SuperTarget and Matador: resources for exploring drug-target relationships
Open accessAbstract
The molecular basis of drug action is often not well understood. This is partly because the very abundant and diverse information generated in the past decades on drugs is hidden in millions of medical articles or textbooks. Therefore, we developed a one-stop data warehouse, SuperTarget that integrates drug-related information about medical indication areas, adverse drug effects, drug metabolization, pathways and Gene Ontology terms of the target proteins. An easy-to-use query interface enables the user to pose complex queries, for example to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target the same protein but are metabolized by different enzymes. Furthermore, we provide tools for 2D drug screening and sequence comparison of the targets. The database contains more than 2500 target proteins, which are annotated with about 7300 relations to 1500 drugs; the vast majority of entries have pointers to the respective literature source. A subset of these drugs has been annotated with additional binding information and indirect interactions and is available as a separate resource called Matador. SuperTarget and Matador are available at http://insilico.charite.de/supertarget and http://matador.embl.de.
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MATADOR enrichment for 426 MAGMA genes identified 10 drug annotations with FDR below 0.05, including four estrogen receptor agonists or anti-estrogens and nicotine, cocaine, cyclothiazide, felbamate, and riluzole.
“This analysis identified 10 drug annotations with FDR < 0.05 including four drugs that are either estrogen receptor agonists (diethylstilbestrol, Implanon [etonogestril implant]), or anti-estrogens (tamoxifen and raloxifene); in addition to nicotine, cocaine, cyclothiazide, felbamate, and riluzole.”
From Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions (2021) · cites this paper as [19]
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MethodProTox: a web server for the in silico prediction of rodent oral toxicity
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“Recently added drug-target interactions to the SuperTarget database (Gunther et al. , 2008) and a literature search immediately allowed us to confirm at least 4 of the 10 most strongly-predicted drug-target interactions for the nuclear receptor data set obtained using our method.”
BackgroundMethodResultInfluentialIn silico methods and tools for drug discovery
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MethodElucidating Compound Mechanism of Action by Network Perturbation Analysis
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Improving compound–protein interaction prediction by building up highly credible negative samples
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“In addition, the disease-drug-target databases such as SuperTarget, Matador [46], DrugBank [47], andTherapeutic Target Database [48] are also used for drug-target research on herbal compounds.”
BackgroundInformation retrieval and text mining technologies for chemistry
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“MATADOR [37] http://matador.embl.de/ A database of protein-chemical interactions including as many direct and indirect interactions as possible.”
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INDI: a computational framework for inferring drug interactions and their associated recommendations
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STITCH 2: an interaction network database for small molecules and proteins
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Self-citationSTITCH 3: zooming in on protein-chemical interactions
Kuhn, M., Szklarczyk, D., Franceschini, A., von Mering, C., Jensen, L. J., & Peer Bork, B. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr1011 · Cited by 275 · see citing works
Self-citationYe, Q., Hsieh, C.-Y., Yang, Z., Kang, Y., Chen, J., Cao, D., He, S., & Hou, T. (2021) · Nature Communications · doi:10.1038/s41467-021-27137-3 · Cited by 261 · see citing works
Drug repositioning for personalized medicine
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“A few drug-target databases focusing on approved drugs include DrugBank, Kyoto Encyclopedia of Genes and Genomes (KEGG) Drug, the Therapeutic Target Database , and Matador [55,88-90] .”
BackgroundPredicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile
van Laarhoven, T., & Marchiori, E. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066952 · Cited by 246 · see citing works
“Drug-target interaction information was retrieved from the KEGG BRITE [6], BRENDA [10], SuperTarget [9] and DrugBank [7] databases.”
MethodA multiple kernel learning algorithm for drug-target interaction prediction
do Nascimento, A. C. A., Prudêncio, R. B. C., & Costa, I. G. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-0890-3 · Cited by 235 · see citing works
“We estimate the most highly ranked drug–target pairs as most likely true interactions, and performed a search on the current release of four major databases (DrugBank [39], MATA-DOR [38], KEGG [57]) and ChEMBL [58].”
