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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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“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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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 · cited 4×
“In a last step, relations from other databases, namely DrugBank (3), KEGG (9), PDB (10), SuperLigands (11) and TTD (4), were checked for drug-target interactions not identified with the preceding steps.”
BackgroundInfluentialThe Universal Protein Resource (UniProt)
Bairoch, A. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki070 · Cited by 3,774
From genomics to chemical genomics: new developments in KEGG
Kanehisa, M. I. (2005) · Nucleic Acids Research · doi:10.1093/nar/gkj102 · Cited by 3,126 · cited 2×
“Proven or potential target proteins are represented by sequences as stored in UniProt (12), by functional annotations extracted from GOA (13) and by related pathway information provided by KEGG (9) (compare Figure 1).”
Backgroundvon Mering, C. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki005 · Cited by 1,768 · cited 2×
“The most probable candidates were identified using a benchmarking scheme ( 8 ) and manually curated.”
MethodThe Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology
Camon, E. B. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh021 · Cited by 927 · cited 1×
“Proven or potential target proteins are represented by sequences as stored in UniProt (12), by functional annotations extracted from GOA (13) and by related pathway information provided by KEGG (9) (compare Figure 1).”
BackgroundDrugs, 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 850 · cited 1×
“For some drugs even the primary targets are still unknown, for example, Diloxanide, Niclosamide and Ambroxol are administered successfully although their effect on human metabolism is still not clarified at a molecular level (1).”
BackgroundDo Structurally Similar Molecules Have Similar Biological Activity?
Martin, Y. C., Kofron, J. L., & Traphagen, L. M. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm020155c · Cited by 806 · cited 1×
“Since compounds with high structural similarity frequently exhibit similar activities (14), all drugs with comparable structural fingerprints are made accessible.”
BackgroundTTD: Therapeutic Target Database
Chen, X. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.412 · Cited by 723 · cited 4×
“Another notable database is the Therapeutic Target Database (TTD) (4), which holds target information on about 1000 small molecule drugs.”
BackgroundInfluentialLiterature mining for the biologist: from information retrieval to biological discovery
Jensen, L. J., Šarić, J., & Peer Bork, B. (2006) · Nature Reviews Genetics · doi:10.1038/nrg1768 · Cited by 632 · cited 1×
“In parallel, potential drug-target relations were automatically extracted from Medline by searching for synonyms of drugs, proteins and Medical Subject Headings (MeSH terms) describing groups of proteins (7).”
MethodSelf-citationThe Universal Protein Resource (UniProt)
The UniProt Consortium (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl929 · Cited by 572
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Drug Repurposing - Hypothesis, Molecular Aspects and Therapeutic Applications
Badria, F. A.-E. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.83082 · Cited by 52
Current advances in ligand‐based target prediction
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Improving drug safety: From adverse drug reaction knowledge discovery to clinical implementation
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“Similar to MATADOR, but with extensive information such as pathways, ontologies and Cyp450s, SuperTarget [21] integrates drug related annotations for medical indication.”
BackgroundMethodQian, Y., Li, X., Wu, J., & Zhang, Q. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05447-1 · Cited by 50
“Their positive samples are obtained from the highest confidence biochemical databases: Drug-Bank [58] database and matador [59] database [56].”
BackgroundDrug knowledge bases and their applications in biomedical informatics research
Zhu, Y., Elemento, O., Pathak, J., & Wang, F. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx169 · Cited by 52
“SuperTarget [29, 30] is a database of drug–target relations that contains information on drugs, targets and side effects.”
BackgroundSurvey of Similarity-Based Prediction of Drug-Protein Interactions
Wang, C., & Kurgan, L. (2019) · Current Medicinal Chemistry · doi:10.2174/0929867326666190808154841 · Cited by 51
Shen, C., Ding, Y., Tang, J., Xu, X., & Guo, F. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18081781 · Cited by 48
“We utilized four well-known biological databases (including ChEMBL (C) [19], DrugBank (D) [18], KEGG (K) [15] and Matador (M) [20]) as references to verify whether or not the predicted new DTIs are true.”
