Survey of Similarity-Based Prediction of Drug-Protein Interactions

Author(s): Chen Wang, Lukasz Kurgan*

Journal Name: Current Medicinal Chemistry

Volume 27 , Issue 35 , 2020


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Abstract:

Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.

Keywords: Drug-protein interactions, drug-protein interaction prediction, drug repurposing, drug side-effects, databases, drug structure, protein sequence.

[1]
Hopkins, A.L.; Groom, C.R. The druggable genome. Nat. Rev. Drug Discov., 2002, 1(9), 727-730.
[http://dx.doi.org/10.1038/nrd892 ] [PMID: 12209152]
[2]
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. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov., 2017, 16(1), 19-34.
[http://dx.doi.org/10.1038/nrd.2016.230 ] [PMID: 27910877]
[3]
Paul, S.M.; Mytelka, D.S.; Dunwiddie, C.T.; Persinger, C.C.; Munos, B.H.; Lindborg, S.R.; Schacht, A.L. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov., 2010, 9(3), 203-214.
[http://dx.doi.org/10.1038/nrd3078 ] [PMID: 20168317]
[4]
Mestres, J.; Gregori-Puigjané, E.; Valverde, S.; Solé, R.V. Data completeness--the Achilles heel of drug-target networks. Nat. Biotechnol., 2008, 26(9), 983-984.
[http://dx.doi.org/10.1038/nbt0908-983 ] [PMID: 18779805]
[5]
Lavecchia, A.; Di Giovanni, C. Virtual screening strategies in drug discovery: a critical review. Curr. Med. Chem., 2013, 20(23), 2839-2860.
[http://dx.doi.org/10.2174/09298673113209990001 ] [PMID: 23651302]
[6]
Bowes, J.; Brown, A.J.; Hamon, J.; Jarolimek, W.; Sridhar, A.; Waldron, G.; Whitebread, S. Reducing safety-related drug attrition: the use of in vitro pharmacological profiling. Nat. Rev. Drug Discov., 2012, 11(12), 909-922.
[http://dx.doi.org/10.1038/nrd3845 ] [PMID: 23197038]
[7]
Urban, L. Translational value of early target-based safety assessment and associated risk mitigation. 4th Annual Predictive Toxicology Summit, London, UK, 15-16, . , 2012.
[8]
Wang, X.; Greene, N. Comparing measures of promiscuity and exploring their relationship to toxicity. Mol. Inform., 2012, 31(2), 145-159.
[http://dx.doi.org/10.1002/minf.201100148 ] [PMID: 27476959]
[9]
Ding, H.; Takigawa, I.; Mamitsuka, H.; Zhu, S. Similarity-based machine learning methods for predicting drug-target interactions: a brief review. Brief. Bioinform., 2014, 15(5), 734-747.
[http://dx.doi.org/10.1093/bib/bbt056 ] [PMID: 23933754]
[10]
Schomburg, K.T.; Rarey, M. What is the potential of structure-based target prediction methods? Future Med. Chem., 2014, 6(18), 1987-1989.
[http://dx.doi.org/10.4155/fmc.14.135 ] [PMID: 25531963]
[11]
Somody, J.C.; MacKinnon, S.S.; Windemuth, A. Structural coverage of the proteome for pharmaceutical applications. Drug Discov. Today, 2017, 22(12), 1792-1799.
[http://dx.doi.org/10.1016/j.drudis.2017.08.004 ] [PMID: 28843631]
[12]
Xie, L.; Bourne, P.E. A robust and efficient algorithm for the shape description of protein structures and its application in predicting ligand binding sites. BMC Bioinformatics, 2007, 8(Suppl. 4), S9.
[http://dx.doi.org/10.1186/1471-2105-8-S4-S9 ] [PMID: 17570152]
[13]
Xie, L.; Xie, L.; Bourne, P.E. A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery. Bioinformatics, 2009, 25(12), i305-i312.
[http://dx.doi.org/10.1093/bioinformatics/btp220 ] [PMID: 19478004]
[14]
Hu, G.; Gao, J.; Wang, K.; Mizianty, M.J.; Ruan, J.; Kurgan, L. Finding protein targets for small biologically relevant ligands across fold space using inverse ligand binding predictions. Structure, 2012, 20(11), 1815-1822.
[http://dx.doi.org/10.1016/j.str.2012.09.011 ] [PMID: 23141694]
[15]
Brylinski, M.; Feinstein, W.P. eFindSite: improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. J. Comput. Aided Mol. Des., 2013, 27(6), 551-567.
[http://dx.doi.org/10.1007/s10822-013-9663-5 ] [PMID: 23838840]
[16]
Feinstein, W.P.; Brylinski, M. eFindSite: enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol. Inform., 2014, 33(2), 135-150.
[http://dx.doi.org/10.1002/minf.201300143 ] [PMID: 27485570]
[17]
Litfin, T.; Zhou, Y.; Yang, Y. SPOT-ligand 2: improving structure-based virtual screening by binding-homology search on an expanded structural template library. Bioinformatics, 2017, 33(8), 1238-1240.
[http://dx.doi.org/10.1093/bioinformatics/btw829 ] [PMID: 28057679]
[18]
Mizianty, M.J.; Fan, X.; Yan, J.; Chalmers, E.; Woloschuk, C.; Joachimiak, A.; Kurgan, L. Covering complete proteomes with X-ray structures: a current snapshot. Acta Crystallogr. D Biol. Crystallogr., 2014, 70(Pt 11), 2781-2793.
[http://dx.doi.org/10.1107/S1399004714019427 ] [PMID: 25372670]
[19]
Liu, T.; Altman, R.B. Relating essential proteins to drug side-effects using canonical component analysis: a structure-based approach. J. Chem. Inf. Model., 2015, 55(7), 1483-1494.
[http://dx.doi.org/10.1021/acs.jcim.5b00030 ] [PMID: 26121262]
[20]
Zhang, Q.C.; Petrey, D.; Deng, L.; Qiang, L.; Shi, Y.; Thu, C.A.; Bisikirska, B.; Lefebvre, C.; Accili, D.; Hunter, T.; Maniatis, T.; Califano, A.; Honig, B. Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature, 2012, 490(7421), 556-560.
[http://dx.doi.org/10.1038/nature11503 ] [PMID: 23023127]
[21]
Mitchell, J.B. The relationship between the sequence identities of alpha helical proteins in the PDB and the molecular similarities of their ligands. J. Chem. Inf. Comput. Sci., 2001, 41(6), 1617-1622.
[http://dx.doi.org/10.1021/ci010364q ] [PMID: 11749588]
[22]
Schuffenhauer, A.; Floersheim, P.; Acklin, P.; Jacoby, E. Similarity metrics for ligands reflecting the similarity of the target proteins. J. Chem. Inf. Comput. Sci., 2003, 43(2), 391-405.
[http://dx.doi.org/10.1021/ci025569t ] [PMID: 12653501]
[23]
Klabunde, T. Chemogenomic approaches to drug discovery: similar receptors bind similar ligands. Br. J. Pharmacol., 2007, 152(1), 5-7.
[http://dx.doi.org/10.1038/sj.bjp.0707308 ] [PMID: 17533415]
[24]
Raju, T.N.K. The Nobel chronicles. 1988: James Whyte Black, (b 1924), Gertrude Elion (1918-99), and George H Hitchings (1905-98). Lancet, 2000, 355(9208), 1022.
[http://dx.doi.org/10.1016/S0140-6736(05)74775-9 ] [PMID: 10768469]
[25]
Pahikkala, T.; Airola, A.; Pietilä, S.; Shakyawar, S.; Szwajda, A.; Tang, J.; Aittokallio, T. Toward more realistic drug-target interaction predictions. Brief. Bioinform., 2015, 16(2), 325-337.
[http://dx.doi.org/10.1093/bib/bbu010 ] [PMID: 24723570]
[26]
Mousavian, Z.; Masoudi-Nejad, A. Drug-target interaction prediction via chemogenomic space: learning-based methods. Expert Opin. Drug Metab. Toxicol., 2014, 10(9), 1273-1287.
[http://dx.doi.org/10.1517/17425255.2014.950222 ] [PMID: 25112457]
[27]
Lavecchia, A. Machine-learning approaches in drug discovery: methods and applications. Drug Discov. Today, 2015, 20(3), 318-331.
[http://dx.doi.org/10.1016/j.drudis.2014.10.012 ] [PMID: 25448759]
[28]
Cichonska, A.; Rousu, J.; Aittokallio, T. Identification of drug candidates and repurposing opportunities through compound-target interaction networks. Expert Opin. Drug Discov., 2015, 10(12), 1333-1345.
[http://dx.doi.org/10.1517/17460441.2015.1096926 ] [PMID: 26429153]
[29]
Lavecchia, A.; Cerchia, C. In silico methods to address polypharmacology: current status, applications and future perspectives. Drug Discov. Today, 2016, 21(2), 288-298.
[http://dx.doi.org/10.1016/j.drudis.2015.12.007 ] [PMID: 26743596]
[30]
Glaab, E. Building a virtual ligand screening pipeline using free software: a survey. Brief. Bioinform., 2016, 17(2), 352-366.
[http://dx.doi.org/10.1093/bib/bbv037 ] [PMID: 26094053]
[31]
Vilar, S.; Hripcsak, G. The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions. Brief. Bioinform., 2017, 18(4), 670-681.
[http://dx.doi.org/10.1093/bib/bbw048 ] [PMID: 27273288]
[32]
Chen, X.; Yan, C.C.; Zhang, X.; Zhang, X.; Dai, F.; Yin, J.; Zhang, Y. Drug-target interaction prediction: databases, web servers and computational models. Brief. Bioinform., 2016, 17(4), 696-712.
[http://dx.doi.org/10.1093/bib/bbv066 ] [PMID: 26283676]
[33]
Hart, T.; Xie, L. Providing data science support for systems pharmacology and its implications to drug discovery. Expert Opin. Drug Discov., 2016, 11(3), 241-256.
[http://dx.doi.org/10.1517/17460441.2016.1135126 ] [PMID: 26689499]
[34]
Fang, J.; Liu, C.; Wang, Q.; Lin, P.; Cheng, F. In silico polypharmacology of natural products. Brief. Bioinform., 2017, bbx045-bbx045.
