DrugCentral 2021 supports drug discovery and repositioning
Open accessAbstract
DrugCentral is a public resource (http://drugcentral.org) that serves the scientific community by providing up-to-date drug information, as described in previous papers. The current release includes 109 newly approved (October 2018 through March 2020) active pharmaceutical ingredients in the US, Europe, Japan and other countries; and two molecular entities (e.g. mefuparib) of interest for COVID19. New additions include a set of pharmacokinetic properties for ∼1000 drugs, and a sex-based separation of side effects, processed from FAERS (FDA Adverse Event Reporting System); as well as a drug repositioning prioritization scheme based on the market availability and intellectual property rights forFDA approved drugs. In the context of the COVID19 pandemic, we also incorporated REDIAL-2020, a machine learning platform that estimates anti-SARS-CoV-2 activities, as well as the ‘drugs in news’ feature offers a brief enumeration of the most interesting drugs at the present moment. The full database dump and data files are available for download from the DrugCentral web portal.
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?
The gold standard labels for evaluating TxGNN come from a previously curated medical KG consisting of 9,388 indications and 30,675 contraindications.
“The gold standard labels for evaluating such a model come from our previously curated medical KG9 (Fig. 1d and Supplementary Tables 4 and 5), which consists of 9,388 indications and 30,675 contraindications23.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [23]
DrugCentral 2023 adds 285 new drugs to the 2021 publication.
“The 2023 version of DrugCentral adds 285 new drugs to the 2021 publication (3).”
From DrugCentral 2023 extends human clinical data and integrates veterinary drugs (2023) · cites this paper as [3]
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
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · see citing works
Scikit-learn: Machine Learning in Python
(2011) · Journal of Machine Learning Research · doi:10.5555/1953048.2078195 · Cited by 8,539 · see citing works
UniProt: a worldwide hub of protein knowledge
The UniProt Consortium (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1049 · Cited by 7,698 · see citing works
A comprehensive map of molecular drug targets
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., et al. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.230 · Cited by 2,427 · see citing works
Self-citationChEMBL: towards direct deposition of bioassay data
Mendez, D., Gaulton, A., Bento, A. P., Chambers, J., De Veij, M., Félix, E., Magariños, M. P., Mosquera, J. F., Mutowo, P., Nowotka, M., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1075 · Cited by 2,372 · see citing works
New approach for understanding genome variations in KEGG
Kanehisa, M. I., Sato, Y., Furumichi, M., Morishima, K., & Tanabe, M. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky962 · Cited by 1,845 · see citing works
ChEBI in 2016: Improved services and an expanding collection of metabolites
Hastings, J., Owen, G., Dekker, A., Ennis, M., Kale, N., Muthukrishnan, V., Turner, S., Swainston, N., Mendes, P., & Steinbeck, C. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1031 · Cited by 1,083 · see citing works
Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Himmelstein, D. S., Lizee, A., Hessler, C., Brueggeman, L., Chen, S. L., Hadley, D., Green, A., Khankhanian, P., & Baranzini, S. E. (2017) · eLife · doi:10.7554/elife.26726 · Cited by 666 · see citing works
BDDCS Applied to Over 900 Drugs
Benet, L. Z., Broccatelli, F., & Oprea, T. I. (2011) · The AAPS Journal · doi:10.1208/s12248-011-9290-9 · Cited by 636 · see citing works
Self-citationObach, R. S., Lombardo, F., & Waters, N. J. (2008) · Drug Metabolism and Disposition · doi:10.1124/dmd.108.020479 · Cited by 461 · see citing works
The Role of Transporters in the Pharmacokinetics of Orally Administered Drugs
Shugarts, S. B., & Benet, L. Z. (2009) · Pharmaceutical Research · doi:10.1007/s11095-009-9924-0 · Cited by 439 · see citing works
THE CONCISE GUIDE TO PHARMACOLOGY 2019/20: Introduction and Other Protein Targets