MethodA survey on adverse drug reaction studies: data, tasks and machine learning methods
Nguyễn, Đ. A., Nguyen, C. H., & Mamitsuka, H. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz140 · Cited by 48
“…from FAERS with a criteria of removing bias data, DrugBank [20] PubChem [21] PDB [22] BindingDB [23] HPRD [24] CTD [25] KEGG [26] SuperTarget [27] ADReCS [28] DART [29] TTD [30] Bio2RDF [31] OFFSIDES for ADRs caused by single drugs and TWOSIDES for ADRs caused by combinations of two drugs…”
MethodLarge-Scale Prediction of Drug-Target Interaction: a Data-Centric Review
Cheng, T., Hao, M., Takeda, T., Bryant, S. H., & Wang, Y. (2017) · The AAPS Journal · doi:10.1208/s12248-017-0092-6 · Cited by 48
Identification of drug-target interactions via multi-view graph regularized link propagation model
Ding, Y., Tang, J., & Guo, F. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.100 · Cited by 47
Chemogenomic Approaches to Infer Drug–Target Interaction Networks
Yamanishi, Y. (2012) · Methods in molecular biology · doi:10.1007/978-1-62703-107-3_9 · Cited by 50
Drug-target interaction prediction using Multi Graph Regularized Nuclear Norm Minimization
Mongia, A., & Majumdar, A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0226484 · Cited by 46
“A lot of algorithms have been proposed for DTI (Drug-target interaction) prediction in recent years [1, 2], which use small number of experimentally validated interactions in existing databases such as ChEMBL [3], DrugBank [4], KEGG DRUG [5], STITCH [6] and SuperTarget [7].”
Zhao, B.-W., You, Z., Hu, L., Guo, Z.-H., Wang, L., Chen, Z., & Wong, L. (2021) · Cancers · doi:10.3390/cancers13092111 · Cited by 45
“The experiment showed that there were 3 targets of the drugs clozapine and risperidone predicted by LGDTI, which could be proved in the SuperTarget database [46].”
BackgroundA review of machine learning-based methods for predicting drug–target interactions
Shi, W., Yang, H. T., Xie, L., Yin, X.-X., & Zhang, Y. (2024) · Health Information Science and Systems · doi:10.1007/s13755-024-00287-6 · Cited by 50
A comprehensive review of computational techniques for the prediction of drug side effects
Sachdev, K., & Gupta, M. K. (2020) · Drug Development Research · doi:10.1002/ddr.21669 · Cited by 45
“Matador (Günther et al., 2007) The matador database consists of the different direct as well as indirect drug – protein interactions.”
BackgroundBayés‐Genís, A., Egea, O. I., Spitaleri, G., Domingo, M., Revuelta‐López, E., Codina, P., Cediel, G., Santiago‐Vacas, E., Cserkóová, A., Pascual-Figal, D. A., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-91546-z · Cited by 48
A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder
Wang, H., Wang, J., Dong, C., Lian, Y., Liu, D., & Yan, Z. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.01592 · Cited by 44
Ji, B., You, Z.-H., Jiang, H.-J., Guo, Z.-H., & Zheng, K. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02490-x · Cited by 44
Open-source chemogenomic data-driven algorithms for predicting drug–target interactions
Hao, M., Bryant, S. H., & Wang, Y. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby010 · Cited by 40
Large-Scale Prediction of Beneficial Drug Combinations Using Drug Efficacy and Target Profiles
Iwata, H., Sawada, R., Mizutani, S., Kotera, M., & Yamanishi, Y. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00444 · Cited by 39
Lee, H., & Kim, W. (2019) · Pharmaceutics · doi:10.3390/pharmaceutics11080377 · Cited by 39
Yang, Y., & He, S. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04327-w · Cited by 39
“…study, we can acquire drug-target pairs with the highest (top 5) probability scores predicted by the RF classifier on each dataset and search for the relevant evidence from six external databases (KEGG (K) [42], DrugBank (D) [34], Matador (M) [43], ChEMBL (C) [44], T3DB (T) [45], and CTD [46]).”
BackgroundMBROLE3: improved functional enrichment of chemical compounds for metabolomics data analysis
López-Ibáñez, J., Pazos, F., & Chagoyen, M. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad405 · Cited by 38
Wang, Y., Fang, J., & Chen, S. (2016) · Scientific Reports · doi:10.1038/srep32679 · Cited by 37
Drug-Target Interaction Prediction Based on Drug Fingerprint Information and Protein Sequence
Li, Y., Huang, Y., You, Z., Li, L., & Wang, Z. (2019) · Molecules · doi:10.3390/molecules24162999 · Cited by 38
“These datasets are collected from four databases, KEGG BRITE [7], SuperTarget [4], BRENDA [24], and DrugBank [5].”