[http://dx.doi.org/10.1093/bib/bbx045] [PMID: 28460068]
[35]
Lotfi Shahreza, M.; Ghadiri, N.; Mousavi, S.R.; Varshosaz, J.; Green, J.R. A review of network-based approaches to drug repositioning. Brief. Bioinform., 2017, bbx017-bbx017.
[http://dx.doi.org/10.1093/bib/bbx017] [PMID: 28334136]
[36]
Hao, M.; Bryant, S.H.; Wang, Y. Open-source chemoge-nomic data-driven algorithms for predicting drug-target inter-actions. Brief. Bioinform., 2018, bby010-bby010.
[http://dx.doi.org/10.1093/bib/bby010] [PMID: 29420684 ]
[37]
Ezzat, A.; Wu, M.; Li, X-L.; Kwoh, C-K. Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey. Brief. Bioinform., 2018, bby002-bby002.
[http://dx.doi.org/10.1093/bib/bby002] [PMID: 29377981]
[38]
Keiser, M.J.; Roth, B.L.; Armbruster, B.N.; Ernsberger, P.; Irwin, J.J.; Shoichet, B.K. Relating protein pharmacology by ligand chemistry. Nat. Biotechnol., 2007, 25(2), 197-206.
[http://dx.doi.org/10.1038/nbt1284 ] [PMID: 17287757]
[39]
Keiser, M.J.; Setola, V.; Irwin, J.J.; Laggner, C.; Abbas, A.I.; Hufeisen, S.J.; Jensen, N.H.; Kuijer, M.B.; Matos, R.C.; Tran, T.B.; Whaley, R.; Glennon, R.A.; Hert, J.; Thomas, K.L.; Edwards, D.D.; Shoichet, B.K.; Roth, B.L. Predicting new molecular targets for known drugs. Nature, 2009, 462(7270), 175-181.
[http://dx.doi.org/10.1038/nature08506 ] [PMID: 19881490]
[40]
Yamanishi, Y.; Araki, M.; Gutteridge, A.; Honda, W.; Kanehisa, M. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, 2008, 24(13), i232-i240.
[http://dx.doi.org/10.1093/bioinformatics/btn162 ] [PMID: 18586719]
[41]
Campillos, M.; Kuhn, M.; Gavin, A.C.; Jensen, L.J.; Bork, P. Drug target identification using side-effect similarity. Science, 2008, 321(5886), 263-266.
[http://dx.doi.org/10.1126/science.1158140 ] [PMID: 18621671]
[42]
Nagamine, N.; Shirakawa, T.; Minato, Y.; Torii, K.; Kobayashi, H.; Imoto, M.; Sakakibara, Y. Integrating statistical predictions and experimental verifications for enhancing protein-chemical interaction predictions in virtual screening. PLOS Comput. Biol., 2009, 5(6) e1000397
[http://dx.doi.org/10.1371/journal.pcbi.1000397 ] [PMID: 19503826]
[43]
Sakakibara, Y.; Hachiya, T.; Uchida, M.; Nagamine, N.; Sugawara, Y.; Yokota, M.; Nakamura, M.; Popendorf, K.; Komori, T.; Sato, K. COPICAT: a software system for predicting interactions between proteins and chemical compounds. Bioinformatics, 2012, 28(5), 745-746.
[http://dx.doi.org/10.1093/bioinformatics/bts031 ] [PMID: 22257668]
[44]
Bleakley, K.; Yamanishi, Y. Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics, 2009, 25(18), 2397-2403.
[http://dx.doi.org/10.1093/bioinformatics/btp433 ] [PMID: 19605421]
[45]
Yamanishi, Y.; Kotera, M.; Kanehisa, M.; Goto, S. Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics, 2010, 26(12), i246-i254.
[http://dx.doi.org/10.1093/bioinformatics/btq176 ] [PMID: 20529913]
[46]
Yabuuchi, H.; Niijima, S.; Takematsu, H.; Ida, T.; Hirokawa, T.; Hara, T.; Ogawa, T.; Minowa, Y.; Tsujimoto, G.; Okuno, Y. Analysis of multiple compound-protein interactions reveals novel bioactive molecules. Mol. Syst. Biol., 2011, 7, 472.
[http://dx.doi.org/10.1038/msb.2011.5 ] [PMID: 21364574]
[47]
van Laarhoven, T.; Nabuurs, S.B.; Marchiori, E. Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics, 2011, 27(21), 3036-3043.
[http://dx.doi.org/10.1093/bioinformatics/btr500 ] [PMID: 21893517]
[48]
Cheng, F.; Liu, C.; Jiang, J.; Lu, W.; Li, W.; Liu, G.; Zhou, W.; Huang, J.; Tang, Y. Prediction of drug-target interactions and drug repositioning via network-based inference. PLOS Comput. Biol., 2012, 8(5) e1002503
[http://dx.doi.org/10.1371/journal.pcbi.1002503 ] [PMID: 22589709]
[49]
Gönen, M. Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization. Bioinformatics, 2012, 28(18), 2304-2310.
[http://dx.doi.org/10.1093/bioinformatics/bts360 ] [PMID: 22730431]
[50]
Takarabe, M.; Kotera, M.; Nishimura, Y.; Goto, S.; Yamanishi, Y. Drug target prediction using adverse event report systems: a pharmacogenomic approach. Bioinformatics, 2012, 28(18), i611-i618.
[http://dx.doi.org/10.1093/bioinformatics/bts413 ] [PMID: 22962489]
[51]
Cao, D-S.; Liu, S.; Xu, Q-S.; Lu, H-M.; Huang, J-H.; Hu, Q-N.; Liang, Y-Z. Large-scale prediction of drug-target interactions using protein sequences and drug topological structures. Anal. Chim. Acta, 2012, 752, 1-10.
[http://dx.doi.org/10.1016/j.aca.2012.09.021 ] [PMID: 23101647]
[52]
Mei, J.P.; Kwoh, C.K.; Yang, P.; Li, X.L.; Zheng, J. Drugtarget interaction prediction by learning from local information and neighbors. Bioinformatics, 2013, 29(2), 238- 245.
[http://dx.doi.org/10.1093/bioinformatics/bts670 ] [PMID: 23162055]
[53]
Cheng, F.; Li, W.; Wu, Z.; Wang, X.; Zhang, C.; Li, J.; Liu, G.; Tang, Y. Prediction of polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. J. Chem. Inf. Model., 2013, 53(4), 753-762.
[http://dx.doi.org/10.1021/ci400010x ] [PMID: 23527559]
[54]
Alaimo, S.; Pulvirenti, A.; Giugno, R.; Ferro, A. Drug-target interaction prediction through domain-tuned network-based inference. Bioinformatics, 2013, 29(16), 2004-2008.
[http://dx.doi.org/10.1093/bioinformatics/btt307 ] [PMID: 23720490]
[55]
Koutsoukas, A.; Lowe, R.; Kalantarmotamedi, Y.; Mussa, H.Y.; Klaffke, W.; Mitchell, J.B.; Glen, R.C.; Bender, A. In silico target predictions: defining a benchmarking data set and comparison of performance of the multiclass Naïve Bayes and Parzen-Rosenblatt window. J. Chem. Inf. Model., 2013, 53(8), 1957-1966.
[http://dx.doi.org/10.1021/ci300435j ] [PMID: 23829430]
[56]
Yamanishi, Y.; Kotera, M.; Moriya, Y.; Sawada, R.; Kanehisa, M.; Goto, S. DINIES: drug-target interaction network inference engine based on supervised analysis.Nucleic Acids Res., 2014, 42(Web Server issue), 39-45.,
[http://dx.doi.org/10.1093/nar/gku337] [PMID: 24838565]
[57]
Shi, J-Y.; Yiu, S-M.; Li, Y.; Leung, H.C.M.; Chin, F.Y.L. Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering. Methods, 2015, 83, 98-104.
[http://dx.doi.org/10.1016/j.ymeth.2015.04.036 ] [PMID: 25957673]
[58]
Liu, H.; Sun, J.; Guan, J.; Zheng, J.; Zhou, S. Improving compound-protein interaction prediction by building up highly credible negative samples. Bioinformatics, 2015, 31(12), i221-i229.
[http://dx.doi.org/10.1093/bioinformatics/btv256 ] [PMID: 26072486]
[59]
Seal, A.; Ahn, Y.Y.; Wild, D.J. Optimizing drug-target interaction prediction based on random walk on heterogeneous networks. J. Cheminform., 2015, 7, 40.
[http://dx.doi.org/10.1186/s13321-015-0089-z ] [PMID: 26300984]
[60]
Kuang, Q.; Xu, X.; Li, R.; Dong, Y.; Li, Y.; Huang, Z.; Li, Y.; Li, M. An eigenvalue transformation technique for predicting drug-target interaction. Sci. Rep., 2015, 5, 13867.
[http://dx.doi.org/10.1038/srep13867 ] [PMID: 26350590]
[61]
Hao, M.; Wang, Y.; Bryant, S.H. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique. Anal. Chim. Acta, 2016, 909, 41-50.
[http://dx.doi.org/10.1016/j.aca.2016.01.014 ] [PMID: 26851083]
[62]
Jamali, A.A.; Ferdousi, R.; Razzaghi, S.; Li, J.; Safdari, R.; Ebrahimie, E. DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins. Drug Discov. Today, 2016, 21(5), 718-724.
[http://dx.doi.org/10.1016/j.drudis.2016.01.007 ] [PMID: 26821132]
[63]
Liu, Y.; Wu, M.; Miao, C.; Zhao, P.; Li, X.L. Neighborhood regularized logistic matrix factorization for drug-target interaction prediction. PLOS Comput. Biol., 2016, 12(2) e1004760
[http://dx.doi.org/10.1371/journal.pcbi.1004760 ] [PMID: 26872142]
[64]
Wu, Z.; Cheng, F.; Li, J.; Li, W.; Liu, G.; Tang, Y. SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning. Brief. Bioinform., 2017, 18(20), 333-347.
[http://dx.doi.org/10.1093/bib/bbw012 ] [PMID: 26944082]
[65]
Ba-Alawi, W.; Soufan, O.; Essack, M.; Kalnis, P.; Bajic, V.B. DASPfind: new efficient method to predict drug-target interactions. J. Cheminform., 2016, 8, 15.
