A comprehensive map of molecular drug targets
Open accessAbstract
The success of mechanism-based drug discovery depends on the definition of the drug target. This definition becomes even more important as we try to link drug response to genetic variation, understand stratified clinical efficacy and safety, rationalize the differences between drugs in the same therapeutic class and predict drug utility in patient subgroups. However, drug targets are often poorly defined in the literature, both for launched drugs and for potential therapeutic agents in discovery and development. Here, we present an updated comprehensive map of molecular targets of approved drugs. We curate a total of 893 human and pathogen-derived biomolecules through which 1,578 US FDA-approved drugs act. These biomolecules include 667 human-genome-derived proteins targeted by drugs for human disease. Analysis of these drug targets indicates the continued dominance of privileged target families across disease areas, but also the growth of novel first-in-class mechanisms, particularly in oncology. We explore the relationships between bioactivity class and clinical success, as well as the presence of orthologues between human and animal models and between pathogen and human genomes. Through the collaboration of three independent teams, we highlight some of the ongoing challenges in accurately defining the targets of molecular therapeutics and present conventions for deconvoluting the complexities of molecular pharmacology and drug efficacy.
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
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?
Tclin currently consists of about 600 protein targets.
“Tclin currently consists of ~600 protein targets9”
From Unexplored therapeutic opportunities in the human genome (2018) · cites this paper as [9]
The Concluding thoughts and future work section identifies Tclin as a subset of the merged drug-efficacy-target data, and Drugs, targets and therapeutic areas reports 667 unique human protein efficacy targets in the merged data. Our copy does not give a count for the Tclin subset, so it does not establish that Tclin itself contains about 600 protein targets; no page numbers are available in the supplied passages. (LLM, conf=0.82)
Experimental — accuracy not yet measuredGPCRs are the largest family of druggable targets in the human genome, with 20-30% of approved drugs acting on them.
“GPCRs represent the largest family of druggable targets in the human genome9, with between 20% and 30% of approved drugs acting on them53,54.”
From Unexplored therapeutic opportunities in the human genome (2018) · cites this paper as [9]
In the citing sentence, this source is cited for the drug-percentage claim, not the preceding ‘largest family’ claim. The Drugs, targets and therapeutic areas section reports that rhodopsin-like GPCRs account for the therapeutic effect of 33% of approved small-molecule drugs, rather than 20–30% of approved drugs generally. Our copy provides no page number for the passage. (LLM, conf=0.88)
Experimental — accuracy not yet measured217 drugs in DrugCentral act through 125 Tclin ion channels for MoA.
“There are 217 drugs annotated in DrugCentral10 as acting through 125 (T) ion channels for the MoA9.”
From Unexplored therapeutic opportunities in the human genome (2018) · cites this paper as [9]
In “Drugs, targets and therapeutic areas” (page unavailable), the paper says it combined DrugCentral with other databases and identifies ion channels as efficacy targets for approved drugs. It reports percentages for ion-channel targets and drugs, but does not give the claimed 217 drugs, 125 Tclin ion channels, or a DrugCentral-only count. (LLM, conf=0.86)
Experimental — accuracy not yet measuredProteins that directly regulate transcription had one of the highest success rates for drug approval among different classes of drug target proteins.
“Interestingly, among different classes of drug target proteins, proteins that directly regulate transcription had one of the highest success rates for drug approval, according to a critical evaluation of all bioactive molecules with drug-like properties in ChEMBL data43.”
From Effects of SPI1-mediated transcriptome remodeling on Alzheimer's disease-related phenotypes in mouse models of Aβ amyloidosis (2024) · cites this paper as [43]
The ‘Drugs, targets and therapeutic areas’ section reports relative enrichment of approved drugs for nuclear receptors when compared with compounds targeting them in ChEMBL. Nuclear receptors are a transcription-regulating target family, but the paper does not establish that proteins directly regulating transcription as a broader class had one of the highest drug-approval success rates. (LLM, conf=0.83)
Experimental — accuracy not yet measuredTclin assignment relied on exhaustive manual searches of primary literature and drug labels to identify mechanism-of-action links.
“Classification into this TDL category was achieved through exhaustive manual querying of primary literature and drug labels for MoA assignments with respect to molecular (protein) targets9”
From Unexplored therapeutic opportunities in the human genome (2018) · cites this paper as [9]
The targets of transcription factors are not randomly selected.