MethodReview of unsupervised pretraining strategies for molecules representation
Yu, L., Su, Y., Liu, Y., & Zeng, X. (2021) · Briefings in Functional Genomics · doi:10.1093/bfgp/elab036 · Cited by 40
DrugQuest - a text mining workflow for drug association discovery
Papanikolaou, N., Pavlopoulos, G. A., Θεοδοσίου, Θ., Vizirianakis, I. S., & Iliopoulos, I. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1041-6 · Cited by 37
“SuperTarget/Mata-dor [8] is designed to give answers to complex queries such as finding drugs that are metabolized by the same enzyme, drugs that target a certain metabolic pathway or even drugs that target the same protein but are metabolized by different enzymes.”
BackgroundZhang, Y., Jiang, Z., Chen, C., Wei, Q., Gu, H., & Yu, B. (2021) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-021-00488-7 · Cited by 37
Literature analysis for systematic drug repurposing: a case study from Biovista
Lekka, E., Deftereos, S., Persidis, A., Persidis, A., & Andronis, C. (2011) · Drug Discovery Today Therapeutic Strategies · doi:10.1016/j.ddstr.2011.06.005 · Cited by 37
Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction
Mayr, F., Möller, G., Garscha, U., Fischer, J., Castaño, P. R., Inderbinen, S. G., Temml, V., Waltenberger, B., Schwaiger, S., Hartmann, R. W., et al. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21197102 · Cited by 40
“Bioactivities that were used to build reference target sets in SP were derived from ChEMBL [65], Binding DB [66], and SuperTarget [67,68].”
MethodA Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds
Yoo, S., Yang, H. C., Lee, S., Shin, J., Seyoung, M., Lee, E.-J., Song, M., & Lee, D. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.584875 · Cited by 41
“The molecular targets of the drugs and natural compounds were collected from the DrugBank, CTD (Davis et al., 2011), MATADOR (Günther et al., 2008), STITCH (Kuhn et al., 2013), and TTD (Zhu et al., 2011) databases.”
MethodCF-DTI: coarse-to-fine feature extraction for enhanced drug-target interaction prediction
Qian, Y., Wang, Q., Yin, L., & Lu, A. (2025) · Health Information Science and Systems · doi:10.1007/s13755-025-00370-6 · Cited by 36
“The Human and C.elegans datasets, introduced by Liu et al. [33], include positive interactions from DrugBank 4.1 and Matador [36], while negative samples are constructed via a systematic dissimilarity-based screening strategy.”
InfluentialHizukuri, Y., Sawada, R., & Yamanishi, Y. (2015) · BMC Medical Genomics · doi:10.1186/s12920-015-0158-1 · Cited by 37
“To investigate the validity of new predictions, we used experimental data from ChEMBL on compound – target interactions (e.g., binding affinity of 30 μ M in IC50), as well as independent information about compound – target interactions stored in KEGG [34], Matador [35], and PDSP Ki [36].”
MethodBan, T., Ohue, M., & Akiyama, Y. (2019) · Biochemistry and Biophysics Reports · doi:10.1016/j.bbrep.2019.01.008 · Cited by 36
AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders
Sajadi, S. Z., Chahooki, M. A. Z., Gharaghani, S., & Abbasi, K. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04127-2 · Cited by 36
Building a drug–target network and its applications
Lee, S., Park, K., & Kim, D. (2009) · Expert Opinion on Drug Discovery · doi:10.1517/17460440903322234 · Cited by 37
“SuperTar-get [19] integrates drug-related information about medical indication, adverse drug effects, drug metabolism, pathways and Gene Ontology terms of the target proteins.”
BackgroundCava, C., Bertoli, G. R., & Castiglioni, I. (2018) · Journal of Translational Medicine · doi:10.1186/s12967-018-1535-2 · Cited by 35
“We validated the approach with respect to its performance in identifying drug target pathways, by using the Matador database [10] that provides interactions between chemicals and proteins.”