[http://dx.doi.org/10.1186/s13321-016-0128-4 ] [PMID: 26985240]
[66]
Yuan, Q.; Gao, J.; Wu, D.; Zhang, S.; Mamitsuka, H.; Zhu, S. DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank. Bioinformatics, 2016, 32(12), i18-i27.
[http://dx.doi.org/10.1093/bioinformatics/btw244 ] [PMID: 27307615]
[67]
Wen, M.; Zhang, Z.; Niu, S.; Sha, H.; Yang, R.; Yun, Y.; Lu, H. Deep-learning-based drug-target interaction prediction. J. Proteome Res., 2017, 16(4), 1401-1409.
[http://dx.doi.org/10.1021/acs.jproteome.6b00618 ] [PMID: 28264154]
[68]
Ezzat, A.; Wu, M.; Li, X-L.; Kwoh, C-K. Drug-target interaction prediction using ensemble learning and dimensionality reduction. Methods, 2017, 129, 81-88.
[http://dx.doi.org/10.1016/j.ymeth.2017.05.016 ] [PMID: 28549952]
[69]
Peón, A.; Naulaerts, S.; Ballester, P.J. Predicting the reliability of drug-target interaction predictions with maximum coverage of target space. Sci. Rep., 2017, 7(1), 3820.
[http://dx.doi.org/10.1038/s41598-017-04264-w ] [PMID: 28630414]
[70]
Peng, L.; Zhu, W.; Liao, B.; Duan, Y.; Chen, M.; Chen, Y.; Yang, J. Screening drug-target interactions with positive-unlabeled learning. Sci. Rep., 2017, 7(1), 8087.
[http://dx.doi.org/10.1038/s41598-017-08079-7 ] [PMID: 28808275]
[71]
Li, Z.; Han, P.; You, Z-H.; Li, X.; Zhang, Y.; Yu, H.; Nie, R.; Chen, X. In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences. Sci. Rep., 2017, 7(1), 11174.
[http://dx.doi.org/10.1038/s41598-017-10724-0 ] [PMID: 28894115]
[72]
Luo, Y.; Zhao, X.; Zhou, J.; Yang, J.; Zhang, Y.; Kuang, W.; Peng, J.; Chen, L.; Zeng, J. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat. Commun., 2017, 8(1), 573.
[http://dx.doi.org/10.1038/s41467-017-00680-8 ] [PMID: 28924171]
[73]
Fang, J.; Wu, Z.; Cai, C.; Wang, Q.; Tang, Y.; Cheng, F. Quantitative and systems pharmacology. 1. In silico prediction of drug-target interactions of natural products enables new targeted cancer therapy. J. Chem. Inf. Model., 2017, 57(11), 2657-2671.
[http://dx.doi.org/10.1021/acs.jcim.7b00216 ] [PMID: 28956927]
[74]
Wu, Z.; Lu, W.; Yu, W.; Wang, T.; Li, W.; Liu, G.; Zhang, H.; Pang, X.; Huang, J.; Liu, M.; Cheng, F.; Tang, Y. Quantitative and systems pharmacology 2. In silico polypharmacology of G protein-coupled receptor ligands via network-based approaches. Pharmacol. Res., 2018, 129, 400-413.
[http://dx.doi.org/10.1016/j.phrs.2017.11.005 ] [PMID: 29133212]
[75]
Rayhan, F.; Ahmed, S.; Shatabda, S.; Farid, D.M.; Mousavian, Z.; Dehzangi, A.; Rahman, M.S. iDTI-ESBoost: identification of drug target interaction using evolutionary and structural features with boosting. Sci. Rep., 2017, 7(1), 17731.
[http://dx.doi.org/10.1038/s41598-017-18025-2 ] [PMID: 29255285]
[76]
Coordinators, N.R. Database resources of the national center for biotechnology information. Nucleic Acids Res., 2017, 45(D1), D12-D17.
[http://dx.doi.org/10.1093/nar/gkw1071 ] [PMID: 27899561]
[77]
Journal Citation Reports®. Clarivate Analytics. 2017.
[78]
Roth, B.L.; Lopez, E.; Patel, S.; Kroeze, W.K. The multiplicity of serotonin receptors: uselessly diverse molecules or an embarrassment of riches? Neuroscientist, 2000, 6(4), 252-262.
[http://dx.doi.org/10.1177/107385840000600408]
[79]
Schomburg, I.; Hofmann, O.; Baensch, C.; Chang, A.; Schomburg, D. Enzyme data and metabolic information: BRENDA, a resource for research in biology, biochemistry, and medicine. Gene Funct. Dis., 2000, 1(3‐4), 109-118.
[http://dx.doi.org/10.1002/1438-826X(200010)1:3/4<109:AID-GNFD109>3.0.CO;2-O]
[80]
Schomburg, I.; Chang, A.; Schomburg, D. BRENDA, enzyme data and metabolic information. Nucleic Acids Res., 2002, 30(1), 47-49.
[http://dx.doi.org/10.1093/nar/30.1.47 ] [PMID: 11752250]
[81]
Schomburg, I.; Chang, A.; Ebeling, C.; Gremse, M.; Heldt, C.; Huhn, G.; Schomburg, D. BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Res., 2004, 32(1), D431-D433.
[http://dx.doi.org/10.1093/nar/gkh081] [PMID: 14681450]
[82]
Barthelmes, J.; Ebeling, C.; Chang, A.; Schomburg, I.; Schomburg, D. BRENDA, AMENDA and FRENDA: the enzyme information system. Nucleic Acids Res., 2007, 35, D511-D514.
[http://dx.doi.org/10.1093/nar/gkl972] [PMID: 17202167]
[83]
Chang, A.; Scheer, M.; Grote, A.; Schomburg, I.; Schom-burg, D. BRENDA, AMENDA and FRENDA the enzyme information system: new content and tools. Nucleic Acids Res., 2008, 37, D588-D592.
[http://dx.doi.org/10.1093/nar/gkn820] [PMID: 18984617 ]
[84]
Scheer, M.; Grote, A.; Chang, A.; Schomburg, I.; Munaretto, C.; Rother, M.; Söhngen, C.; Stelzer, M.; Thiele, J.; Schom-burg, D. BRENDA, the enzyme information system. Nucleic Acids Res., 2010, 39(1), D670-D676.
[http://dx.doi.org/10.1093/nar/gkn820] [PMID: 18984617 ]
[85]
Schomburg, I.; Chang, A.; Placzek, S.; Söhngen, C.; Rother, M.; Lang, M.; Munaretto, C.; Ulas, S.; Stelzer, M.; Grote, A.; Scheer, M.; Schomburg, D. BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA. Nucleic Acids Res., 2013, 41(Database issue), D764-D772.
[http://dx.doi.org/10.1093/nar/gks1049] [PMID: 23203881]
[86]
Chang, A.; Schomburg, I.; Placzek, S.; Jeske, L.; Ulbrich, M.; Xiao, M.; Sensen, C.W.; Schomburg, D. BRENDA in 2015: exciting developments in its 25th year of existence. Nucleic Acids Res., 2015, 43(Database issue), D439-D446.
[http://dx.doi.org/10.1093/nar/gku1068 ] [PMID: 25378310]
[87]
Placzek, S.; Schomburg, I.; Chang, A.; Jeske, L.; Ulbrich, M.; Tillack, J.; Schomburg, D. BRENDA in 2017: new perspectives and new tools in BRENDA. Nucleic Acids Res., 2017, 45(D1), D380-D388.
[http://dx.doi.org/10.1093/nar/gkw952 ] [PMID: 27924025]
[88]
Chen, X.; Liu, M.; Gilson, M.K.; Binding, D.B. BindingDB: a web-accessible molecular recognition database. Comb. Chem. High Throughput Screen., 2001, 4(8), 719-725.
[http://dx.doi.org/10.2174/1386207013330670 ] [PMID: 11812264]
[89]
Chen, X.; Lin, Y.; Liu, M.; Gilson, M.K. The binding database: data management and interface design. Bioinformatics, 2002, 18(1), 130-139.
[http://dx.doi.org/10.1093/bioinformatics/18.1.130 ] [PMID: 11836221]
[90]
Chen, X.; Lin, Y.; Gilson, M.K. The binding database: overview and user’s guide.Biopolymers, 2001-2002-2002, 61(2), 127-141.,
[http://dx.doi.org/10.1002/1097-0282(2002)61:2127::AIDBIP100763.0.CO;2-N] [PMID: 11987162]
[91]
Liu, T.; Lin, Y.; Wen, X.; Jorissen, R.N.; Gilson, M.K.; Binding, D.B. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res., 2007, 35(Database issue), D198-D201.
[http://dx.doi.org/10.1093/nar/gkl999 ] [PMID: 17145705]
[92]
Gilson, M.K.; Liu, T.; Baitaluk, M.; Nicola, G.; Hwang, L.; Chong, J. BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res., 2016, 44(D1), D1045-D1053.
[http://dx.doi.org/10.1093/nar/gkv1072 ] [PMID: 26481362]
[93]
Chen, X.; Ji, Z.L.; Chen, Y.Z. TTD: therapeutic target data-base. Nucleic Acids Res., 2002, 30(1), 412-415.
[http://dx.doi.org/10.1093/nar/30.1.412 ] [PMID: 11752352]
[94]
Zhu, F.; Han, B.; Kumar, P.; Liu, X.; Ma, X.; Wei, X.; Huang, L.; Guo, Y.; Han, L.; Zheng, C.; Chen, Y. Update of TTD: therapeutic target database. Nucleic Acids Res., 2010, 38(Database issue), D787-D791.
[http://dx.doi.org/10.1093/nar/gkp1014 ] [PMID: 19933260]
[95]
Zhu, F.; Shi, Z.; Qin, C.; Tao, L.; Liu, X.; Xu, F.; Zhang, L.; Song, Y.; Liu, X.; Zhang, J.; Han, B.; Zhang, P.; Chen, Y. Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery. Nucleic Acids Res., 2012, 40(Database issue), D1128-D1136.
[http://dx.doi.org/10.1093/nar/gkr797 ] [PMID: 21948793]
[96]
Qin, C.; Zhang, C.; Zhu, F.; Xu, F.; Chen, S.Y.; Zhang, P.; Li, Y.H.; Yang, S.Y.; Wei, Y.Q.; Tao, L.; Chen, Y.Z. Therapeutic target database update 2014: a resource for targeted therapeutics. Nucleic Acids Res., 2014, 42(Database issue), D1118-D1123.