“One critical concept to keep in mind here is that the targets of transcription factors are not randomly selected.”
From Effects of SPI1-mediated transcriptome remodeling on Alzheimer's disease-related phenotypes in mouse models of Aβ amyloidosis (2024) · cites this paper as [43]
The Drugs, targets and therapeutic areas and Concluding thoughts and future work sections discuss proteins selected as drug targets, not genes selected by transcription factors. Searches for transcription-factor target selection and gene regulation found no passage addressing whether that selection is random; page numbers were not available in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredTarget genes of transcription factors are almost always within the same or similar pathways that need to be co-regulated in a coordinated manner.
“These target genes are almost always within the same or similar pathways that need to be co-regulated in a coordinated manner43,44.”
From Effects of SPI1-mediated transcriptome remodeling on Alzheimer's disease-related phenotypes in mouse models of Aβ amyloidosis (2024) · cites this paper as [43]
The Drugs, targets and therapeutic areas section classifies drug efficacy targets, while Cancer drivers and cancer targets discusses druggable pathways. Neither addresses whether transcription factors’ target genes are almost always in similar pathways or require coordinated co-regulation. Searches for transcription factors and co-regulated pathways found no passage supporting that point; page numbers were not available in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredAlthough some have entered clinical trials with a primary focus on PROTAC, the diverse range of protein regulation methods has undoubtedly laid a robust foundation for developing drugs targeting previously undruggable proteins.
“Although some have entered clinical trials with a primary focus on PROTAC, the diverse range of protein regulation methods has undoubtedly laid a robust foundation for developing drugs targeting previously undruggable proteins.477–479”
From Recent advances in targeting the “undruggable” proteins: from drug discovery to clinical trials (2023) · cites this paper as [479]
39 new targets were annotated as Tclin.
“39 new targets were annotated as Tclin, i.e. MoA targets through which approved drugs exert their therapeutic action (32–34).”
From DrugCentral 2023 extends human clinical data and integrates veterinary drugs (2023) · cites this paper as [32]
Tclin are protein drug targets via which approved drugs act, and currently include 659 human proteins.
“Tclin are protein drug targets via which approved drugs act (24–26), which currently includes 659 human proteins”
From TCRD and pharos 2021: mining the human proteome for disease biology (2021) · cites this paper as [24]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Circos: An information aesthetic for comparative genomicsPreprint
Krzywinski, M. I., Schein, J. E., Birol, İ., Connors, J. M., Gascoyne, R. D., Horsman, D. E., Jones, S. J. M., & Marra, M. A. (2009) · Genome Research · doi:10.1101/gr.092759.109 · Cited by 10,838 · see citing works
Vogelstein, B., Papadopoulos, N., Velculescu, V. E., Zhou, S., Diaz, L. A., & Kinzler, K. W. (2013) · Science · doi:10.1126/science.1235122 · Cited by 7,863 · see citing works
Identification of the transforming EML4–ALK fusion gene in non-small-cell lung cancer
Soda, M., Choi, Y. L., Enomoto, M., Takada, S., Yamashita, Y., Ishikawa, S., Fujiwara, S.-I., Watanabe, H., Kurashina, K., Hatanaka, H., et al. (2007) · Nature · doi:10.1038/nature05945 · Cited by 5,439 · see citing works
Mutational landscape and significance across 12 major cancer types
Kandoth, C., McLellan, M. D., Vandin, F., Ye, K., Niu, B., Lu, C., Xie, M., Zhang, Q., McMichael, J. F., Wyczalkowski, M. A., et al. (2013) · Nature · doi:10.1038/nature12634 · Cited by 4,419 · see citing works
DrugBank: a comprehensive resource for in silico drug discovery and exploration
Wishart, D. S. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj067 · Cited by 3,892 · see citing works
How many drug targets are there?
Overington, J. P., Al-Lazikani, B., & Hopkins, A. L. (2006) · Nature Reviews Drug Discovery · doi:10.1038/nrd2199 · Cited by 3,505 · see citing works
Self-citationHopkins, A. L., & Groom, C. R. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd892 · Cited by 3,305 · see citing works
Discovery and saturation analysis of cancer genes across 21 tumour types
Lawrence, M. S., Stojanov, P., Mermel, C. H., Robinson, J. T., Garraway, L. A., Golub, T. R., Meyerson, M., Gabriel, S. B., Lander, E. S., & Getz, G. (2014) · Nature · doi:10.1038/nature12912 · Cited by 3,096 · cited 1× · see citing works
“Compiling an accurate and agreed list of drug efficacy targets is not a trivial task, and with work from three teams we have made significant progress towards this goal, as well as highlighted some of the GPCRs (7TM1) Voltage-gated ion channels Ligand-gated ion channels Kinases practical challenges.”