Alexander, S. P. H., Kelly, E., Mathie, A. A., Peters, J. A., Veale, E. L., Armstrong, J. F., Faccenda, E., Harding, S. D., Pawson, A. J., Sharman, J. L., et al. (2019) · British Journal of Pharmacology · doi:10.1111/bph.14747 · Cited by 423 · see citing works
Favipiravir: A new and emerging antiviral option in COVID-19
Agrawal, U., Raju, R. S., & Udwadia, Z. (2020) · Medical Journal Armed Forces India · doi:10.1016/j.mjafi.2020.08.004 · Cited by 390 · 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 · cited 1× · see citing works
“Our knowledge-based protein classification (19) bins human proteins into four categories, according to their ‘target 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…”
BackgroundSelf-citationTherapies 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-citationA standard database for drug repositioning
Brown, A. S., & Patel, C. J. (2017) · Scientific Data · doi:10.1038/sdata.2017.29 · Cited by 308 · see citing works
Erratum: Unexplored therapeutic opportunities in the human genome
Oprea, T. I., Bologa, C., 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.2018.52 · Cited by 304 · see citing works
Self-citationPharos: 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-citationSex bias in trials and treatment must end
Kim, A. M., Tingen, C. M., & Woodruff, T. K. (2010) · Nature · doi:10.1038/465688a · Cited by 254 · see citing works
Identification of SARS-CoV-2 3CL Protease Inhibitors by a Quantitative High-Throughput Screening
Zhu, W. J., Xu, M., Chen, C. Z., Guo, H., Shen, M., Hu, X., Shinn, P., Klumpp-Thomas, C. A., Michael, S., & Zheng, W. (2020) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.0c00108 · Cited by 222 · see citing works
The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text
Pafilis, E., Frankild, S. P., Fanini, L., Faulwetter, S., Pavloudi, C., Vasileiadou, A., Arvanitidis, C., & Jensen, L. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0065390 · Cited by 206 · see citing works
Lombardo, F., Berellini, G., & Obach, R. S. (2018) · Drug Metabolism and Disposition · doi:10.1124/dmd.118.082966 · Cited by 176 · see citing works
UniChem: a unified chemical structure cross-referencing and identifier tracking system
Chambers, J. K., Davies, M., Gaulton, A., Hersey, A., Velankar, S. S., Petryszak, R., Hastings, J., Bellis, L. J., McGlinchey, S., & Overington, J. P. (2013) · Journal of Cheminformatics · doi:10.1186/1758-2946-5-3 · Cited by 168 · see citing works
Systematic Analysis of Adverse Event Reports for Sex Differences in Adverse Drug Events
Yu, Y., Chen, J., Li, D., Wang, L., Wang, W., & Liu, H. (2016) · Scientific Reports · doi:10.1038/srep24955 · Cited by 162 · see citing works
TCRD 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-citationde Vries, S. T., Denig, P., Ekhart, C., Burgers, J. S., Kleefstra, N., Mol, P. G. M., & van Puijenbroek, E. (2019) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.13923 · Cited by 152 · see citing works
Ursu, O., Holmes, J., Bologa, C., Yang, J. J., Mathias, S. L., Stathias, V., Nguyễn, Ð.-T., Schürer, S. C., & Oprea, T. I. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky963 · Cited by 144 · see citing works
Self-citationAntibodypedia, a Portal for Sharing Antibody and Antigen Validation Data*S
Björling, E., & Uhlén, M. (2008) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m800264-mcp200 · Cited by 143 · see citing works
Kim, M. T., Sedykh, A., Chakravarti, S., Saiakhov, R., & Zhu, H. (2013) · Pharmaceutical Research · doi:10.1007/s11095-013-1222-1 · Cited by 127 · see citing works
Computational and Practical Aspects of Drug Repositioning
Oprea, T. I., & Overington, J. P. (2015) · Assay and Drug Development Technologies · doi:10.1089/adt.2015.29011.tiodrrr · Cited by 114 · see citing works
Self-citationA Likelihood Ratio Test Based Method for Signal Detection With Application to FDA’s Drug Safety Data
Huang, L., Zalkikar, J. N., & Tiwari, R. C. (2011) · Journal of the American Statistical Association · doi:10.1198/jasa.2011.ap10243 · Cited by 101 · see citing works
Drug Repurposing Screen for Compounds Inhibiting the Cytopathic Effect of SARS-CoV-2
Chen, C. Z., Shinn, P., Itkin, Z., Eastman, R. T., Bostwick, R. J., Rasmussen, L. A., Huang, R., Shen, M., Hu, X., Wilson, K. M., et al. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.592737 · Cited by 96 · see citing works
