Novel drug targets in 2019
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Analysis of novel MoA drug targets published annually in Nature Reviews Drug Discovery with data pulled from DrugCentral shows, on average, an enrichment rate of 15 novel drug targets per year.
“The analysis of novel MoA drug targets (targets that an approved drug had not previously perturbed) published annually in the Nature Reviews Drug Discovery series (33,34,41,42) with data pulled from DrugCentral, shows, on average, an enrichment rate of 15 novel drug targets per year”
From DrugCentral 2023 extends human clinical data and integrates veterinary drugs (2023) · cites this paper as [34]
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 [34]
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 [26]
Cited by
Sorted by citations
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
“…into four categories, according to their ‘tar-get development level’ (TDL): Tclin are MoA-designated drug targets via which approved drugs act (15,20,21), currently 659 human proteins; Tchem are proteins that are not Tclin , but are known to bind small molecules with high potency; Tbio…”
BackgroundSelf-citationDrugCentral 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
“The analysis of novel MoA drug targets (targets that an approved drug had not previously perturbed) published annually in the Nature Reviews Drug Discovery series (33,34,41,42) with data pulled from DrugCentral, shows, on average, an enrichment rate of 15 novel drug targets per year (some of the data overlap with Tabl…”
BackgroundSelf-citationWang, T., Liu, J., Luo, X., Hu, L., & Lu, H. (2021) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2021.107824 · Cited by 88 · see citing works
An Overview of Cell-Based Assay Platforms for the Solute Carrier Family of Transporters
Dvorak, V., Wiedmer, T., Inglés‐Prieto, Á., Altermatt, P., Batoulis, H., Bärenz, F., Bender, E., Digles, D., Dürrenberger, F., Heitman, L. H., et al. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.722889 · Cited by 69 · see citing works
Ferdosi, S., Stukalov, A., Hasan, M., Tangeysh, B., Brown, T. R., Wang, T., Elgierari, E. M., Zhao, X., Huang, Y., Alavi, A., et al. (2022) · Advanced Materials · doi:10.1002/adma.202206008 · Cited by 61 · see citing works
Therapeutic Target Identification and Drug Discovery Driven by Chemical Proteomics
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“Some of the early synthetic drugs, such as aspirin (acetylsalicylic acid) and phenytoin sodium, are still in today, while some others have disappeared from the market but The development of synthetic small-molecule drugs has provided a powerful tool for precision medicine, enabling personalized…”
BackgroundUnderstanding the Polypharmacological Profiles of Triple Reuptake Inhibitors by Molecular Simulation
Tu, G., Fu, T., Yang, F., Yang, J., Zhang, Z., Yao, X., Xue, W., & Zhu, F. (2021) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.1c00127 · Cited by 28 · 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-citationTIGA: target illumination GWAS analytics
Yang, J. J., Grissa, D., Lambert, C. G., Bologa, C., Mathias, S. L., Waller, A., Wild, D. J., Jensen, L. J., & Oprea, T. I. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab427 · Cited by 16 · see citing works
Self-citationExploring DrugCentral: from molecular structures to clinical effects
Halip, L., Avram, S. I., Curpan, R. F., et al. (2023) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-023-00529-x · Cited by 14 · see citing works
Self-citationRu, Z., Wu, Y., Shao, J., Yin, J., Qian, L., & Miao, X. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad271 · Cited by 6 · see citing works
Temporal trends in evidence supporting novel drug target discovery
Falaguera, M. J., McDonagh, E. M., Ochoa, D., Rusina, P. V., Roldán‐Romero, J. M., Hulcoop, D. G., Leach, A. R., & Dunham, I. (2025) · Nature Communications · doi:10.1038/s41467-025-67180-y · Cited by 6 · see citing works
Falaguera, M. J., McDonagh, E. M., Ochoa, D., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-5669559/v1 · Cited by 1 · see citing works
TIGA: Target illumination GWAS analyticsPreprint
Yang, J. J., Grissa, D., Lambert, C. G., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.11.378596
“TDL is a knowledge-based classification that bins human proteins into four categories: Tclin, mechanism-of-action designated targets via which approved drugs act (Santos et al., 2017; Ursu et al., 2019; Avram et al., 2020); Tchem are proteins known to bind small molecules with high potency; Tbio includes proteins that…”
BackgroundSelf-citationCNS Drug Discovery in “The Century of Biology”
Doller, D. (2025) · doi:10.1002/9783527844678.ch1
The evolving landscape of drug targets
Halip, L., Avram, S. I., Overington, J. P., Al‐Lazikani, B., Curpan, R. F., Sirimulla, S., Pushechnikov, A. O., Beccari, A. R., Savchuk, N., & Oprea, T. I. (2026) · Nature Reviews Drug Discovery · doi:10.1038/s41573-026-01530-3
Self-citationTemporally Validated Models for Target “Tclin-likeness”
Quazi, M. A., Sirimulla, S., Bologa, C., Pushechnikov, A. O., Farley, B., Savchuk, N., & Oprea, T. I. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c03088
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-citation
- Authors
- Avram, S., Halip, L., Curpan, R., & Oprea, T. I.
- Affiliations
- University of New Mexico (US)
- Corresponding author
- Oprea, Tudor I.
- Venue
- Nature Reviews Drug Discovery
- Published
- 2020-04-09
- Volume
- 19
- Issue
- 5
- Pages
- 300-300
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- OpenAlex
- W3016161809
- Cited by
- 17
- References
- 0
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2 · 2023: 2 · 2022: 2 · 2021: 4 · 2020: 3
- FWCI
- 1.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Reviews Drug Discovery (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Reproductive Medicine › Science, Research, and Medicine
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Keywords
- therapeutic target, drug target identification, drug discovery
- MeSH terms
- Humans, United States, United States Food and Drug Administration, Drug Approval, Rare Diseases, Small Molecule Libraries, Drug Discovery, Molecular Targeted Therapy
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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.
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