BackgroundInternational network of cancer genome projects
The International Cancer Genome Consortium (2010) · Nature · doi:10.1038/nature08987 · Cited by 2,299 · see citing works
DNA-Repair Defects and Olaparib in Metastatic Prostate Cancer
Mateo, J., Carreira, S., Sandhu, S., Miranda, S., Mossop, H., Perez-Lopez, R., Nava Rodrigues, D., Robinson, D., Omlin, A., Tunariu, N., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1506859 · Cited by 2,185 · see citing works
How were new medicines discovered?
Swinney, D. C., & Anthony, J. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3480 · Cited by 1,812 · see citing works
Principles of cancer therapy: oncogene and non-oncogene addiction
Luo, J., Solimini, N. L., & Elledge, S. J. (2009) · Cell · doi:10.1016/j.cell.2009.02.024 · Cited by 1,765 · see citing works
Ligand-targeted therapeutics in anticancer therapy
Allen, T. M. (2002) · Nature reviews. Cancer · doi:10.1038/nrc903 · Cited by 1,648 · see citing works
Wells, S. A., Robinson, B. G., Gagel, R. F., Dralle, H., Fagin, J. A., Santoro, M., Baudin, É., Elisei, R., Jarząb, B., Vasselli, J. R., et al. (2011) · Journal of Clinical Oncology · doi:10.1200/jco.2011.35.5040 · Cited by 1,556 · see citing works
The ChEMBL bioactivity database: an update
Bento, A. P., Gaulton, A., Hersey, A., Bellis, L. J., Chambers, J. K., Davies, M., Krüger, F. A., Light, Y., Mak, L., McGlinchey, S., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1031 · Cited by 1,487 · see citing works
Self-citationEnsembl BioMarts: a hub for data retrieval across taxonomic space
Kinsella, R., Kähäri, A., Haider, S., Zamora, J., Proctor, G., Spudich, G., Almeida-King, J., Staines, D., Derwent, P., Kerhornou, A., et al. (2011) · Database J. Biol. Databases Curation · doi:10.1093/database/bar030 · Cited by 1,465 · see citing works
ABC transporters in cancer: more than just drug efflux pumps
Fletcher, J. I., Haber, M., Henderson, M. J., & Norris, M. D. (2010) · Nature reviews. Cancer · doi:10.1038/nrc2789 · Cited by 1,022 · see citing works
THE UNITED STATES PHARMACOPEIAL CONVENTION
Hirschfelder, A. D. (1930) · JAMA · doi:10.1001/jama.1930.02720030050030 · Cited by 1,015 · see citing works
Trends in the exploitation of novel drug targets
Rask-Andersen, M., Almén, M. S., & Schiöth, H. B. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3478 · Cited by 853 · see citing works
Pawson, A. J., Sharman, J. L., Benson, H. E., Faccenda, E., Alexander, S. P. H., Buneman, O. P., Davenport, A. P., McGrath, J. C., Peters, J. A., Southan, C., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1143 · Cited by 845 · see citing works
Drugs, their targets and the nature and number of drug targets
Imming, P., Sinning, C., & Meyer, A. (2006) · Nature Reviews Drug Discovery · doi:10.1038/nrd2132 · Cited by 840 · see citing works
Rang, H. P., & Dale, M. M. (2012) · Andalas University Repository (Andalas University) · Cited by 835 · see citing works
Flicek, P., Amode, M. R., Barrell, D. G., Beal, K. M., Brent, S., Carvalho‐Silva, D., Clapham, P., Coates, G. M. P., Fairley, S., Fitzgerald, S. F., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr991 · Cited by 818 · see citing works
Global mapping of pharmacological space
Paolini, G. V., Shapland, R. H. B., van Hoorn, W. P., Mason, J. S., & Hopkins, A. L. (2006) · Nature Biotechnology · doi:10.1038/nbt1228 · Cited by 812 · see citing works
Weinstein, I. B., & Joe, A. (2008) · Cancer Research · doi:10.1158/0008-5472.can-07-3293 · Cited by 805 · see citing works
TTD: Therapeutic Target Database
Chen, X. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.412 · Cited by 723 · see citing works
SuperTarget and Matador: resources for exploring drug-target relationships
Gunther, S., Kuhn, M., Dunkel, M., Campillos, M., Senger, C., Petsalaki, E., Ahmed, J., Urdiales, E. G., Gewiess, A., Jensen, L. J., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm862 · Cited by 647 · cited 1× · see citing works
“DrugBank 9 , the most widely used specialist drug information resource, maps drugs to proteins that have been reported to bind to them, and SuperTarget 10 is a text-mining-based compilation of direct and indirect drug targets.”