Contrera, J. F., Matthews, E. J., Kruhlak, N. L., & Benz, R. D. (2004) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2004.08.004 · Cited by 90 · see citing works
Brimacombe, K. R., Scully, P. C., Zhao, T., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.04.135046 · Cited by 90 · see citing works
Hanson, Q., Wilson, K. M., Shen, M., Itkin, Z., Eastman, R. T., Shinn, P., & Hall, M. D. (2020) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.0c00161 · Cited by 81 · see citing works
Artificial intelligence, drug repurposing and peer review
Levin, J. M., Oprea, T. I., Davidovich, S., Clozel, T., Overington, J. P., Vanhaelen, Q., Cantor, C. R., Bischof, E., & Zhavoronkov, A. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0686-x · Cited by 79 · see citing works
Self-citationImproving the prediction of the brain disposition for orally administered drugs using BDDCS
Broccatelli, F., Larregieu, C. A., Cruciani, G., Oprea, T. I., & Benet, L. Z. (2011) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2011.12.008 · Cited by 76 · see citing works
Self-citationHosey, C. M., Chan, R., & Benet, L. Z. (2015) · The AAPS Journal · doi:10.1208/s12248-015-9845-2 · Cited by 72 · see citing works
Exploring the dark genome: implications for precision medicine
Oprea, T. I. (2019) · Mammalian Genome · doi:10.1007/s00335-019-09809-0 · Cited by 50 · see citing works
Self-citationAguirre‐Plans, J., Piñero, J., Sanz, F., Furlong, L. I., Fernández‐Fuentes, N., Oliva, B., & Güney, E. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.02.027 · Cited by 43 · see citing works
Drug Repurposing Screen for Compounds Inhibiting the Cytopathic Effect of SARS-CoV-2Preprint
Chen, C. Z., Shinn, P., Itkin, Z., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.18.255877 · Cited by 30 · see citing works
Hanson, Q., Wilson, K. M., Shen, M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.16.154708 · Cited by 27 · see citing works
Medical Terminologies That Work: The Example of MeSH
Nelson, S. J. (2009) · International Symposium on Pervasive Systems, Algorithms, and Networks · doi:10.1109/i-span.2009.84 · Cited by 20 · see citing works
Ursu, O., Glick, M., & Oprea, T. I. (2019) · Nature Reviews Drug Discovery · doi:10.1038/d41573-019-00052-5 · Cited by 20 · see citing works
Self-citationAvram, 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-citationAvram, S., Halip, L., Curpan, R., & Oprea, T. I. (2020) · Nature Reviews Drug Discovery · doi:10.1038/d41573-020-00052-w · Cited by 17 · cited 1× · 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-citationDevelopment of an adverse drug event network to predict drug toxicity
Wu, Q., Taboureau, O., & Audouze, K. (2020) · Current Research in Toxicology · doi:10.1016/j.crtox.2020.06.001 · Cited by 13 · see citing works
Can BDDCS illuminate targets in drug design?
Bocci, G., Benet, L. Z., & Oprea, T. I. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.09.021 · Cited by 7 · see citing works
Self-citationTime-resolved evaluation of compound repositioning predictions on a text-mined knowledge network
Mayers, M., Li, T. S., Queralt-Rosiñach, N., & Su, A. I. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3297-0 · Cited by 6 · see citing works
REDIAL-2020: A suite of machine learning models to estimate Anti-SARS-CoV-2 activitiesPreprint
Govinda, K. C., Bocci, G., Verma, S., et al. (2020) · doi:10.21203/rs.3.rs-76894/v1 · Cited by 6 · see citing works
Self-citationAshar, B. H. (2017) · Medical Clinics of North America · doi:10.1016/j.mcna.2017.09.004 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Building a knowledge graph to enable precision medicine
Chandak, P., Huang, K., & Zitnik, M. (2023) · Scientific Data · doi:10.1038/s41597-023-01960-3 · Cited by 522 · see citing works
A foundation model for clinician-centered drug repurposing
Huang, K., Chandak, P., Wang, Q., Havaldar, S., Vaid, A., Leskovec, J., Nadkarni, G. N., Glicksberg, B. S., Gehlenborg, N., & Zitnik, M. (2024) · Nature Medicine · doi:10.1038/s41591-024-03233-x · Cited by 249 · see citing works
“1d and Supplementary Tables 4 and 5), which consists of 9,388 indications and 30,675 contraindications 23 .”