[http://dx.doi.org/10.1093/nar/gkt1129 ] [PMID: 24265219]
[97]
Yang, H.; Qin, C.; Li, Y.H.; Tao, L.; Zhou, J.; Yu, C.Y.; Xu, F.; Chen, Z.; Zhu, F.; Chen, Y.Z. Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information. Nucleic Acids Res., 2016, 44(D1), D1069-D1074.
[http://dx.doi.org/10.1093/nar/gkv1230 ] [PMID: 26578601]
[98]
Li, Y.H.; Yu, C.Y.; Li, X.X.; Zhang, P.; Tang, J.; Yang, Q.; Fu, T.; Zhang, X.; Cui, X.; Tu, G.; Zhang, Y.; Li, S.; Yang, F.; Sun, Q.; Qin, C.; Zeng, X.; Chen, Z.; Chen, Y.Z.; Zhu, F. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res., 2018, 46(D1), D1121-D1127.
[http://dx.doi.org/10.1093/nar/gkx1076 ] [PMID: 29140520]
[99]
Kanehisa, M.; Goto, S.; Hattori, M.; Aoki-Kinoshita, K.F.; Itoh, M.; Kawashima, S.; Katayama, T.; Araki, M.; Hirakawa, M. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res., 2006, 34(1), D354-D357.
[http://dx.doi.org/10.1093/nar/gkj102] [PMID: 16381885 ]
[100]
Kanehisa, M.; Araki, M.; Goto, S.; Hattori, M.; Hirakawa, M.; Itoh, M.; Katayama, T.; Kawashima, S.; Okuda, S.; Tokimatsu, T.; Yamanishi, Y. KEGG for linking genomes to life and the environment. Nucleic Acids Res., 2008, 36(1), D480-D484.
[http://dx.doi.org/10.1093/nar/gkm882] [PMID: 18077471]
[101]
Kanehisa, M.; Goto, S.; Furumichi, M.; Tanabe, M.; Hirakawa, M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res., 2009, 38(1), D355-D360.
[http://dx.doi.org/10.1093/nar/gkp896] [PMID: 19880382]
[102]
Kanehisa, M.; Goto, S.; Sato, Y.; Furumichi, M.; Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res., 2012, 40(Database issue), D109-D114.
[http://dx.doi.org/10.1093/nar/gkr988] [PMID: 22080510]
[103]
Kanehisa, M.; Goto, S.; Sato, Y.; Kawashima, M.; Furumichi, M.; Tanabe, M. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res., 2014, 42(Database issue), D199-D205.
[http://dx.doi.org/10.1093/nar/gkt1076 ] [PMID: 24214961]
[104]
Kanehisa, M.; Sato, Y.; Kawashima, M.; Furumichi, M.; Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res., 2016, 44(D1), D457-D462.
[http://dx.doi.org/10.1093/nar/gkv1070 ] [PMID: 26476454]
[105]
Kanehisa, M.; Furumichi, M.; Tanabe, M.; Sato, Y.; Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res., 2017, 45(D1), D353-D361.
[http://dx.doi.org/10.1093/nar/gkw1092 ] [PMID: 27899662]
[106]
Wishart, D.S.; Knox, C.; Guo, A.C.; Shrivastava, S.; Hassanali, M.; Stothard, P.; Chang, Z.; Woolsey, J. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res., 2006, 34(1), D668-D672.
[http://dx.doi.org/10.1093/nar/gkj067] [PMID: 16381955]
[107]
Wishart, D.S.; Knox, C.; Guo, A.C.; Cheng, D.; Shrivastava, S.; Tzur, D.; Gautam, B.; Hassanali, M. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res., 2008, 36(1), D901-D906.
[http://dx.doi.org/10.1093/nar/gkm958 ] [PMID: 18048412]
[108]
Knox, C.; Law, V.; Jewison, T.; Liu, P.; Ly, S.; Frolkis, A.; Pon, A.; Banco, K.; Mak, C.; Neveu, V.; Djoumbou, Y.; Eisner, R.; Guo, A.C.; Wishart, D.S. DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs. Nucleic Acids Res., 2011, 39(1), D1035-D1041.
[http://dx.doi.org/10.1093/nar/gkq1126] [PMID: 21059682]
[109]
Law, V.; Knox, C.; Djoumbou, Y.; Jewison, T.; Guo, A.C.; Liu, Y.; Maciejewski, A.; Arndt, D.; Wilson, M.; Neveu, V.; Tang, A.; Gabriel, G.; Ly, C.; Adamjee, S.; Dame, Z.T.; Han, B.; Zhou, Y.; Wishart, D.S. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res., 2014, 42(Database issue), D1091-D1097.
[http://dx.doi.org/10.1093/nar/gkt1068 ] [PMID: 24203711]
[110]
Wishart, D.S.; Feunang, Y.D.; Guo, A.C.; Lo, E.J.; Marcu, A.; Grant, J.R.; Sajed, T.; Johnson, D.; Li, C.; Sayeeda, Z.; Assempour, N.; Iynkkaran, I.; Liu, Y.; Maciejewski, A.; Gale, N.; Wilson, A.; Chin, L.; Cummings, R.; Le, D.; Pon, A.; Knox, C.; Wilson, M. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res., 2018, 46(D1), D1074-D1082.
[http://dx.doi.org/10.1093/nar/gkx1037 ] [PMID: 29126136]
[111]
Okuno, Y.; Yang, J.; Taneishi, K.; Yabuuchi, H.; Tsujimoto, G. GLIDA: GPCR-ligand database for chemical genomic drug discovery. Nucleic Acids Res., 2006, 34(1), D673-D677.
[http://dx.doi.org/10.1093/nar/gkj028] [PMID: 16381956]
[112]
Okuno, Y.; Tamon, A.; Yabuuchi, H.; Niijima, S.; Minowa, Y.; Tonomura, K.; Kunimoto, R.; Feng, C. GLIDA: GPCR—ligand database for chemical genomics drug discovery—database and tools update. Nucleic Acids Res., 2008, 36(1), D907-D912.
[http://dx.doi.org/10.1093/nar/gkm948] [PMID: 17986454]
[113]
Günther, 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. Supertarget and matador: resources for exploring drug-target relationships. Nucleic Acids Res., 2008, 36(1), D919-D922.
[http://dx.doi.org/10.1093/nar/gkm862] [PMID: 17942422]
[114]
Hecker, N.; Ahmed, J.; von Eichborn, J.; Dunkel, M.; Macha, K.; Eckert, A.; Gilson, M.K.; Bourne, P.E.; Preissner, R. SuperTarget goes quantitative: update on drug-target interactions. Nucleic Acids Res., 2012, 40(Database issue), D1113-D1117.
[http://dx.doi.org/10.1093/nar/gkr912 ] [PMID: 22067455]
[115]
Kuhn, M.; von Mering, C.; Campillos, M.; Jensen, L.J.; Bork, P. STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res., 2008, 36(1), D684-D688.
[http://dx.doi.org/10.1093/nar/gkm795] [PMID: 18084021]
[116]
Kuhn, M.; Szklarczyk, D.; Franceschini, A.; Campillos, M.; von Mering, C.; Jensen, L.J.; Beyer, A.; Bork, P. STITCH 2: an interaction network database for small molecules and proteins. Nucleic Acids Res., 2010, 38(Database issue), D552-D556.
[http://dx.doi.org/10.1093/nar/gkp937 ] [PMID: 19897548]
[117]
Kuhn, M.; Szklarczyk, D.; Franceschini, A.; von Mering, C.; Jensen, L.J.; Bork, P. STITCH 3: zooming in on protein-chemical interactions. Nucleic Acids Res., 2012, 40(Database issue), D876-D880.
[http://dx.doi.org/10.1093/nar/gkr1011 ] [PMID: 22075997]
[118]
Kuhn, M.; Szklarczyk, D.; Pletscher-Frankild, S.; Blicher, T.H.; von Mering, C.; Jensen, L.J.; Bork, P. STITCH 4: integration of protein-chemical interactions with user data. Nucleic Acids Res., 2014, 42(Database issue), D401-D407.
[http://dx.doi.org/10.1093/nar/gkt1207 ] [PMID: 24293645]
[119]
Szklarczyk, D.; Santos, A.; von Mering, C.; Jensen, L.J.; Bork, P.; Kuhn, M. STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res., 2016, 44(D1), D380-D384.
[http://dx.doi.org/10.1093/nar/gkv1277 ] [PMID: 26590256]
[120]
Gaulton, A.; Bellis, L.J.; Bento, A.P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J.P. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res., 2012, 40(Database issue), D1100-D1107.
[http://dx.doi.org/10.1093/nar/gkr777 ] [PMID: 21948594]
[121]
Bento, A.P.; Gaulton, A.; Hersey, A.; Bellis, L.J.; Chambers, J.; Davies, M.; Krüger, F.A.; Light, Y.; Mak, L.; McGlinchey, S.; Nowotka, M.; Papadatos, G.; Santos, R.; Overington, J.P. The ChEMBL bioactivity database: an update. Nucleic Acids Res., 2014, 42(Database issue), D1083-D1090.
[http://dx.doi.org/10.1093/nar/gkt1031 ] [PMID: 24214965]
[122]
Davies, M.; Nowotka, M.; Papadatos, G.; Dedman, N.; Gaulton, A.; Atkinson, F.; Bellis, L.; Overington, J.P. ChEMBL web services: streamlining access to drug discovery data and utilities. Nucleic Acids Res., 2015, 43(W1) W612-20
[http://dx.doi.org/10.1093/nar/gkv352 ] [PMID: 25883136]
[123]
Gaulton, A.; Hersey, A.; Nowotka, M.; Bento, A.P.; Chambers, J.; Mendez, D.; Mutowo, P.; Atkinson, F.; Bellis, L.J.; Cibrián-Uhalte, E.; Davies, M.; Dedman, N.; Karlsson, A.; Magariños, M.P.; Overington, J.P.; Papadatos, G.; Smit, I.; Leach, A.R. The ChEMBL database in 2017. Nucleic Acids Res., 2017, 45(D1), D945-D954.