BackgroundMartindale : the complete drug reference
Brayfield, A. (2014) · Pharmaceutical Press eBooks · Cited by 564 · see citing works
The discovery of first-in-class drugs: origins and evolution
Eder, J., Sedrani, R., & Wiesmann, C. (2014) · Nature Reviews Drug Discovery · doi:10.1038/nrd4336 · Cited by 514 · see citing works
Probing the links between in vitro potency, ADMET and physicochemical parameters
Gleeson, M. P., Hersey, A., Montanari, D., & Overington, J. P. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3367 · Cited by 514 · see citing works
Self-citationProdrugs—from Serendipity to Rational Design
Huttunen, K. M., Raunio, H., & Rautio, J. (2011) · Pharmacological Reviews · doi:10.1124/pr.110.003459 · Cited by 505 · see citing works
InParanoid 8: orthology analysis between 273 proteomes, mostly eukaryotic
Sonnhammer, E. L. L., & Östlund, G. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1203 · Cited by 482 · see citing works
Huang, R., Southall, N. T., Wang, Y., Yasgar, A., Shinn, P., Jadhav, A., Nguyễn, Ð.-T., & Austin, C. P. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3001862 · Cited by 399 · see citing works
DrugCentral: online drug compendium
Ursu, O., Holmes, J., Knockel, J., Bologa, C. G., Yang, J. J., Mathias, S. L., Nelson, S. J., & Oprea, T. I. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw993 · Cited by 323 · see citing works
Self-citationGenomic sciences and the medicine of tomorrow
Drews, J. (1996) · Nature Biotechnology · doi:10.1038/nbt1196-1516 · Cited by 246 · see citing works
Mamo, D. C., Graff, A., Mizrahi, R., Shammi, C., Romeyer, F., & Kapur, S. (2007) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2007.06091479 · Cited by 241 · see citing works
Drug Development: The role of innovation in drug development
Drews, J., & Ryser, S. (1997) · Nature Biotechnology · doi:10.1038/nbt1297-1318 · Cited by 179 · see citing works
canSAR: an updated cancer research and drug discovery knowledgebase
Tym, J. E., Mitsopoulos, C., Coker, E. A., Razaz, P., Schierz, A. C., Antolín, A. A., & Al‐Lazikani, B. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1030 · Cited by 135 · see citing works
Self-citationObjective assessment of cancer genes for drug discovery
Patel, M. N., Halling‐Brown, M., Tym, J. E., Workman, P., & Al‐Lazikani, B. (2012) · Nature Reviews Drug Discovery · doi:10.1038/nrd3913 · Cited by 130 · see citing works
Self-citationRaju, T. N. K. (2000) · The Lancet · doi:10.1016/s0140-6736(05)74775-9 · Cited by 128 · see citing works
Thollon, C., Bedut, S., Villeneuve, N., Cogé, F., Piffard, L., Guillaumin, J., Brunel-Jacquemin, C., Chomarat, P., Boutin, J. A., Peglion, J., et al. (2006) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0706940 · Cited by 122 · see citing works
Lorcaserin and pimavanserin: emerging selectivity of serotonin receptor subtype–targeted drugs
Meltzer, H. Y., & Roth, B. L. (2013) · Journal of Clinical Investigation · doi:10.1172/jci70678 · Cited by 109 · see citing works
Lammers, J. J., Minettte, P., McCusker, M., et al. (1989) · American Review of Respiratory Disease · doi:10.1164/ajrccm/139.2.446 · Cited by 104 · see citing works
Expression of muscarinic receptors by human macrophages
Koarai, A., Traves, S. L., Fenwick, P. S., et al. (2011) · European Respiratory Journal · doi:10.1183/09031936.00136710 · Cited by 68 · see citing works
Identification of the Molecular Site of Ivabradine Binding to HCN4 Channels
Bucchi, A., Baruscotti, M., Nardini, M., Barbuti, A., Micheloni, S., Bolognesi, M., & DiFrancesco, D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0053132 · Cited by 67 · see citing works
Penicillin-Binding Proteins 2x and 2b as Primary PBP Targets in Streptococcus pneumoniae
Krauss, J. B., van der Linden, M., Grebe, T. W., et al. (1996) · Microbial Drug Resistance · doi:10.1089/mdr.1996.2.183 · Cited by 58 · see citing works
Workman, P., & Al‐Lazikani, B. (2013) · Nature Reviews Drug Discovery · doi:10.1038/nrd4184 · Cited by 55 · see citing works
Self-citationPimavanserin for the treatment of Parkinson's disease psychosis
