓, E. A., Aleksich⧓, M., Willson, M. C., Inoue, I., Owada, S., Tono, K., et al. (2023) · Small Science · doi:10.1002/smsc.202300110 · Cited by 10
Yu, J., Liao, P.-J., Keller, T. H., Cherian, J., Virshup, D. M., & Xu, W. (2024) · iScience · doi:10.1016/j.isci.2024.110454 · Cited by 10
Chen, S., & Zhong, F. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c01983 · Cited by 10
Santos, L. M., de Araújo, L. P., Falleiros, L., Mariano, C., de Azevedo, W. F., & da Silveira, N. J. F. (2025) · Current Medicinal Chemistry · doi:10.2174/0109298673331253241004110953 · Cited by 11
Emerging gut microbial glycoside hydrolase inhibitors
Kowalewski, M. E., & Redinbo, M. R. (2025) · RSC Chemical Biology · doi:10.1039/d5cb00050e · Cited by 12
Neumann, A., & Klein, R. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00719 · Cited by 10
Zhai, S., Tan, Y., Zhang, C., Hipolito, C. J., Song, L., Zhu, C., Zhang, Y., Duan, H., & Yin, Y. (2023) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.3c00627 · Cited by 11
Faizan, S., Talath, S., Wali, A. F., Hani, U., Haider, N., Mandal, S. P., & Kumar, B. R. P. (2024) · RSC Advances · doi:10.1039/d4ra01424c · Cited by 10
It all clicks together: In silico drug discovery becoming mainstream
Nazarova, A. L., & Katritch, V. (2022) · Clinical and Translational Medicine · doi:10.1002/ctm2.766 · Cited by 12
“14 For cannabinoid antagonists, chemical synthesis and experimental testing of novel compounds predicted by V-SYNTHES resulted in hit rate as high as 33%, including 14 submicromolar ligands.”
BackgroundMethodSelf-citationAlphaFold3 for Structure-guided Ligand DiscoveryPreprint
Menon, K. M., Davasam, A., Chen, G., Bryant, C., Deng, Z., Lam, B., Yang, C., Barcelos, D., Liu, F., Alon, A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.04.692352 · Cited by 15
Retrieval Augmented Docking Using Hierarchical Navigable Small Worlds
Hall, B. W., & Keiser, M. J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00683 · Cited by 9
Introducing SpaceGA: A Search Tool to Accelerate Large Virtual Screenings of Combinatorial Libraries
Moesgaard, L., & Kongsted, J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01308 · Cited by 9
Supekar, S., Tay, D. W. P., Yeo, W. L., Tam, K. W. E., Koo, Y. S., See, J. Y., Miyajima, J. M. T., Maurer‐Stroh, S., Ang, E. L., Lim, Y. H., et al. (2024) · ACS Catalysis · doi:10.1021/acscatal.4c04660 · Cited by 9
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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