MethodErlina, L., Paramita, R. I., Kusuma, W. A., Fadilah, F., Tedjo, A., Pratomo, I. P., Ramadhanti, N. S., Nasution, A. K., Surado, F. K., Fitriawan, A., et al. (2022) · BMC Complementary Medicine and Therapies · doi:10.1186/s12906-022-03686-y · Cited by 35
Li, Z., Zhong, W., Liu, Z., Huang, M.-H., Xie, Y. X., Dai, Z., & Zou, X. (2015) · Analytica Chimica Acta · doi:10.1016/j.aca.2015.02.032 · Cited by 35
“To further validate the reliability of these predicted target proteins, the target proteins verified by experiments are retrieved from the database of MATADOR [38] and Pharmacology [39], respectively.”
MethodImatinib and spironolactone suppress hepcidin expression
Mleczko‐Sanecka, K., da Silva, A. R., Call, D., Neves, J. C., Schmeer, N., Damm, G., Seehofer, D., & Muckenthaler, M. U. (2017) · Haematologica · doi:10.3324/haematol.2016.162917 · Cited by 34
“de/), a resource of drug and drug-target interactions.(25) Among 2159 putative hepcidin regulators, our bioinformatic analysis identified approximately 160 screening hits that are potentially sensitive to pharmacological manipulation.”
In Silico Target Prediction for Small Molecules
Byrne, R., & Schneider, G. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8891-4_16 · Cited by 39
Discovering the Targets of Drugs Via Computational Systems Biology
Chua, H. N., & Roth, F. P. (2011) · Journal of Biological Chemistry · doi:10.1074/jbc.r110.174797 · Cited by 34
Özdemir, E. S., Halakou, F., Nussinov, R., Gürsoy, A., & Keskin, Ö. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8955-3_1 · Cited by 34
A survey of web resources and tools for the study of TCM network pharmacology
Zhao, J., Yang, J., Tian, S., & Zhang, W. (2019) · Quantitative Biology · doi:10.1007/s40484-019-0167-8 · Cited by 35
“The databases that STITCH collects data include manually curated databases such as Drug-Bank [34], GLIDA (GPCR-ligand database) [44], Mata-dor [37], TTD (Therapeutic Targets Database) [45] and CTD (Comparative Toxicogenomics Database) [46]; pathway databases such as KEGG (Kyoto Encyclopedia of…”
MethodA Comprehensive Review on Deep Synergistic Drug Prediction Techniques for Cancer
Kumar, V., & Dogra, N. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09617-3 · Cited by 33
Jin, Y., Lu, J., Shi, R., & Yang, Y. (2021) · Biomolecules · doi:10.3390/biom11121783 · Cited by 33
Chapter 3: Small Molecules and Disease
Wishart, D. S. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002805 · Cited by 33
“Like TTD and SuperTarget, DrugBank supports very extensive text, sequence and chemical structure searches.”
BackgroundInfluentialText Mining in Cancer Gene and Pathway Prioritization
Luo, Y., Riedlinger, G., & Szolovits, P. (2014) · Cancer Informatics · doi:10.4137/cin.s13874 · Cited by 33
Supervised Bipartite Graph Inference
Yamanishi, Y. (2008) · Cited by 33
Comprehensive survey of target prediction web servers for Traditional Chinese Medicine
Ren, X., Yan, C.-X., Zhai, R.-X., Xu, K., Li, H., & Fu, X. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e19151 · Cited by 33
Liu, B., Pliakos, K., Vens, C., & Tsoumakas, G. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02495-z · Cited by 32
“To verify the reliability of those new interactions, we checked whether they are included in the corresponding updated dataset that incorporate validated interactions from the last version of the KEGG, DrugBank, ChEMBL and Matador databases.”
BackgroundGSKB: A gene set database for pathway analysis in mousePreprint
Lai, L., Hennessey, J., Bares, V., Son, E., Ban, Y. J., Wang, W., Qi, J., Jiang, G., Liberzon, A., & Ge, X. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/082511 · Cited by 31
DTome: a web-based tool for drug-target interactome construction
Sun, J., Wu, Y., Xu, H., & Zhao, Z. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s9-s7 · Cited by 32
“Since several target-centered databases are available, such as Matador and SuperTarget [46], and the Therapeutic Target Database (TTD) [47], we will integrate more drug target datasets into the system to ameliorate the effects of data limitation in the future.”