Friedman, J. H. (2013) · Expert Opinion on Pharmacotherapy · doi:10.1517/14656566.2013.819345 · Cited by 48 · see citing works
Shukla, N. N., Somwar, R., Smith, R. S., et al. (2016) · Cancer Research · doi:10.1158/0008-5472.can-16-1040 · Cited by 46 · see citing works
Molecular biology of the MEN2 gene
Santoro, Melillo, R. M., Carlomagno, et al. (1998) · Journal of Internal Medicine · doi:10.1046/j.1365-2796.1998.00330.x · Cited by 45 · see citing works
Novelty in the target landscape of the pharmaceutical industry
Agarwal, P., Sanséau, P., & Cardon, L. R. (2013) · Nature Reviews Drug Discovery · doi:10.1038/nrd4089 · Cited by 35 · see citing works
Structural basis of drugs that increase cardiac inward rectifier Kir2.1 currents
Gómez, R. J., Caballero, R., Barana, A., et al. (2014) · Cardiovascular Research · doi:10.1093/cvr/cvu203 · Cited by 32 · see citing works
Drews, J., & Ryser, S. (1997) · Nature Biotechnology · doi:10.1038/nbt1297-1350 · Cited by 29 · see citing works
Pleiotropic Effects of Antiarrhythmic Agents: Dronedarone in the Treatment of Atrial Fibrillation
Heijman, J., Heusch, G., & Dobrev, D. (2013) · Clinical Medicine Insights Cardiology · doi:10.4137/cmc.s8445 · Cited by 28 · see citing works
Tolboom, N., Berendse, H. W., Leysen, J. E., et al. (2014) · Neuropsychopharmacology · doi:10.1038/npp.2014.195 · Cited by 26 · see citing works
A Forensic Analysis of Drug Targets From 2000 Through 2012
Munos, B. H. (2013) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2013.126 · Cited by 20 · see citing works
Prioritizing the human genome: knowledge management for drug discovery.
Golden, J. B. (2003) · PubMed · Cited by 20 · see citing works
Sobrado, L. F., Varone, B. B., Machado, A. D., Nearing, B. D., Zeng, D., Belardinelli, L., & Verrier, R. L. (2013) · Journal of Cardiovascular Electrophysiology · doi:10.1111/jce.12155 · Cited by 15 · see citing works
Blocking the survival of the nastiest by HSP90 inhibition
Workman, P., Clarke, P. A., & Al‐Lazikani, B. (2016) · Oncotarget · doi:10.18632/oncotarget.6971 · Cited by 14 · see citing works
Self-citationUnpicking the Combination Lock for Mutant BRAF and RAS Melanomas
Al‐Lazikani, B., & Workman, P. (2013) · Cancer Discovery · doi:10.1158/2159-8290.cd-12-0520 · Cited by 7 · see citing works
Self-citationXynogalos, P., Seyler, C., Scherer, D. J., et al. (2014) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-014-1045-6 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [55]Huttunen KM Raunio H Rautio J Prodrugs — from serendipity to rational design Pharmacol Rev 2011 63 750 771 21737530
Cited by
Sorted by citations
Opportunities and challenges in phenotypic drug discovery: an industry perspective
Moffat, J. G., Vincent, F., Lee, J. A., Eder, J., & Prunotto, M. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.111 · Cited by 939 · see citing works
Lu, H., Zhou, Q., He, J., Jiang, Z., Peng, C., Tong, R., & Shi, J. (2020) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00315-3 · Cited by 891 · see citing works
Rethinking drug design in the artificial intelligence era
Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S., et al. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0050-3 · Cited by 894 · see citing works
Zheng, J., Haberland, V., Baird, D., Walker, V., Haycock, P., Hurle, M., Gutteridge, A., Erola, P., Liu, Y., Luo, S., et al. (2019) · doi:10.1101/627398 · Cited by 812 · see citing works
Self-citationThe International League Against Epilepsy Consortium on Complex Epilepsies, Abou-Khalil, B., Auce, P., Avbersek, A., Bahlo, M., Balding, D. J., Bast, T., Baum, L., Becker, A. J., Becker, F., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07524-z · Cited by 482 · see citing works
Big data and artificial intelligence modeling for drug discovery
Zhu, H. (2020) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010919-023324 · Cited by 455 · see citing works
Unexplored therapeutic opportunities in the human genome
Oprea, T. I., Bologa, C. G., Brunak, S., Campbell, A., Gan, G. N., Gaulton, A., Gomez, S. M., Guha, R., Hersey, A., Holmes, J., et al. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2018.14 · Cited by 378 · see citing works