BackgroundMethodOlayan, R. S., Ashoor, H., & Bajić, V. B. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx731 · Cited by 231 · see citing works
Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling
Zhao, L., Ciallella, H. L., Aleksunes, L. M., & Zhu, H. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.07.005 · Cited by 237 · see citing works
“…[51], AICD [52], and e-Drug3D [53]); (iii) collections of drug targets, including genomics and proteomics data (e.g., BindingDB [54], Supertarget [55], and Ligand Expo [56]); (iv) databases storing biological data obtained from assay screening, metabolism, and efficacy studies (e.g., HMDB [57],…”
BackgroundIn silico methods to address polypharmacology: current status, applications and future perspectives
Lavecchia, A., & Cerchia, C. (2015) · Drug Discovery Today · doi:10.1016/j.drudis.2015.12.007 · Cited by 228 · see citing works
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“In the gold standard dataset, drug-target interactions information has been obtained from KEGG BRITE [53], BRENDA [54], Super-Target [55] and DrugBank [56] databases.”
MethodDiscovering protein drug targets using knowledge graph embeddings
Mohamed, S. K., Nováček, V., & Nounu, A. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz600 · Cited by 227 · see citing works
“The Yamanishi_08 dataset is a collection of known drug target interactions gathered from different sources like KEGG BRITE (Kanehisa and Goto 2006), BRENDA (Schomburg et al. 2004), SuperTarget (Günther et al. 2007), and DrugBank (Wishart et al. 2008).”
PROMISCUOUS: a database for network-based drug-repositioning
von Eichborn, J., Murgueitio, M. S., Dunkel, M., Koerner, S., Bourne, P. E., & Preißner, R. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1037 · Cited by 225 · see citing works
“To provide a comprehensive dataset of drug–target relations, PROMISCUOUS integrates drug–target relations from the extensive databases DrugBank (23), SuperTarget (24) and SuperCyp (25), as well as newly explored ones as follows.”
BackgroundSelf-citationChen, B., Dong, X., Jiao, D., Wang, H., Zhu, Q., Ding, Y., & Wild, D. J. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-255 · Cited by 222 · see citing works
“We have created a single repository called Chem2Bio2RDF by aggregating data from multiple repositories including PubChem Bioassay [28], DrugBank [29], KEGG Ligand [30], CTD [31], BindingDB [32], PharmGKB [33], MATADOR [34], and a number of small QSAR sets available on the web [35].”
MethodCombining Drug and Gene Similarity Measures for Drug-Target Elucidation
Perlman, L., Gottlieb, A., Atias, N., Ruppin, E., & Sharan, R. (2011) · Journal of Computational Biology · doi:10.1089/cmb.2010.0213 · Cited by 218 · see citing works
“To validate these predictions we compared the predicted associations to those reported in two other sources of drug-target interactions that were not used in the learning process: The Therapeutic Target Database (TTD) [21] and the Matador database [22].”
SuperTarget goes quantitative: update on drug-target interactions
Hecker, N., Ahmed, J., von Eichborn, J., Dunkel, M., Macha, K., Eckert, A. P., Gilson, M. K., Bourne, P. E., & Preißner, R. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr912 · Cited by 213 · see citing works
“In 2008, the first version of SuperTarget was developed with the intention to accentuate drug–target interactions themselves and to provide references to other resources for more elaborate analysis (15).”
BackgroundSelf-citationPredicting drug-target interactions using restricted Boltzmann machines
Wang, Y., & Zeng, J. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt234 · Cited by 211 · see citing works
Relating drug–protein interaction network with drug side effects
Mizutani, S., Pauwels, E., Stoven, V., Goto, S., & Yamanishi, Y. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts383 · Cited by 211 · see citing works
MBROLE 2.0—functional enrichment of chemical compounds
López-Ibáñez, J., Pazos, F., & Chagoyen, M. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw253 · Cited by 210 · see citing works
“coli Metabolome Database (ECMDB) (21), pathway and metabolic reaction databases such as the BioCyc Database Collection (22), Rhea (23) and UniPathway (24), and resources centred on specific classes of compounds including lipids, such as the LIPID MAPS Structure Database (LMSD) (25), and drugs, such as the Comparative…”
BackgroundChu, Y., Kaushik, A. C., Wang, X., Wang, W., Zhang, Y., Shan, X., Salahub, D. R., Xiong, Y., & Wei, D. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz152 · Cited by 206 · see citing works
“These data sets contain known human DTIs retrieved from the KEGG BRITE [98], BRENDA [99], SuperTarget [100], and DrugBank [101] databases.”