[http://dx.doi.org/10.1093/nar/gkw1074 ] [PMID: 27899562]
[124]
Schuffenhauer, A.; Zimmermann, J.; Stoop, R.; van der Vyver, J-J.; Lecchini, S.; Jacoby, E. An ontology for pharmaceutical ligands and its application for in silico screening and library design. J. Chem. Inf. Comput. Sci., 2002, 42(4), 947-955.
[http://dx.doi.org/10.1021/ci010385k ] [PMID: 12132896]
[125]
Southan, C.; Várkonyi, P.; Muresan, S. Quantitative assessment of the expanding complementarity between public and commercial databases of bioactive compounds. J. Cheminform., 2009, 1(1), 10.
[http://dx.doi.org/10.1186/1758-2946-1-10 ] [PMID: 20298516]
[126]
Euskirchen, G. Integrative approaches in molecular medicine. Pharmacogenomics, 2004, 5(4), 357-360.
[http://dx.doi.org/10.1517/14622416.5.4.357 ] [PMID: 15165172]
[127]
Overington, J. ChEMBL. An interview with John Overington, team leader, chemogenomics at the European bioinformatics institute outstation of the European molecular biology laboratory (EMBL-EBI). Interview by Wendy A. Warr. J. Comput. Aided Mol. Des., 2009, 23(4), 195-198.
[http://dx.doi.org/10.1007/s10822-009-9260-9 ] [PMID: 19194660]
[128]
Bender, A. Databases: compound bioactivities go public. Nat. Chem. Biol., 2010, 6(5), 309-309.
[http://dx.doi.org/10.1038/nchembio.354]
[129]
Zhou, H.; Gao, M.; Skolnick, J. Comprehensive prediction of drug-protein interactions and side effects for the human proteome. Sci. Rep., 2015, 5, 11090.
[http://dx.doi.org/10.1038/srep11090 ] [PMID: 26057345]
[130]
Chartier, M.; Morency, L-P.; Zylber, M.I.; Najmanovich, R.J. Large-scale detection of drug off-targets: hypotheses for drug repurposing and understanding side-effects. BMC Pharmacol. Toxicol., 2017, 18(1), 18.
[http://dx.doi.org/10.1186/s40360-017-0128-7 ] [PMID: 28449705]
[131]
Brylinski, M. Aromatic interactions at the ligand–protein interface: implications for the development of docking scoring functions. Chem. Biol. Drug Des., 2017, 1-11.
[PMID: 28816025]
[132]
Tatonetti, N.P.; Ye, P.P.; Daneshjou, R.; Altman, R.B. Data-driven prediction of drug effects and interactions. Sci. Transl. Med., 2012, 4(125), D684-D688.
[http://dx.doi.org/10.1126/scitranslmed.3003377 ] [PMID: 22422992]
[133]
Schomburg, K.T.; Rarey, M. Benchmark data sets for structure-based computational target prediction. J. Chem. Inf. Model., 2014, 54(8), 2261-2274.
[http://dx.doi.org/10.1021/ci500131x ] [PMID: 25084060]
[134]
Wishart, D.; Arndt, D.; Pon, A.; Sajed, T.; Guo, A.C.; Djoumbou, Y.; Knox, C.; Wilson, M.; Liang, Y.; Grant, J.; Liu, Y.; Goldansaz, S.A.; Rappaport, S.M. T3DB: the toxic exposome database. Nucleic Acids Res., 2015, 43(Database issue), D928-D934.
[http://dx.doi.org/10.1093/nar/gku1004 ] [PMID: 25378312]
[135]
Legehar, A.; Xhaard, H.; Ghemtio, L. IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data. J. Cheminform., 2016, 8(1), 33.
[http://dx.doi.org/10.1186/s13321-016-0141-7 ] [PMID: 27303447]
[136]
Shameer, K.; Glicksberg, B.S.; Hodos, R.; Johnson, K.W.; Badgeley, M.A.; Readhead, B.; Tomlinson, M.S.; O’Connor, T.; Miotto, R.; Kidd, B.A.; Chen, R.; Ma’ayan, A.; Dudley, J.T. Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning. Brief. Bioinform., 2017, bbw136-bbw136.
[137]
Russ, A.P.; Lampel, S. The druggable genome: an update. Drug Discov. Today, 2005, 10(23-24), 1607-1610.
[http://dx.doi.org/10.1016/S1359-6446(05)03666-4 ] [PMID: 16376820]
[138]
Rask-Andersen, M.; Masuram, S.; Schiöth, H.B. The druggable genome: Evaluation of drug targets in clinical trials suggests major shifts in molecular class and indication. Annu. Rev. Pharmacol. Toxicol., 2014, 54, 9-26.
[http://dx.doi.org/10.1146/annurev-pharmtox-011613-135943 ] [PMID: 24016212]
[139]
Hu, G.; Wu, Z.; Wang, K.; Uversky, V.N.; Kurgan, L. Untapped potential of disordered proteins in current druggable human proteome. Curr. Drug Targets, 2016, 17(10), 1198-1205.
[http://dx.doi.org/10.2174/1389450116666150722141119 ] [PMID: 26201486]
[140]
Paolini, G.V.; Shapland, R.H.B.; van Hoorn, W.P.; Mason, J.S.; Hopkins, A.L. Global mapping of pharmacological space. Nat. Biotechnol., 2006, 24(7), 805-815.
[http://dx.doi.org/10.1038/nbt1228 ] [PMID: 16841068]
[141]
Hopkins, A.L. Drug discovery: predicting promiscuity. Nature, 2009, 462(7270), 167-168.
[http://dx.doi.org/10.1038/462167a ] [PMID: 19907483]
[142]
Anighoro, A.; Bajorath, J.; Rastelli, G. Polypharmacology: challenges and opportunities in drug discovery. J. Med. Chem., 2014, 57(19), 7874-7887.
[http://dx.doi.org/10.1021/jm5006463 ] [PMID: 24946140]
[143]
Chong, C.R.; Sullivan, D.J. Jr. New uses for old drugs. Nature, 2007, 448(7154), 645-646.
[http://dx.doi.org/10.1038/448645a ] [PMID: 17687303]
[144]
Haupt, V.J.; Schroeder, M. Old friends in new guise: repositioning of known drugs with structural bioinformatics. Brief. Bioinform., 2011, 12(4), 312-326.
[http://dx.doi.org/10.1093/bib/bbr011 ] [PMID: 21441562]
[145]
Hu, Y.; Bajorath, J. Compound promiscuity: what can we learn from current data? Drug Discov. Today, 2013, 18(13-14), 644-650.
[http://dx.doi.org/10.1016/j.drudis.2013.03.002 ] [PMID: 23524195]
[146]
Lounkine, E.; Keiser, M.J.; Whitebread, S.; Mikhailov, D.; Hamon, J.; Jenkins, J.L.; Lavan, P.; Weber, E.; Doak, A.K.; Côté, S.; Shoichet, B.K.; Urban, L. Large-scale prediction and testing of drug activity on side-effect targets. Nature, 2012, 486(7403), 361-367.
[http://dx.doi.org/10.1038/nature11159 ] [PMID: 22722194]
[147]
Tarcsay, Á.; Keserű, G.M. Contributions of molecular properties to drug promiscuity. J. Med. Chem., 2013, 56(5), 1789-1795.
[http://dx.doi.org/10.1021/jm301514n ] [PMID: 23356819]
[148]
Hu, G.; Wang, K.; Groenendyk, J.; Barakat, K.; Mizianty, M.J.; Ruan, J.; Michalak, M.; Kurgan, L. Human structural proteome-wide characterization of Cyclosporine A targets. Bioinformatics, 2014, 30(24), 3561-3566.
[http://dx.doi.org/10.1093/bioinformatics/btu581 ] [PMID: 25172926]
[149]
Jasial, S.; Hu, Y.; Bajorath, J. Determining the degree of promiscuity of extensively assayed compounds. PLoS One, 2016, 11(4) e0153873
[http://dx.doi.org/10.1371/journal.pone.0153873 ] [PMID: 27082988]
[150]
Davis, A.P.; Grondin, C.J.; Johnson, R.J.; Sciaky, D.; King, B.L.; McMorran, R.; Wiegers, J.; Wiegers, T.C.; Mattingly, C.J. The comparative toxicogenomics database: update 2017. Nucleic Acids Res., 2017, 45(D1), D972-D978.
[http://dx.doi.org/10.1093/nar/gkw838 ] [PMID: 27651457]
[151]
Wang, Y.; Bryant, S.H.; Cheng, T.; Wang, J.; Gindulyte, A.; Shoemaker, B.A.; Thiessen, P.A.; He, S.; Zhang, J. PubChem bioassay: 2017 update. Nucleic Acids Res., 2017, 45(D1), D955-D963.
[http://dx.doi.org/10.1093/nar/gkw1118 ] [PMID: 27899599]
[152]
Anastassiadis, T.; Deacon, S.W.; Devarajan, K.; Ma, H.; Peterson, J.R. Comprehensive assay of kinase catalytic activity reveals features of kinase inhibitor selectivity. Nat. Biotechnol., 2011, 29(11), 1039-1045.
[http://dx.doi.org/10.1038/nbt.2017 ] [PMID: 22037377]
[153]
Davis, M.I.; Hunt, J.P.; Herrgard, S.; Ciceri, P.; Wodicka, L.M.; Pallares, G.; Hocker, M.; Treiber, D.K.; Zarrinkar, P.P. Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol., 2011, 29(11), 1046-1051.
[http://dx.doi.org/10.1038/nbt.1990 ] [PMID: 22037378]
[154]
Southan, C.; Sitzmann, M.; Muresan, S. Comparing the chemical structure and protein content of ChEMBL, DrugBank, human metabolome database and the therapeutic target database. Mol. Inform., 2013, 32(11-12), 881-897.
[http://dx.doi.org/10.1002/minf.201300103 ] [PMID: 24533037]
[155]
Ursu, O.; Holmes, J.; Knockel, J.; Bologa, C.G.; Yang, J.J.; Mathias, S.L.; Nelson, S.J.; Oprea, T.I. DrugCentral: online drug compendium. Nucleic Acids Res., 2017, 45(D1), D932-D939.