BackgroundA critical overview of computational approaches employed for COVID-19 drug discovery
Muratov, E. N., Amaro, R., Andrade, C. H., Brown, N., Ekins, S., Fourches, D., Isayev, O., Kozakov, D., Medina-Franco, J. L., Merz, K. M., et al. (2021) · Chemical Society Reviews · doi:10.1039/d0cs01065k · Cited by 206 · see citing works
Self-citationHarding, S. D., Armstrong, J. F., Faccenda, E., Southan, C., Alexander, S. P. H., Davenport, A. P., Pawson, A. J., Spedding, M., Davies, J. A., & NC-IUPHAR (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1010 · Cited by 149 · see citing works
Knowledge graphs and their applications in drug discovery
MacLean, F. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1910673 · Cited by 105 · 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-citationAlphaFold illuminates half of the dark human proteins
Binder, J. L., Berendzen, J., Stevens, A. O., He, Y., Wang, J., Dokholyan, N. V., & Oprea, T. I. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102372 · Cited by 81 · see citing works
Self-citationAntifungal activity of vitamin D3 against Candida albicans in vitro and in vivo
Lei, J., Xiao, W., Zhang, J., Liu, F., Xin, C., Zhou, B., Chen, W., & Song, Z. (2022) · Microbiological Research · doi:10.1016/j.micres.2022.127200 · Cited by 39 · see citing works
An ontology-based knowledge graph for representing interactions involving RNA molecules
Cavalleri, E., Cabri, A., Soto-Gomez, M., Bonfitto, S., Perlasca, P., Gliozzo, J., Callahan, T. J., Reese, J., Robinson, P. N., Casiraghi, E., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03673-7 · Cited by 33 · see citing works
Annotating off-label drug usage from unconventional sources
Avram, S., Halip, L., Curpan, R., Borota, A., Bora, A., & Oprea, T. I. (2022) · medRxiv · doi:10.1101/2022.09.08.22279709 · Cited by 4 · see citing works
Self-citation
- Authors
- Avram, S., Bologa, C. G., Holmes, J., Bocci, G., Wilson, T. B., Nguyen, D.-T., Curpan, R., Halip, L., Bora, A., Yang, J. J., Knockel, J., Sirimulla, S., Ursu, O., & Oprea, T. I.
- Affiliations
- Department of Computational Chemistry, “Coriolan Dragulescu’’ Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş, 300223, România
- University of New Mexico (US)
- University of New Mexico Health Sciences Center (US)
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
- The University of Texas at El Paso (US)
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- University of Copenhagen (DK)
- Corresponding author
- Oprea, Tudor I
- ORCID
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-11-05
- Volume
- 49
- Issue
- D1
- Pages
- D1160-D1169
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC7779058
- OpenAlex
- W3095380161
- Cited by
- 189
- References
- 53
- Citations by year
- 2026: 10 · 2025: 24 · 2024: 25 · 2023: 54 · 2022: 48 · 2021: 27 · 2020: 1
- FWCI
- 16.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- scholarworks - UTEP (The University of Texas at El Paso) (submittedVersion)
- Funders
- National Institutes of Health (CA224370)
- National Cancer Institute (P30 CA118100)
- NIH NCATS Clinical and Translational Science Center for UNM (UL1 TR001449)
- Intramural Research Program, Division of Preclinical Innovation, NIH NCATS
- National Science Foundation (NSF-PREM, DMR-1827745)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- drug information database, drug discovery, drug repurposing, pharmacokinetic properties, intellectual property, COVID-19 pandemic, SARS-CoV-2
- MeSH terms
- COVID-19, SARS-CoV-2, COVID-19 Drug Treatment, Antiviral Agents, Europe, Humans, Japan, United States, Information Storage and Retrieval, Drug Approval, Internet, Drug Discovery, Drug Repositioning, Epidemics, 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