MethodKaratzas, E., Bourdakou, M. M., Kolios, G. C., & Spyrou, G. M. (2017) · Scientific Reports · doi:10.1038/s41598-017-12849-8 · Cited by 31
“SuperTarget contains a database of information on about 332828 drug-target interactions and DrugBank is another database containing bioinformatics and cheminformatics details on 8174 drugs and their targets.”
BackgroundDrug-Target Interaction Prediction via Dual Laplacian Graph Regularized Matrix Completion
Wang, M., Tang, C., & Chen, J. (2018) · BioMed Research International · doi:10.1155/2018/1425608 · Cited by 31
“Specifically, the validated DTIs are obtained from public datasets including BRENDA [42], KEGG BRITE [43], DrugBank [44], and SuperTarget [45].”
MethodNegStacking: Drug−Target Interaction Prediction Based on Ensemble Learning and Logistic Regression
Yang, J., He, S., Zhang, Z., & Bo, X. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.2968025 · Cited by 33
“The small amounts of DTIs are stored in databases, such as DrugBank, TTD, and Matador, but larger amounts of DTIs are yet to be discovered [5], [6], [7], [8].”
BackgroundCheng, Z., Zhou, S., Wang, Y., Liu, H., Guan, J., & Chen, Y. P. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2570211 · Cited by 31
In Silico Drug–Target Profiling
Trosset, J., & Cavé, C. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9145-7_6 · Cited by 31
Relax with CouchDB — Into the non-relational DBMS era of bioinformatics
Manyam, G. C., Payton, M. A., Roth, J. A., Abruzzo, L. V., & Coombes, K. R. (2012) · Genomics · doi:10.1016/j.ygeno.2012.05.006 · Cited by 30
“Over the decade, many other resources like DrugBank [26,27], PDTD [28], STITCH [29,30] and SuperTarget [31]were developed to store information about drug–target interactions.”
BackgroundInfluentialWang, C., & Kurgan, L. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby069 · Cited by 32
“These sources include MDDR [22, 23], PDSP Ki [24], BRENDA [25-33], BindingDB [34-38], TTD [39-44], KEGG BRITE [45-51], DrugBank [52-56], GLIDA [57, 58], KEGG DRUG [4551], SuperTarget [59, 60], Matador [59], STITCH [61-65], and ChEMBL [66-69] (see “Source databases” column in Supplementary Table S2).”
BackgroundMethodDrug Target Interaction Prediction Using Machine Learning Techniques – A Review.
Suruliandi, A., Idhaya, T., & Raja, S. P. (2023) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2022.11.002 · Cited by 31
Wang, F., Chou, A., & Segatori, L. (2011) · Chemistry & Biology · doi:10.1016/j.chembiol.2011.04.008 · Cited by 29
“We found that cell treatment with lacidipine, a third-generation 1,4-dihydropyridine that antagonizes Ca mobilization through LTCCs and RyRs (Gunther et al., 2008; Wishart et al., 2008) (Figure 1A), results in enhanced folding, trafficking, and activity of mutated GC variants.”
BackgroundInfluentialKampoDB, database of predicted targets and functional annotations of natural medicines
Sawada, R., Iwata, M., Umezaki, M., Usui, Y., KOBAYASHI, T., Kubono, T., Hayashi, S., Kadowaki, M., & Yamanishi, Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-29516-1 · Cited by 29
Jeon, J., Kang, S., & Kim, H. U. (2021) · Natural Product Reports · doi:10.1039/d1np00016k · Cited by 29
Wang, L., Zhou, Y., & Chen, Q. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241814142 · Cited by 30
A Strategy Based on Protein–Protein Interface Motifs May Help in Identifying Drug Off-Targets
Engin, H. B., Keskin, Ö., Nussinov, R., & Gürsoy, A. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300072q · Cited by 28
DetoxiProt: an integrated database for detoxification proteins
Yang, Z., Yu, Y., Yao, L., Li, G., Wang, L., Hu, Y., Wei, H., Wang, L., Hammami, R., Razavi, R., et al. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-s3-s2 · Cited by 29
“Although information concerning specific types of detoxification proteins, for example, peroxidase or peroxisome related proteins, proteins involved in ethanol or drug metabolism, can be found in some databases including the PeroxisomeDB, PeroxiBase, ERGR and SuperTarget [39], a complete collection of all these catego…”
BackgroundStruck, S., Schmidt, U. G., Gruening, B., Jaeger, I. S., Hossbach, J., & Preißner, R. (2008) · International Conference on Genome Informatics (GIW) · doi:10.1142/9781848163003_0020 · Cited by 28
“These data were extracted from the free database SuperDrug containing WHO-classified drugs [5, 10 ].”