MethodMolecular networks for the study of TCM Pharmacology
Zhao, J., Jiang, P., & Zhang, W. (2009) · Briefings in Bioinformatics · doi:10.1093/bib/bbp063 · Cited by 205 · see citing works
“The DrugBank database [64, 65], Therapeutic Target Database (TTD) [66, 67], SuperTarget [68, 69], Matador [68, 70], and Potential Drug Target Database (PDTD) [71, 72] have collected known information of drug targets.”
BackgroundIn Silico Discovery of Candidate Drugs against Covid-19
Cava, C., Bertoli, G. R., & Castiglioni, I. (2020) · Viruses · doi:10.3390/v12040404 · Cited by 207 · see citing works
“The interaction between the drug and protein was obtained using the Matador and DGIdb database [29,30].”
MethodXie, L., Xie, L., Kinnings, S., & Bourne, P. E. (2011) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010611-134630 · Cited by 198 · see citing works
Self-citationGeneration of a p16 Reporter Mouse and Its Use to Characterize and Target p16high Cells In Vivo
Omori, S., Wang, T.-W., Johmura, Y., Kanai, T., Nakano, Y., Kido, T., Susaki, E. A., Nakajima, T., Shichino, S., Ueha, S., et al. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.09.006 · Cited by 194 · see citing works
Biomedical data and computational models for drug repositioning: a comprehensive review
Luo, H., Li, M., Yang, M., Wu, F., Li, Y., & Wang, J. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz176 · Cited by 199 · see citing works
Identification of drug-target interactions via multiple information integration
Ding, Y., Tang, J., & Guo, F. (2017) · Information Sciences · doi:10.1016/j.ins.2017.08.045 · Cited by 195 · see citing works
Targeting breast cancer stem cells
McDermott, S., & Wicha, M. S. (2010) · Molecular Oncology · doi:10.1016/j.molonc.2010.06.005 · Cited by 192 · see citing works
An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation
Vo, A. H., Vleet, T. R. V., Gupta, R. R., Liguori, M. J., & Rao, M. S. (2019) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.9b00227 · Cited by 191 · see citing works
SuperPred: drug classification and target prediction
Dunkel, M., Günther, S., Ahmed, J., Wittig, B., & Preißner, R. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn307 · Cited by 191 · see citing works
“The calculated fingerprints from the 2500 compounds of the SuperDrug database were used for a further structural screening against the SuperTarget database ( 17 ).”
BackgroundMethodSelf-citationWang, L., You, Z., Chen, X., Xia, S., Liu, F., Yan, X., Zhou, Y. Z., & Song, K. (2017) · Journal of Computational Biology · doi:10.1089/cmb.2017.0135 · Cited by 188 · see citing works
“In the experiments, we used the data about the interactions between drugs and target proteins from SuperTarget (Gunther et al., 2008), DrugBank (Wishart et al., 2008), KEGG BRITE (Kanehisa et al., 2006), and BRENDA (Schomburg et al., 2004) databases collected by Yamanishi et al. (2008).”
A comprehensive review of feature based methods for drug target interaction prediction
Sachdev, K., & Gupta, M. K. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103159 · Cited by 188 · see citing works
Graph neural network approaches for drug-target interactions
Zhang, Z., Chen, L., Zhong, F., Wang, D., Jiang, J., Zhang, S., Jiang, H., Zheng, M., & Li, X. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.102327 · Cited by 184 · see citing works
Microbial-host-isozyme analyses reveal microbial DPP4 as a potential antidiabetic target
Wang, K., Zhang, Z., Hang, J., Liu, J., Guo, F., Ding, Y., Li, M., Nie, Q., Lin, J., Zhuo, Y., et al. (2023) · Science · doi:10.1126/science.add5787 · Cited by 178 · see citing works
“(E) Activity of hDPP4 and nine active microbial DPP4 homologs against GLP-1(7-37), n = 3 biological replicates per group.”
BackgroundInfluentialTang, J. P., & Aittokallio, T. A. (2014) · Current Pharmaceutical Design · doi:10.2174/13816128113199990470 · Cited by 184 · see citing works
“DrugBank [130], Therapeutic Target Database [131], SuperTarget [132] and Matador [133] provide additional resources related to drug-target interactions by text mining the literature.”