[http://dx.doi.org/10.1093/nar/gkw993 ] [PMID: 27789690]
[156]
Nguyen, D-T.; Mathias, S.; Bologa, C.; Brunak, S.; Fernandez, N.; Gaulton, A.; Hersey, A.; Holmes, J.; Jensen, L.J.; Karlsson, A.; Liu, G.; Ma’ayan, A.; Mandava, G.; Mani, S.; Mehta, S.; Overington, J.; Patel, J.; Rouillard, A.D.; Schürer, S.; Sheils, T.; Simeonov, A.; Sklar, L.A.; Southall, N.; Ursu, O.; Vidovic, D.; Waller, A.; Yang, J.; Jadhav, A.; Oprea, T.I.; Guha, R. Pharos: collating protein information to shed light on the druggable genome. Nucleic Acids Res., 2017, 45(D1), D995-D1002.
[http://dx.doi.org/10.1093/nar/gkw1072 ] [PMID: 27903890]
[157]
Whirl-Carrillo, M.; McDonagh, E.M.; Hebert, J.M.; Gong, L.; Sangkuhl, K.; Thorn, C.F.; Altman, R.B.; Klein, T.E. Pharmacogenomics knowledge for personalized medicine. Clin. Pharmacol. Ther., 2012, 92(4), 414-417.
[http://dx.doi.org/10.1038/clpt.2012.96 ] [PMID: 22992668]
[158]
Griffith, M.; Griffith, O.L.; Coffman, A.C.; Weible, J.V.; McMichael, J.F.; Spies, N.C.; Koval, J.; Das, I.; Callaway, M.B.; Eldred, J.M.; Miller, C.A.; Subramanian, J.; Govindan, R.; Kumar, R.D.; Bose, R.; Ding, L.; Walker, J.R.; Larson, D.E.; Dooling, D.J.; Smith, S.M.; Ley, T.J.; Mardis, E.R.; Wilson, R.K. DGIdb: mining the druggable genome. Nat. Methods, 2013, 10(12), 1209-1210.
[http://dx.doi.org/10.1038/nmeth.2689 ] [PMID: 24122041]
[159]
Wagner, A.H.; Coffman, A.C.; Ainscough, B.J.; Spies, N.C.; Skidmore, Z.L.; Campbell, K.M.; Krysiak, K.; Pan, D.; McMichael, J.F.; Eldred, J.M.; Walker, J.R.; Wilson, R.K.; Mardis, E.R.; Griffith, M.; Griffith, O.L. DGIdb 2.0: mining clinically relevant drug-gene interactions. Nucleic Acids Res., 2016, 44(D1), D1036-D1044.
[http://dx.doi.org/10.1093/nar/gkv1165 ] [PMID: 26531824]
[160]
Roider, H.G.; Pavlova, N.; Kirov, I.; Slavov, S.; Slavov, T.; Uzunov, Z.; Weiss, B. Drug2Gene: an exhaustive resource to explore effectively the drug-target relation network. BMC Bioinformatics, 2014, 15, 68.
[http://dx.doi.org/10.1186/1471-2105-15-68 ] [PMID: 24618344]
[161]
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.; Spedding, M.; Yu, W.; Harmar, A.J. The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands. Nucleic Acids Res., 2014, 42(Database issue), D1098-D1106.
[http://dx.doi.org/10.1093/nar/gkt1143] [PMID: 24234439]
[162]
Southan, C.; Sharman, J.L.; Benson, H.E.; Faccenda, E.; Pawson, A.J.; Alexander, S.P.; Buneman, O.P.; Davenport, A.P.; McGrath, J.C.; Peters, J.A.; Spedding, M.; Catterall, W.A.; Fabbro, D.; Davies, J.A. NC-IUPHAR. The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands. Nucleic Acids Res., 2016, 44(D1), D1054-D1068.
[http://dx.doi.org/10.1093/nar/gkv1037 ] [PMID: 26464438]
[163]
Koscielny, G.; An, P.; Carvalho-Silva, D.; Cham, J.A.; Fumis, L.; Gasparyan, R.; Hasan, S.; Karamanis, N.; Maguire, M.; Papa, E.; Pierleoni, A.; Pignatelli, M.; Platt, T.; Rowland, F.; Wankar, P.; Bento, A.P.; Burdett, T.; Fabregat, A.; Forbes, S.; Gaulton, A.; Gonzalez, C.Y.; Hermjakob, H.; Hersey, A.; Jupe, S.; Kafkas, Ş.; Keays, M.; Leroy, C.; Lopez, F-J.; Magarinos, M.P.; Malone, J.; McEntyre, J.; Munoz-Pomer Fuentes, A.; O’Donovan, C.; Papatheodorou, I.; Parkinson, H.; Palka, B.; Paschall, J.; Petryszak, R.; Pratanwanich, N.; Sarntivijal, S.; Saunders, G.; Sidiropoulos, K.; Smith, T.; Sondka, Z.; Stegle, O.; Tang, Y.A.; Turner, E.; Vaughan, B.; Vrousgou, O.; Watkins, X.; Martin, M-J.; Sanseau, P.; Vamathevan, J.; Birney, E.; Barrett, J.; Dunham, I. Open Targets: a platform for therapeutic target identification and validation. Nucleic Acids Res., 2017, 45(D1), D985-D994.
[http://dx.doi.org/10.1093/nar/gkw1055 ] [PMID: 27899665]
[164]
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The Protein Data Bank. Nucleic Acids Res., 2000, 28(1), 235-242.
[http://dx.doi.org/10.1093/nar/28.1.235 ] [PMID: 10592235]
[165]
Rose, P.W.; Prlić, A.; Altunkaya, A.; Bi, C.; Bradley, A.R.; Christie, C.H.; Costanzo, L.D.; Duarte, J.M.; Dutta, S.; Feng, Z.; Green, R.K.; Goodsell, D.S.; Hudson, B.; Kalro, T.; Lowe, R.; Peisach, E.; Randle, C.; Rose, A.S.; Shao, C.; Tao, Y-P.; Valasatava, Y.; Voigt, M.; Westbrook, J.D.; Woo, J.; Yang, H.; Young, J.Y.; Zardecki, C.; Berman, H.M.; Burley, S.K. The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res., 2017, 45(D1), D271-D281.
[http://dx.doi.org/10.1093/nar/gkw1000] [PMID: 27794042]
[166]
Yang, J.; Roy, A.; Zhang, Y. BioLiP: a semi-manually curated database for biologically relevant ligand-protein interactions. Nucleic Acids Res., 2013, 41(Database issue), D1096-D1103.
[http://dx.doi.org/10.1093/nar/gks966] [PMID: 23087378]
[167]
Wang, C.; Hu, G.; Wang, K.; Brylinski, M.; Xie, L.; Kurgan, L. PDID: database of molecular-level putative protein-drug interactions in the structural human proteome. Bioinformatics, 2016, 32(4), 579-586.
[http://dx.doi.org/10.1093/bioinformatics/btv597 ] [PMID: 26504143]
[168]
Higueruelo, A.P.; Schreyer, A.; Bickerton, G.R.J.; Pitt, W.R.; Groom, C.R.; Blundell, T.L. Atomic interactions and profile of small molecules disrupting protein-protein interfaces: the TIMBAL database. Chem. Biol. Drug Des., 2009, 74(5), 457-467.
[http://dx.doi.org/10.1111/j.1747-0285.2009.00889.x ] [PMID: 19811506]
[169]
Higueruelo, A.P.; Jubb, H.; Blundell, T.L. TIMBAL v2: update of a database holding small molecules modulating protein-protein interactions. Database (Oxford), 2013, 2013, bat039-bat039.
[http://dx.doi.org/10.1093/database/bat039 ] [PMID: 23766369]
[170]
Bourgeas, R.; Basse, M-J.; Morelli, X.; Roche, P. Atomic analysis of protein-protein interfaces with known inhibitors: the 2P2I database. PLoS One, 2010, 5(3) e9598
[http://dx.doi.org/10.1371/journal.pone.0009598 ] [PMID: 20231898]
[171]
Basse, M.J.; Betzi, S.; Bourgeas, R.; Bouzidi, S.; Chetrit, B.; Hamon, V.; Morelli, X.; Roche, P. 2P2Idb: a structural database dedicated to orthosteric modulation of protein-protein interactions. Nucleic Acids Res., 2013, 41(Database issue), D824-D827.
[http://dx.doi.org/10.1093/nar/gks1002] [PMID: 23203891]
[172]
Basse, M-J.; Betzi, S.; Morelli, X.; Roche, P. 2P2Idb v2: update of a structural database dedicated to orthosteric modulation of protein-protein interactions. Database (Oxford), 2016, 2016, baw007-baw007.
[http://dx.doi.org/10.1093/database/baw007 ] [PMID: 26980515]
[173]
Labbé, C.M.; Laconde, G.; Kuenemann, M.A.; Villoutreix, B.O.; Sperandio, O. iPPI-DB: a manually curated and interactive database of small non-peptide inhibitors of protein-protein interactions. Drug Discov. Today, 2013, 18(19-20), 958-968.
[http://dx.doi.org/10.1016/j.drudis.2013.05.003 ] [PMID: 23688585]
[174]
Labbé, C.M.; Kuenemann, M.A.; Zarzycka, B.; Vriend, G.; Nicolaes, G.A.F.; Lagorce, D.; Miteva, M.A.; Villoutreix, B.O.; Sperandio, O. iPPI-DB: an online database of modulators of protein-protein interactions. Nucleic Acids Res., 2016, 44(D1), D542-D547.
[http://dx.doi.org/10.1093/nar/gkv982 ] [PMID: 26432833]
[175]
Liu, Y.; Hu, B.; Fu, C.; Chen, X. DCDB: drug combination database. Bioinformatics, 2010, 26(4), 587-588.
[http://dx.doi.org/10.1093/bioinformatics/btp697 ] [PMID: 20031966]
[176]
Liu, Y.; Wei, Q.; Yu, G.; Gai, W.; Li, Y.; Chen, X. DCDB 2.0: a major update of the drug combination database. Database (Oxford), 2014, 2014, bau124-bau124.
[http://dx.doi.org/10.1093/database/bau124 ] [PMID: 25539768]
[177]
Juan-Blanco, T.; Duran-Frigola, M.; Aloy, P. IntSide: a web server for the chemical and biological examination of drug side effects. Bioinformatics, 2015, 31(4), 612-613.