Self-citationRecent advances in targeting the “undruggable” proteins: from drug discovery to clinical trials
Xie, X., Yu, T., Li, X., Zhang, N., Foster, L. J., Peng, C., Huang, W., & He, G. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01589-z · Cited by 396 · see citing works
Target identification among known drugs by deep learning from heterogeneous networks
Zeng, X., Zhu, S., Lu, W., Liu, Z., Huang, J., Zhou, Y., Fang, J., Huang, Y., Guo, H., Li, L., et al. (2020) · Chemical Science · doi:10.1039/c9sc04336e · Cited by 357 · see citing works
Therapies for rare diseases: therapeutic modalities, progress and challenges ahead
Tambuyzer, E., Vandendriessche, B., Austin, C. P., Brooks, P. J., Larsson, K., Miller Needleman, K. I., Valentine, J., Davies, K., Groft, S. C., Preti, R., et al. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0049-9 · Cited by 357 · see citing works
Self-citationDrugCentral: online drug compendium
Ursu, O., Holmes, J., Knockel, J., Bologa, C. G., Yang, J. J., Mathias, S. L., Nelson, S. J., & Oprea, T. I. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw993 · Cited by 323 · see citing works
Self-citationAI-powered therapeutic target discovery
Pun, F. W., Ozerov, I. V., & Zhavoronkov, A. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2023.06.010 · Cited by 341 · see citing works
Pharos: collating protein information to shed light on the druggable genome
Nguyen, D.-T., Mathias, S., Bologa, C., Brunak, S., Fernandez, N., Gaulton, A., Hersey, A., Holmes, J., Jensen, L. J., Karlsson, A., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1072 · Cited by 279 · see citing works
Self-citationMelatonin in type 2 diabetes mellitus and obesity
Karamitri, A., & Jockers, R. (2019) · Nature Reviews Endocrinology · doi:10.1038/s41574-018-0130-1 · Cited by 268 · see citing works
Synergistic insights into human health from aptamer- and antibody-based proteomic profiling
Pietzner, M., Wheeler, E., Carrasco-Zanini, J., Kerrison, N. D., Oerton, E., Koprulu, M., Luan, J., Hingorani, A. D., Williams, S. A., Wareham, N. J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27164-0 · Cited by 234 · see citing works
DrugCentral 2021 supports drug discovery and repositioning
Avram, S., Bologa, C. G., Holmes, J., Bocci, G., Wilson, T. B., Nguyen, D.-T., Curpan, R., Halip, L., Bora, A., Yang, J. J., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa997 · Cited by 189 · see citing works
Self-citationTCRD and pharos 2021: mining the human proteome for disease biology
Sheils, T. K., Mathias, S. L., Kelleher, K. J., Siramshetty, V. B., Nguyen, D.-T., Bologa, C. G., Jensen, L. J., Vidović, D., Koleti, A., Schürer, S. C., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa993 · Cited by 160 · see citing works
Self-citationPillaiyar, T., Meenakshisundaram, S., Manickam, M., & Sankaranarayanan, M. (2020) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2020.112275 · Cited by 140 · see citing works
Neuropeptide and small transmitter coexistence: Fundamental studies and relevance to mental illness
Hökfelt, T., Barde, S., Xu, Z.-Q. D., Kuteeva, E., Rüegg, J., Le Maitre, E., Risling, M., Kehr, J., Ihnatko, R., Theodorsson, E., et al. (2018) · Frontiers in Neural Circuits · doi:10.3389/fncir.2018.00106 · Cited by 139 · see citing works
Exploration of databases and methods supporting drug repurposing: a comprehensive survey
Tanoli, Z., Seemab, U., Scherer, A., Wennerberg, K., Tang, J., & Vähä-Koskela, M. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa003 · Cited by 131 · see citing works
DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network
Pu, L., Govindaraj, R. G., Lemoine, J. M., Wu, H.-C., & Brylinski, M. (2019) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006718 · Cited by 133 · see citing works
Pharos 2023: an integrated resource for the understudied human proteome
Kelleher, K. J., Sheils, T. K., Mathias, S. L., Yang, J. J., Metzger, V. T., Siramshetty, V. B., Nguyen, D.-T., Jensen, L. J., Vidović, D., Schürer, S. C., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1033 · Cited by 137 · see citing works