MethodCombinations of Protein-Chemical Complex Structures Reveal New Targets for Established Drugs
Kalinina, O. V., Wichmann, O., Apic, G., & Russell, R. B. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002043 · Cited by 28
“Range estimates as to the average number of proteins to which a typical drug will bind can come from known 3D structures (3 proteins per compound), or drug-target databases such as SuperTarget/ Matador [27] (4.”
BackgroundSelf-citationBioTextQuest + : a knowledge integration platform for literature mining and concept discovery
Papanikolaou, N., Pavlopoulos, G. A., Pafilis, E., Θεοδοσίου, Θ., Schneider, R., Satagopam, V., Ouzounis, C., Eliopoulos, A. G., Promponas, V. J., & Iliopoulos, I. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu524 · Cited by 30
Self-citationMora, A., & Donaldson, I. M. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-294 · Cited by 28
“Construction of the Drug target List There are several drug target databases, such as DrugBank [55], SuperTarget [56], TTD [57], PharmGKB [58] and others.”
BackgroundDrugormerDTI: Drug Graphormer for drug–target interaction prediction
Hu, J., Yu, W., Pang, C., Jin, J., Pham, N. T., Manavalan, B., & Wei, L. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106946 · Cited by 26
Chemical-protein interactome and its application in off-target identification
Yang, L., Wang, K., Wang, L., Jegga, A. G., Qin, S., He, G., Chen, J., Xiao, Y., & He, L. (2011) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-011-0051-8 · Cited by 27
Satagopam, V., Theodoropoulou, M. C., Stampolakis, C. K., Pavlopoulos, G. A., Papandreou, N. C., Bagos, P. G., Schneider, R., & Hamodrakas, S. J. (2010) · Database · doi:10.1093/database/baq019 · Cited by 26
“Drugs related to three categories of molecules of Human-gpDB (GPCRs, G-proteins, effectors) were collected from DrugBank (29), Madator (33) and AKS2 (34).”
BackgroundSelf-citationLyu, X., Zeng, L., Zhang, H., Ke, Y., Liu, X., Zhao, N., Yuan, J., Chen, G., & Yang, S. (2020) · Life Sciences · doi:10.1016/j.lfs.2020.117366 · Cited by 25
Lian, M., Wang, X., & Du, W. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126509 · Cited by 25
Wang, F., & Segatori, L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061418 · Cited by 25
“Lowered [Ca] in the cytosol observed upon treatment of GD cells with lacidipine, a small molecule that inhibits both RyRs on the ER membrane and L-type Ca channels (LTCC) on the plasma membrane [24,25], correlates with the increase in trafficking and lysosomal activity of L444P GC [14].”
BackgroundNath, A., Kumari, P., & Chaube, R. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7756-7_2 · Cited by 25
Li, Y., Liu, X., You, Z., Li, L., Guo, J., & Wang, Z. (2020) · International Journal of Intelligent Systems · doi:10.1002/int.22332 · Cited by 26
“These databases are BREN-DA, 28 DrugBank, 6 KEGG BRITE, 29 and SuperTarget, 9 respectively, and these four data sets are defined as golden standard data sets by Yamanishi et al. 17 Among them, the counts of drugs possessed by enzymes, GPCRs, ion channels, and nuclear receptors were 445, 223, 210, and 54, respectively.”
MethodPredicting Drug Interaction With Adenosine Receptors Using Machine Learning and SMOTE Techniques
Saad, A. I., Omar, Y. M. K., & Maghraby, F. A. (2019) · IEEE Access · doi:10.1109/access.2019.2946314 · Cited by 25
Prediction of drug’s Anatomical Therapeutic Chemical (ATC) code by integrating drug–domain network
Chen, F.-S., & Jiang, Z. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.09.016 · Cited by 25
Debnath, K., Rana, P., & Ghosh, P. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf491 · Cited by 28
“Created by Yamanishi et al. [10] by sourcing KEGG BRITE [119], BRENDA [120], SuperTarget [113], and DrugBank databases, this dataset contains high-quality positive interactions and is one of the earliest DTI benchmarks.”