[http://dx.doi.org/10.1093/bioinformatics/btu688 ] [PMID: 25380960]
[178]
Ahmed, J.; Meinel, T.; Dunkel, M.; Murgueitio, M.S.; Ad-ams, R.; Blasse, C.; Eckert, A.; Preissner, S.; Preissner, R. CancerResource: a comprehensive database of cancer-relevant proteins and compound interactions supported by experimental knowledge. Nucleic Acids Res., 2011, 39(1), D960-D967.
[http://dx.doi.org/10.1093/nar/gkq910]
[179]
Gohlke, B-O.; Nickel, J.; Otto, R.; Dunkel, M.; Preissner, R. CancerResource--updated database of cancer-relevant proteins, mutations and interacting drugs. Nucleic Acids Res., 2016, 44(D1), D932-D937.
[http://dx.doi.org/10.1093/nar/gkv1283 ] [PMID: 26590406]
[180]
Halling-Brown, M.D.; Bulusu, K.C.; Patel, M.; Tym, J.E.; Al-Lazikani, B. canSAR: an integrated cancer public translational research and drug discovery resource. Nucleic Acids Res., 2012, 40(Database issue), D947-D956.
[http://dx.doi.org/10.1093/nar/gkr881 ] [PMID: 22013161]
[181]
Bulusu, K.C.; Tym, J.E.; Coker, E.A.; Schierz, A.C.; Al-Lazikani, B. canSAR: updated cancer research and drug discovery knowledgebase. Nucleic Acids Res., 2014, 42(Database issue), D1040-D1047.
[http://dx.doi.org/10.1093/nar/gkt1182 ] [PMID: 24304894]
[182]
Tym, J.E.; Mitsopoulos, C.; Coker, E.A.; Razaz, P.; Schierz, A.C.; Antolin, A.A.; Al-Lazikani, B. canSAR: an updated cancer research and drug discovery knowledgebase. Nucleic Acids Res., 2016, 44(D1), D938-D943.
[http://dx.doi.org/10.1093/nar/gkv1030 ] [PMID: 26673713]
[183]
Siramshetty, V.B.; Nickel, J.; Omieczynski, C.; Gohlke, B-O.; Drwal, M.N.; Preissner, R. WITHDRAWN--a resource for withdrawn and discontinued drugs. Nucleic Acids Res., 2016, 44(D1), D1080-D1086.
[http://dx.doi.org/10.1093/nar/gkv1192 ] [PMID: 26553801]
[184]
Chan, W.K.B.; Zhang, H.; Yang, J.; Brender, J.R.; Hur, J.; Özgür, A.; Zhang, Y. GLASS: a comprehensive database for experimentally validated GPCR-ligand associations. Bioinformatics, 2015, 31(18), 3035-3042.
[http://dx.doi.org/10.1093/bioinformatics/btv302 ] [PMID: 25971743]
[185]
He, Z.; Zhang, J.; Shi, X-H.; Hu, L-L.; Kong, X.; Cai, Y-D.; Chou, K-C. Predicting drug-target interaction networks based on functional groups and biological features. PLoS One, 2010, 5(3) e9603
[http://dx.doi.org/10.1371/journal.pone.0009603 ] [PMID: 20300175]
[186]
Xia, Z.; Wu, L-Y.; Zhou, X.; Wong, S.T.C. Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces. BMC Syst. Biol., 2010, 4(2)(Suppl. 2), S6.
[http://dx.doi.org/10.1186/1752-0509-4-S2-S6 ] [PMID: 20840733]
[187]
Yu, W.; Jiang, Z.; Wang, J.; Tao, R. Using feature selection technique for drug-target interaction networks prediction. Curr. Med. Chem., 2011, 18(36), 5687-5693.
[http://dx.doi.org/10.2174/092986711798347270 ] [PMID: 22172073]
[188]
Chen, X.; Liu, M-X.; Yan, G-Y. Drug-target interaction prediction by random walk on the heterogeneous network. Mol. Biosyst., 2012, 8(7), 1970-1978.
[http://dx.doi.org/10.1039/c2mb00002d ] [PMID: 22538619]
[189]
Chen, H.; Zhang, Z. A semi-supervised method for drug-target interaction prediction with consistency in networks. PLoS One, 2013, 8(5) e62975
[http://dx.doi.org/10.1371/journal.pone.0062975 ] [PMID: 23667553]
[190]
van Laarhoven, T.; Marchiori, E. Predicting drug-target interactions for new drug compounds using a weighted nearest neighbor profile. PLoS One, 2013, 8(6) e66952
[http://dx.doi.org/10.1371/journal.pone.0066952 ] [PMID: 23840562]
[191]
Yu, W.; Yan, Y.; Liu, Q.; Wang, J.; Jiang, Z. Predicting drug-target interaction networks of human diseases based on multiple feature information. Pharmacogenomics, 2013, 14(14), 1701-1707.
[http://dx.doi.org/10.2217/pgs.13.162 ] [PMID: 24192119]
[192]
Cao, D-S.; Zhang, L-X.; Tan, G-S.; Xiang, Z.; Zeng, W-B.; Xu, Q-S.; Chen, A.F. Computational prediction of drug target interactions using chemical, biological, and network features. Mol. Inform., 2014, 33(10), 669-681.
[http://dx.doi.org/10.1002/minf.201400009 ] [PMID: 27485302]
[193]
Huang, Y-A.; You, Z-H.; Chen, X. A systematic prediction of drug-target interactions using molecular fingerprints and protein sequences. Curr. Protein Pept. Sci., 2016, 19(5), 468-478.
[http://dx.doi.org/10.2174/1389203718666161122103057] [PMID: 27875970]
[194]
Nascimento, A.C.A.; Prudêncio, R.B.C.; Costa, I.G. A multiple kernel learning algorithm for drug-target interaction prediction. BMC Bioinformatics, 2016, 17(1), 46.
[http://dx.doi.org/10.1186/s12859-016-0890-3 ] [PMID: 26801218]
[195]
Shi, J-Y.; Li, J-X.; Lu, H-M. Predicting existing targets for new drugs base on strategies for missing interactions. BMC Bioinformatics, 2016, 17(8)(Suppl. 8), 282.
[http://dx.doi.org/10.1186/s12859-016-1118-2 ] [PMID: 27585458]
[196]
Wang, L.; You, Z-H.; Chen, X.; Yan, X.; Liu, G.; Zhang, W. RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions using Drug Structure and Protein Sequence Information. Curr. Protein Pept. Sci., 2018, 19(5), 445-454.
[PMID: 27842479]
[197]
Yan, X-Y.; Zhang, S-W.; Zhang, S-Y. Prediction of drug-target interaction by label propagation with mutual interaction information derived from heterogeneous network. Mol. Biosyst., 2016, 12(2), 520-531.
[http://dx.doi.org/10.1039/C5MB00615E ] [PMID: 26675534]
[198]
Buza, K.; Peška, L. Drug–target interaction prediction with Bipartite Local Models and hubness-aware regression. Neurocomputing, 2017, 260, 284-293.
[http://dx.doi.org/10.1016/j.neucom.2017.04.055]
[199]
Keum, J.; Nam, H. SELF-BLM: Prediction of drug-target interactions via self-training SVM. PLoS One, 2017, 12(2) e0171839
[http://dx.doi.org/10.1371/journal.pone.0171839 ] [PMID: 28192537]
[200]
Meng, F-R.; You, Z-H.; Chen, X.; Zhou, Y.; An, J-Y. Pre-diction of Drug-Target Interaction Networks from the Inte-gration of Protein Sequences and Drug Chemical Structures. Molecules, 2017, 22(7), 1119.
[http://dx.doi.org/10.3390/molecules22071119]
[201]
Shen, C.; Ding, Y.; Tang, J.; Xu, X.; Guo, F. An Ameliorated Prediction of Drug-Target Interactions Based on Multi-Scale Discrete Wavelet Transform and Network Features. Int. J. Mol. Sci., 2017, 18(8), 1781.
[http://dx.doi.org/10.3390/ijms18081781 ] [PMID: 28813000]
[202]
Zhang, J.; Zhu, M.; Chen, P.; Wang, B. DrugRPE: Random projection ensemble approach to drug-target interaction pre-diction. Neurocomputing, 2017, 228(Suppl. C), 256-262.
[http://dx.doi.org/10.1016/j.neucom.2016.10.039]
[203]
Bender, A.; Jenkins, J.L.; Scheiber, J.; Sukuru, S.C.K.; Glick, M.; Davies, J.W. How similar are similarity searching methods? A principal component analysis of molecular descriptor space. J. Chem. Inf. Model., 2009, 49(1), 108-119.
[http://dx.doi.org/10.1021/ci800249s ] [PMID: 19123924]
[204]
Cereto-Massagué, A.; Ojeda, M.J.; Valls, C.; Mulero, M.; Garcia-Vallvé, S.; Pujadas, G. Molecular fingerprint similarity search in virtual screening. Methods, 2015, 71, 58-63.
[http://dx.doi.org/10.1016/j.ymeth.2014.08.005 ] [PMID: 25132639]
[205]
Hattori, M.; Okuno, Y.; Goto, S.; Kanehisa, M. Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J. Am. Chem. Soc., 2003, 125(39), 11853-11865.
[http://dx.doi.org/10.1021/ja036030u ] [PMID: 14505407]
[206]
Hattori, M.; Tanaka, N.; Kanehisa, M.; Goto, S. SIMCOMP/SUBCOMP: chemical structure search servers for network analyses. Nucleic Acids Res., 2010, 38(2), W652-W656.
[207]
Willett, P.; Barnard, J.M.; Downs, G.M. Chemical Similarity Searching. J. Chem. Inf. Comput. Sci., 1998, 38(6), 983-996.
[http://dx.doi.org/10.1021/ci9800211]]
[208]
Wood, T.C.; William, P.R. Evolution of protein sequences and structures. J. Mol. Biol., 1999, 291(4), 977-995.
[http://dx.doi.org/10.1006/jmbi.1999.2972]]
[209]
Baker, D.; Sali, A. Protein structure prediction and structural genomics. Science, 2001, 294(5540), 93-96.
[http://dx.doi.org/10.1126/science.1065659 ] [PMID: 11588250]
[210]
Liu, J.; Rost, B. Target space for structural genomics revisited. Bioinformatics, 2002, 18(7), 922-933.