Self-citationGlimmers in illuminating the druggable genome
Rodgers, G., Austin, C., Anderson, J., Pawlyk, A., Colvis, C., Margolis, R., & Baker, J. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.252 · Cited by 126 · see citing works
Hot-spot analysis for drug discovery targeting protein-protein interactions
Rosell, M., & Fernández-Recio, J. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1430763 · Cited by 105 · see citing works
Xuan, P., Sun, C., Zhang, T., Ye, Y., Shen, T., & Dong, Y. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00459 · Cited by 100 · see citing works
DrugCentral 2023 extends human clinical data and integrates veterinary drugs
Avram, S., Wilson, T. B., Curpan, R., Halip, L., Borota, A., Bora, A., Bologa, C. G., Holmes, J., Knockel, J., Yang, J. J., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1085 · Cited by 101 · see citing works
Self-citationKaramboulas, C., Bruce, J. P., Hope, A. J., Meens, J., Huang, S. H., Erdmann, N., Hyatt, E., Pereira, K., Goldstein, D. P., Weinreb, I., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.10.004 · Cited by 94 · see citing works
Artificial intelligence facilitates drug design in the big data era
Wang, L., Ding, J., Pan, L., Cao, D., Jiang, H., & Ding, X. (2019) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2019.103850 · Cited by 80 · see citing works
Cryptic pocket formation underlies allosteric modulator selectivity at muscarinic GPCRs
Hollingsworth, S. A., Kelly, B., Valant, C., Michaelis, J. A., Mastromihalis, O., Thompson, G., Venkatakrishnan, A. J., Hertig, S., Scammells, P. J., Sexton, P. M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11062-7 · Cited by 73 · see citing works
Lindbohm, J. V., Mars, N., Walker, K. A., Singh‐Manoux, A., Livingston, G., Brunner, E. J., Sipilä, P. N., Saksela, K., Ferrie, J. E., Lovering, R. C., et al. (2022) · Alzheimer's & Dementia · doi:10.1002/alz.12419 · Cited by 70 · see citing works
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts
Karimi, M., Wu, D., Wang, Z., & Shen, Y. (2019) · doi:10.1101/2019.12.28.890103 · Cited by 64 · see citing works
Cheng, F., Wang, F., Tang, J., Zhou, Y., Fu, Z., Zhang, P., Haines, J. L., Leverenz, J. B., Gan, L., Hu, J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101379 · Cited by 63 · see citing works
Deep learning methods for protein function prediction
Boadu, F., Lee, A., & Cheng, J. (2025) · PROTEOMICS · doi:10.1002/pmic.202300471 · Cited by 53 · see citing works
NetBID2 provides comprehensive hidden driver analysis
Dong, X., Ding, L., Thrasher, A., Wang, X., Liu, J., Pan, Q., Rash, J., Dhungana, Y., Yang, X., Risch, I., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38335-6 · Cited by 37 · see citing works
HIV-1 fusion inhibitors targeting the membrane-proximal external region of Env spikes
Xiao, T., Frey, G., Fu, Q., Lavine, C. L., Scott, D. A., Seaman, M. S., Chou, J. J., & Chen, B. (2020) · Nature Chemical Biology · doi:10.1038/s41589-020-0496-y · Cited by 38 · see citing works
Cooperativity between the orthosteric and allosteric ligand binding sites of RORγt
de Vries, R. M. J. M., Meijer, F. A., Doveston, R. G., Leijten-van de Gevel, I. A., & Brunsveld, L. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2021287118 · Cited by 38 · see citing works
TIN-X: target importance and novelty explorer
Cannon, D. C., Yang, J. J., Mathias, S. L., Ursu, O., Mani, S., Waller, A., Schürer, S. C., Jensen, L. J., Sklar, L. A., Bologa, C. G., et al. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx200 · Cited by 34 · see citing works
Self-citationSloop, K. W., Briere, D. A., Emmerson, P. J., & Willard, F. S. (2018) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.8b00009 · Cited by 31 · see citing works
Kim, B., Dabin, L. C., Tate, M. D., Karahan, H., Sharify, A. D., Acri, D. J., Al-Amin, M. M., Philtjens, S., Smith, D. C., Wijeratne, H. R. S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48484-x · Cited by 28 · see citing works
“These target genes are almost always within the same or similar pathways that need to be co-regulated in a coordinated manner 43,44 .”