BackgroundAssessing Drug Target Association Using Semantic Linked Data
Chen, B., Ding, Y., & Wild, D. J. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002574 · Cited by 166 · see citing works
“In addition, we examined drug target pairs from MATADOR”
MethodDocking-based inverse virtual screening: methods, applications, and challenges
Xu, X., Huang, M., & Zou, X. (2018) · Biophysics Reports · doi:10.1007/s41048-017-0045-8 · Cited by 164 · see citing works
“…directly derived from a protein–ligand complex structure database like sc-PDB, or constructed by collecting information from publically accessible drug–target databases such as SuperTarget (Gu¨nther et al. 2008), BindingDB (Liu et al. 2007), and DrugBank (Wishart et al. 2006), as listed in Table 1.”
Wang, Y., Chen, S., Deng, N.-Y., & Wang, Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0078518 · Cited by 164 · see citing works
“The high-quality drug-target interactions can be manually constructed from the KEGG BRITE [16], BRENDA [17], SuperTarget [18], and DrugBank [19].”
Structure-based systems biology for analyzing off-target binding
Xie, L., Xie, L., & Bourne, P. E. (2011) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2011.01.004 · Cited by 152 · see citing works
“As shown in Figure 2, among 6,127 drugs or drug candidates in the SuperTarget database [27], around 15% and 60% of them have molecular fingerprint similarity scores larger than 90 and 70, respectively, to ligands deposited in PDB. Comprehensive coverage by structures and ligands provides an…”
BackgroundSelf-citationWang, Y., You, Z., Yang, S., Yi, H.-C., Chen, Z., & Zheng, K. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-1052-0 · Cited by 152 · see citing works
Quantum machine learning for chemistry and physics
Sajjan, M., Li, J., Selvarajan, R., Sureshbabu, S. H., Kale, S. S., Gupta, R., Singh, V., & Kais, S. (2022) · Chemical Society Reviews · doi:10.1039/d2cs00203e · Cited by 157 · see citing works
Ding, Y., Tang, J., & Guo, F. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106254 · Cited by 147 · see citing works
Role of systems pharmacology in understanding drug adverse events
Berger, S., & Iyengar, R. (2010) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.114 · Cited by 145 · see citing works
Drug Side-Effect Prediction Based on the Integration of Chemical and Biological Spaces
Yamanishi, Y., Pauwels, E., & Kotera, M. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci2005548 · Cited by 144 · see citing works
Thafar, M. A., Olayan, R. S., Ashoor, H., Albaradei, S., Bajic, V. B., Gao, X., Gojobori, T., & Essack, M. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00447-2 · Cited by 143 · see citing works
“…used biomedical literature and several reference databases, including KEGG [81, 82], DrugBank [83, 84], PubChem [85, 86], CheMBLE [87– 89], MATADOR [90], SuperTarget [90], Comparative Toxicogenomics Database (CTD) [91, 92], and the annotated database of common toxins and their targets (T3DB) [93].”
MethodOld friends in new guise: repositioning of known drugs with structural bioinformatics
Haupt, V. J., & Schroeder, M. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr011 · Cited by 142 · see citing works
“freely available online [25, 51–54] together with a”
BackgroundComprehensive Survey of Recent Drug Discovery Using Deep Learning
Kim, J., Park, S., Min, D., & Kim, W. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22189983 · Cited by 141 · see citing works
“SuperTarget/MATADOR [218] 8936 1799 719 2008 http://matador.”
Kell, D. B., Dobson, P. D., Bilsland, E., & Oliver, S. G. (2012) · Drug Discovery Today · doi:10.1016/j.drudis.2012.11.008 · Cited by 141 · see citing works
Öztürk, H., Özkırımlı, E., & Özgür, A. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-0977-x · Cited by 140 · see citing works
“Yamanishi et al. retrieved the interaction information between target proteins and compounds from the KEGG BRITE [34], BRENDA [35], SuperTarget [36], and DrugBank [37] databases.”