[http://dx.doi.org/10.1093/bioinformatics/18.7.922 ] [PMID: 12117789]
[211]
Ginalski, K. Comparative modeling for protein structure prediction. Curr. Opin. Struct. Biol., 2006, 16(2), 172-177.
[http://dx.doi.org/10.1016/j.sbi.2006.02.003 ] [PMID: 16510277]
[212]
Aravind, L.; Koonin, E.V. Gleaning non-trivial structural, functional and evolutionary information about proteins by it-erative database searches11Edited by J. M.Thornton.J. Mol. Biol., 1999, pp. 287(5), 1023-1040..
[http://dx.doi.org/10.1006/jmbi.1999.2653 ] [PMID: 10222208]
[213]
Wilson, C.A.; Kreychman, J.; Gerstein, M. Assessing annotation transfer for genomics: quantifying the relations between protein sequence, structure and function through traditional and probabilistic scores. J. Mol. Biol., 2000, 297(1), 233-249.
[http://dx.doi.org/10.1006/jmbi.2000.3550 ] [PMID: 10704319]
[214]
Rost, B.; Liu, J.; Nair, R.; Wrzeszczynski, K.O.; Ofran, Y. Automatic prediction of protein function. Cell. Mol. Life Sci., 2003, 60(12), 2637-2650.
[http://dx.doi.org/10.1007/s00018-003-3114-8 ] [PMID: 14685688]
[215]
Lee, D.; Redfern, O.; Orengo, C. Predicting protein function from sequence and structure. Nat. Rev. Mol. Cell Biol., 2007, 8(12), 995-1005.
[http://dx.doi.org/10.1038/nrm2281 ] [PMID: 18037900]
[216]
Sangar, V.; Blankenberg, D.J.; Altman, N.; Lesk, A.M. Quantitative sequence-function relationships in proteins based on gene ontology. BMC Bioinformatics, 2007, 8(1), 294.
[http://dx.doi.org/10.1186/1471-2105-8-294 ] [PMID: 17686158]
[217]
Addou, S.; Rentzsch, R.; Lee, D.; Orengo, C.A. Domain-based and family-specific sequence identity thresholds increase the levels of reliable protein function transfer. J. Mol. Biol., 2009, 387(2), 416-430.
[http://dx.doi.org/10.1016/j.jmb.2008.12.045 ] [PMID: 19135455]
[218]
Clark, W.T.; Radivojac, P. Analysis of protein function and its prediction from amino acid sequence. Proteins, 2011, 79(7), 2086-2096.
[http://dx.doi.org/10.1002/prot.23029 ] [PMID: 21671271]
[219]
Altschul, S.F.; Madden, T.L.; Schäffer, A.A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, D.J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res., 1997, 25(17), 3389-3402.
[http://dx.doi.org/10.1093/nar/25.17.3389 ] [PMID: 9254694]
[220]
Smith, T.F.; Waterman, M.S. Identification of common molecular subsequences. J. Mol. Biol., 1981, 147(1), 195-197.
[http://dx.doi.org/10.1016/0022-2836(81)90087-5 ] [PMID: 7265238]
[221]
ATC classification index with DDDs; WHO Collaborating Centre for Drug Statistics Methodology: Oslo, Norway, 2017.
[222]
Kuhn, M.; Campillos, M.; Letunic, I.; Jensen, L.J.; Bork, P. A side effect resource to capture phenotypic effects of drugs. Mol. Syst. Biol., 2010, 6, 343.
[http://dx.doi.org/10.1038/msb.2009.98 ] [PMID: 20087340]
[223]
Kuhn, M.; Letunic, I.; Jensen, L.J.; Bork, P. The SIDER database of drugs and side effects. Nucleic Acids Res., 2016, 44(D1), D1075-D1079.
[http://dx.doi.org/10.1093/nar/gkv1075 ] [PMID: 26481350]
[224]
Cheng, F.; Li, W.; Wang, X.; Zhou, Y.; Wu, Z.; Shen, J.; Tang, Y. Adverse drug events: database construction and in silico prediction. J. Chem. Inf. Model., 2013, 53(4), 744-752.
[http://dx.doi.org/10.1021/ci4000079 ] [PMID: 23521697]
[225]
Weininger, D. SMILES, a chemical language and infor-mation system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci., 1988, 28(1), 31-36.
[http://dx.doi.org/10.1021/ci00057a005]
[226]
Dalby, A.; Nourse, J.G.; Hounshell, W.D.; Gushurst, A.K.I.; Grier, D.L.; Leland, B.A.; Laufer, J. Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited. J. Chem. Inf. Comput. Sci., 1992, 32(3), 244-255.
[http://dx.doi.org/10.1021/ci00007a012]
[227]
Rogers, D.; Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model., 2010, 50(5), 742-754.
[http://dx.doi.org/10.1021/ci100050t ] [PMID: 20426451]
[228]
Peng, Z.; Mizianty, M.J.; Kurgan, L. Genome-scale prediction of proteins with long intrinsically disordered regions. Proteins, 2014, 82(1), 145-158.
[http://dx.doi.org/10.1002/prot.24348 ] [PMID: 23798504]
[229]
Peng, Z.; Yan, J.; Fan, X.; Mizianty, M.J.; Xue, B.; Wang, K.; Hu, G.; Uversky, V.N.; Kurgan, L. Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in all domains of life. Cell. Mol. Life Sci., 2015, 72(1), 137-151.
[http://dx.doi.org/10.1007/s00018-014-1661-9 ] [PMID: 24939692]
[230]
Mark, W-Y.; Liao, J.C.C.; Lu, Y.; Ayed, A.; Laister, R.; Szymczyna, B.; Chakrabartty, A.; Arrowsmith, C.H. Characterization of segments from the central region of BRCA1: an intrinsically disordered scaffold for multiple protein-protein and protein-DNA interactions? J. Mol. Biol., 2005, 345(2), 275-287.
[http://dx.doi.org/10.1016/j.jmb.2004.10.045 ] [PMID: 15571721]
[231]
Cheng, Y.; LeGall, T.; Oldfield, C.J.; Dunker, A.K.; Uversky, V.N. Abundance of intrinsic disorder in protein associated with cardiovascular disease. Biochemistry, 2006, 45(35), 10448-10460.
[http://dx.doi.org/10.1021/bi060981d ] [PMID: 16939197]
[232]
Uversky, V.N.; Oldfield, C.J.; Dunker, A.K. Intrinsically disordered proteins in human diseases: introducing the D2 concept. Annu. Rev. Biophys., 2008, 37(1), 215-246.
[http://dx.doi.org/10.1146/annurev.biophys.37.032807.125924 ] [PMID: 18573080]
[233]
Midic, U.; Oldfield, C.J.; Dunker, A.K.; Obradovic, Z.; Uversky, V.N. Unfoldomics of human genetic diseases: illustrative examples of ordered and intrinsically disordered members of the human diseasome. Protein Pept. Lett., 2009, 16(12), 1533-1547.
[http://dx.doi.org/10.2174/092986609789839377 ] [PMID: 20001916]
[234]
Uversky, V.N.; Oldfield, C.J.; Midic, U.; Xie, H.; Xue, B.; Vucetic, S.; Iakoucheva, L.M.; Obradovic, Z.; Dunker, A.K. Unfoldomics of human diseases: linking protein intrinsic disorder with diseases. BMC Genomics, 2009, 10(1)(Suppl. 1), S7.
[http://dx.doi.org/10.1186/1471-2164-10-S1-S7 ] [PMID: 19594884]
[235]
Rajagopalan, K.; Mooney, S.M.; Parekh, N.; Getzenberg, R.H.; Kulkarni, P. A majority of the cancer/testis antigens are intrinsically disordered proteins. J. Cell. Biochem., 2011, 112(11), 3256-3267.
[http://dx.doi.org/10.1002/jcb.23252 ] [PMID: 21748782]
[236]
Casu, F.; Duggan, B.M.; Hennig, M. The arginine-rich RNA-binding motif of HIV-1 Rev is intrinsically disordered and folds upon RRE binding. Biophys. J., 2013, 105(4), 1004-1017.
[http://dx.doi.org/10.1016/j.bpj.2013.07.022 ] [PMID: 23972852]
[237]
Uversky, V.N.; Davé, V.; Iakoucheva, L.M.; Malaney, P.; Metallo, S.J.; Pathak, R.R.; Joerger, A.C. Pathological unfoldomics of uncontrolled chaos: intrinsically disordered proteins and human diseases. Chem. Rev., 2014, 114(13), 6844-6879.
[http://dx.doi.org/10.1021/cr400713r ] [PMID: 24830552]
[238]
Ward, J.J.; Sodhi, J.S.; McGuffin, L.J.; Buxton, B.F.; Jones, D.T. Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J. Mol. Biol., 2004, 337(3), 635-645.
[http://dx.doi.org/10.1016/j.jmb.2004.02.002 ] [PMID: 15019783]
[239]
Kathiriya, J.J.; Pathak, R.R.; Clayman, E.; Xue, B.; Uversky, V.N.; Davé, V. Presence and utility of intrinsically disordered regions in kinases. Mol. Biosyst., 2014, 10(11), 2876-2888.
[http://dx.doi.org/10.1039/C4MB00224E ] [PMID: 25099472]
[240]
Wang, C.; Uversky, V.N.; Kurgan, L. Disordered nucleiome: Abundance of intrinsic disorder in the DNA- and RNA-binding proteins in 1121 species from Eukaryota, Bacteria and Archaea. Proteomics, 2016, 16(10), 1486-1498.
[http://dx.doi.org/10.1002/pmic.201500177 ] [PMID: 27037624]
[241]
DeForte, S.; Uversky, V.N. Not an exception to the rule: the functional significance of intrinsically disordered protein regions in enzymes. Mol. Biosyst., 2017, 13(3), 463-469.
[http://dx.doi.org/10.1039/C6MB00741D ] [PMID: 28098335]
[242]
Imming, P.; Sinning, C.; Meyer, A. Drugs, their targets and the nature and number of drug targets. Nat. Rev. Drug Discov., 2006, 5(10), 821-834.
[http://dx.doi.org/10.1038/nrd2132 ] [PMID: 17016423]


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VOLUME: 27
ISSUE: 35
Year: 2020
Published on: 28 October, 2020
Page: [5856 - 5886]
Pages: 31
DOI: 10.2174/0929867326666190808154841
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