Elucidating the druggability of the human proteome with eFindSite
Kana, O., & Brylinski, M. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-019-00197-w · Cited by 27 · see citing works
Liu, X., Zhang, Y., Ward, L. D., Yan, Q., Bohnuud, T., Hernandez, R., Lao, S., Yuan, J., & Fan, F. (2021) · Scientific Reports · doi:10.1038/s41598-021-95354-3 · Cited by 21 · see citing works
Quantitatively accounting for protein reorganization in computer-aided drug design
Fajer, M., Borrelli, K., Abel, R., & Wang, L. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00009 · Cited by 18 · see citing works
Avram, S., Curpan, R., Halip, L., Bora, A., & Oprea, T. I. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00826 · Cited by 18 · see citing works
Self-citationNordquist, E. B., Zhao, M., Kumar, A., & MacKerell, A. D. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01189 · Cited by 15 · see citing works
In vitro affinity maturation of antibody against membrane-bound GPCR molecules
Wang, J., An, L., Zhao, Y., Zhang, C., Li, S., Ye, C., Jing, S., & Hang, H. (2019) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-019-10030-x · Cited by 11 · see citing works
Advancing membrane-associated protein docking with improved sampling and scoring in Rosetta
Samnata, R., Harmalkar, A., Prathima, P., & Gray, J. J. (2024) · doi:10.1101/2024.07.09.602802 · Cited by 5 · see citing works
- Authors
- Santos, R., Ursu, O., Gaulton, A., Bento, A. P., Donadi, R. S., Bologa, C. G., Karlsson, A., Al-Lazikani, B., Hersey, A., Oprea, T. I., & Overington, J. P.
- Affiliations
- European Bioinformatics Institute (GB)
- University of New Mexico (US)
- Corresponding author
- Hersey, Anne
- Oprea, Tudor I.
- Overington, John P.
- ORCID
- Venue
- Nature Reviews Drug Discovery
- Published
- 2016-12-02
- Volume
- 16
- Issue
- 1
- Pages
- 19-34
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- PMC
- PMC6314433
- OpenAlex
- W2558217333
- Cited by
- 2,427
- References
- 61
- Citations by year
- 2026: 236 · 2025: 302 · 2024: 307 · 2023: 247 · 2022: 275 · 2021: 284 · 2020: 251 · 2019: 209 · 2018: 200 · 2017: 107 · 2016: 4 · 2015: 1 · 2012: 2
- FWCI
- 13.41
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Drug Discovery (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Wellcome Trust (104104/A/14/Z, WT104104/Z/14/Z, WT086151/Z/08/Z)
- European Bioinformatics Institute
- Cancer Research UK (11566, 22897)
- National Institutes of Health (UL1TR001449)
- National Cancer Institute (U54CA189205, P30CA118100)
- National Center for Advancing Translational Sciences
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- therapeutic target, molecular therapeutic targets, approved drugs, model organisms, oncology drugs, drug efficacy, molecular pharmacology
- MeSH terms
- Humans, Pharmacogenetics, Drug Prescriptions, United States, United States Food and Drug Administration, Genetic Variation, Genome, Human, Drug Delivery Systems, Drug Approval, Drug Discovery, Databases, Pharmaceutical
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