MDAD: A Special Resource for Microbe-Drug Associations
Sun, Y., Zhang, D., Cai, S., Ming, Z., Li, J., & Chen, X. (2018) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2018.00424 · Cited by 138 · see citing works
“We browsed almost all drug databases, including DrugBank (Law et al., 2014), TTD (Qin et al., 2014), SuperTarget (Hecker et al., 2012), MATADOR (Gunther et al., 2008), STITCH (Kuhn et al., 2014), TDR targets (Magarinos et al., 2012), PDTD (Gao et al., 2008), ChEMBL (Gaulton et al., 2012), aBiofilm (Rajput et al., 2018…”
MethodDuffy, S., Sykes, M. L., Jones, A. J., Shelper, T., Simpson, M. M., Lang, R., Poulsen, S., Sleebs, B. E., & Avery, V. M. (2017) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.00379-17 · Cited by 137 · see citing works
Drug databases and their contributions to drug repurposing
Masoudi-Sobhanzadeh, Y., Omidi, Y., Amanlou, M., & Masoudi-Nejad, A. (2020) · Genomics · doi:10.1016/j.ygeno.2019.06.021 · Cited by 131 · see citing works
Kim, H., Kim, E., Lee, I., Bae, B., Park, M., & Nam, H. (2020) · Biotechnology and Bioprocess Engineering · doi:10.1007/s12257-020-0049-y · Cited by 138 · see citing works
“MATADOR [317] Integrated drug-related information about medical indication areas, adverse drug effects, drug metabolization, pathways, and gene ontology terms of target proteins 2.”
BackgroundInfluentialMachine learning methods, databases and tools for drug combination prediction
Wu, L., Wen, Y., Leng, D., Zhang, Q., Dai, C., Wang, Z., Liu, Z., Yan, B., Zhang, Y., Wang, J., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab355 · Cited by 131 · see citing works
Metabolomics in pesticide research and development: review and future perspectives
Aliferis, K. A., & Chrysayi‐Tokousbalides, M. (2010) · Metabolomics · doi:10.1007/s11306-010-0231-x · Cited by 126 · see citing works
Virtual Screening Techniques in Drug Discovery: Review and Recent Applications
Rocha, S. F. L. D. S., Olanda, C. G., Fokoue, H. H., & Sant’Anna, C. M. R. (2019) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026619666190816101948 · Cited by 124 · see citing works
Network-Based Drug-Target Interaction Prediction with Probabilistic Soft Logic
Fakhraei, S., Huang, B., Raschid, L., & Getoor, L. C. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2325031 · Cited by 120 · see citing works
Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design
Ehrt, C., Brinkjost, T., & Koch, O. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b00078 · Cited by 122 · see citing works
Parisi, D., Adasme, M. F., Sveshnikova, A. N., Bolz, S. N., Moreau, Y., & Schroeder, M. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.04.004 · Cited by 122 · see citing works
“Examples of such databases are the Therapeutic Targets Database [55], Drugbank[56], SuperTarget [57], and the IUPHAR/BPS Guide [58].”
BackgroundSystematic identification of proteins that elicit drug side effects
Kuhn, M., Banchaabouchi, M. A., Campillos, M., Jensen, L. J., Gross, C. T., Gavin, A., & Peer Bork, B. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.10 · Cited by 117 · see citing works
Self-citationComputational polypharmacology: a new paradigm for drug discovery
Chaudhari, R. R., Tan, Z., Huang, B., & Zhang, S. (2017) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2017.1280024 · Cited by 120 · see citing works
“In recent years, polypharmacology databases have been developed including STITCH [11], Polypharma [12], SuperTarget [13], IBIS [14], and SIDER [15].”
Method
- Authors
- Gunther, S., Kuhn, M., Dunkel, M., Campillos, M., Senger, C., Petsalaki, E., Ahmed, J., Urdiales, E. G., Gewiess, A., Jensen, L. J., Schneider, R., Skoblo, R., Russell, R. B., Bourne, P. E., Bork, P., & Preissner, R.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Preissner, R.
- ORCID
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-12-23
- Volume
- 36
- Issue
- Database
- Pages
- D919-D922
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC2238858
- OpenAlex
- W2108069034
- Cited by
- 647
- References
- 18
- Citations by year
- 2026: 13 · 2025: 34 · 2024: 33 · 2023: 42 · 2022: 57 · 2021: 73 · 2020: 57 · 2019: 48 · 2018: 43 · 2017: 40 · 2016: 24 · 2015: 36 · 2014: 22 · 2013: 28 · 2012: 30
- FWCI
- 16.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by-nc-sa)
- Funders
- Deutsche Forschungsgemeinschaft (SFB 449)
- Bundesministerium für Bildung und Forschung (SFB 449)
- Deutsche Krebshilfe
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- drug-target interactions, matadors, cytochrome P450, drug pathways, Gene Ontology, adverse drug reactions, drug screening, protein sequence comparison, literature annotation
- MeSH terms
- Pharmaceutical Preparations, Pharmacology, Proteins, User-Computer Interface, Drug Design, Databases, Factual, Drug Delivery Systems, Internet
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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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