In silico methods for drug-target interaction prediction
Ru, X., Xu, L., Wu, H., & Zou, Q. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101184 · Cited by 26
An eigenvalue transformation technique for predicting drug-target interaction
Kuang, Q., Xu, X., Li, R., Dong, Y., Li, Y., Huang, Z., Li, Y., & Li, M. (2015) · Scientific Reports · doi:10.1038/srep13867 · Cited by 23
do Nascimento, A. C. A., Prudêncio, R. B. C., & Costa, I. G. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8955-3_17 · Cited by 23
Wang, W., Lv, H., Zhao, Y., Liu, D., Wang, Y., & Zhang, Y. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00330 · Cited by 22
“The MATADOR database 1 is a free online database of DPIs, which includes interaction patterns between chemicals and proteins (Gunther et al., 2008).”
BackgroundExploring drug-target interaction networks of illicit drugs
Atreya, R. V., Sun, J., & Zhao, Z. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-s4-s1 · Cited by 23
“Future research in this area should include more drug-target information from multiple target-centered databases such as Matador and SuperTarget [46] and the Therapeutic Target Database (TTD) [47,48].”
PharmDB-K: Integrated Bio-Pharmacological Network Database for Traditional Korean Medicine
Lee, J.-H., Park, K. M., Han, D.-J., Bang, N. Y., Kim, D.-H., Na, H., Lim, S., Kim, T., Kim, D. G., Kim, H. J., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142624 · Cited by 23
Modified linear regression predicts drug-target interactions accurately
Búza, K., Peška, L., & Koller, J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230726 · Cited by 23
“We say that a predicted interaction is validated if it is included in the current version of KEGG [38], DrugBank [39] or Matador [40].”
BackgroundInfluentialFu, Y., Wang, Y., & Zhang, B. (2014) · Journal of Traditional Chinese Medical Sciences · doi:10.1016/j.jtcms.2014.09.006 · Cited by 23
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
iPHACE: integrative navigation in pharmacological space
García-Serna, R., Ursu, O., Oprea, T. I., & Mestres, J. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq061 · Cited by 23
The Discovery of New Drug-Target Interactions for Breast Cancer Treatment
Song, J., Xu, Z., Cao, L., Wang, M., Hou, Y., & Li, K. (2021) · Molecules · doi:10.3390/molecules26247474 · Cited by 22
Network‐Based Approaches for Drug Repositioning
Song, T., Wang, G., Ding, M., Rodríguez‐Patón, A., Wang, X., & Wang, S. (2021) · Molecular Informatics · doi:10.1002/minf.202100200 · Cited by 25
In silico protein function prediction: the rise of machine learning-based approaches
Chen, J., Gu, Z., Lai, L., & Pei, J. (2023) · Medical Review · doi:10.1515/mr-2023-0038 · Cited by 23
“For example, DrugBank [168] and SuperTarget [169] mainly contain pharmaceutical properties, and PubChem [170] and ChEMBL [171] mainly contain functional and biological activities of compounds.”
BackgroundThe effects of the intake of plant foods on the human metabolome
Medina, S., Domínguez‐Perles, R., Ferreres, F., Tomás-Barberán, F. A., & Gil‐Izquierdo, Á. (2013) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2013.08.002 · Cited by 22
Insights into polypharmacology from drug-domain associations
Moya‐García, A. A., & Ranea, J. A. G. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt321 · Cited by 22
Lucas, F. A. S., Fowler, J., Chang, K., Kopetz, S., Vilar, E., & Scheet, P. A. (2014) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-14-0260 · Cited by 22
“Subsequently, CiDD characterizes candidate drugs using databases such as DrugBank, Matador, and KEGG Drug.”
BackgroundOne Drug for Multiple Targets: A Computational Perspective
Méndez‐Lucio, O., Naveja, J. J., Vite‐Caritino, H., Prieto-Martínez, F. D., & Medina‐Franco, J. L. (2017) · Journal of the Mexican Chemical Society · doi:10.29356/jmcs.v60i3.100 · Cited by 36
Extended Multitarget Pharmacology of Anticancer Drugs
Shi, D., Khan, F., & Abagyan, R. A. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00031 · Cited by 22
Metadata
- 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
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › 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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