Computational Drug Repurposing: Current Trends

Author(s): Berin Karaman*, Wolfgang Sippl.

Journal Name: Current Medicinal Chemistry

Volume 26 , Issue 28 , 2019

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Abstract:

Biomedical discovery has been reshaped upon the exploding digitization of data which can be retrieved from a number of sources, ranging from clinical pharmacology to cheminformatics-driven databases. Now, supercomputing platforms and publicly available resources such as biological, physicochemical, and clinical data, can all be integrated to construct a detailed map of signaling pathways and drug mechanisms of action in relation to drug candidates. Recent advancements in computer-aided data mining have facilitated analyses of ‘big data’ approaches and the discovery of new indications for pre-existing drugs has been accelerated. Linking gene-phenotype associations to predict novel drug-disease signatures or incorporating molecular structure information of drugs and protein targets with other kinds of data derived from systems biology provide great potential to accelerate drug discovery and improve the success of drug repurposing attempts. In this review, we highlight commonly used computational drug repurposing strategies, including bioinformatics and cheminformatics tools, to integrate large-scale data emerging from the systems biology, and consider both the challenges and opportunities of using this approach. Moreover, we provide successful examples and case studies that combined various in silico drug-repurposing strategies to predict potential novel uses for known therapeutics.

Keywords: Drug repurposing, bioinformatics, cheminformatics, screening, network pharmacology, systems biology.

[1]
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]
[2]
Hughes, J.P.; Rees, S.; Kalindjian, S.B.; Philpott, K.L. Principles of early drug discovery. Br. J. Pharmacol., 2011, 162(6), 1239-1249.
[http://dx.doi.org/10.1111/j.1476-5381.2010.01127.x] [PMID: 21091654]
[3]
Kola, I.; Landis, J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov., 2004, 3(8), 711-715.
[http://dx.doi.org/10.1038/nrd1470] [PMID: 15286737]
[4]
Li, J.; Zheng, S.; Chen, B.; Butte, A.J.; Swamidass, S.J.; Lu, Z. A survey of current trends in computational drug repositioning. Brief. Bioinform., 2016, 17(1), 2-12.
[http://dx.doi.org/10.1093/bib/bbv020] [PMID: 25832646]
[5]
Shaughnessy, A.F. Old drugs, new tricks. BMJ, 2011, 342, d741.
[http://dx.doi.org/10.1136/bmj.d741] [PMID: 21307112]
[6]
Vanhaelen, Q.; Mamoshina, P.; Aliper, A.M.; Artemov, A.; Lezhnina, K.; Ozerov, I.; Labat, I.; Zhavoronkov, A. Design of efficient computational workflows for in silico drug repurposing. Drug Discov. Today, 2017, 22(2), 210-222.
[http://dx.doi.org/10.1016/j.drudis.2016.09.019] [PMID: 27693712]
[7]
Kim, J.H.; Scialli, A.R. Thalidomide: the tragedy of birth defects and the effective treatment of disease. Toxicol. Sci., 2011, 122(1), 1-6.
[http://dx.doi.org/10.1093/toxsci/kfr088]
[8]
Matthews, S.J.; McCoy, C. Thalidomide: A review of approved and investigational uses. Clin. Ther., 2003, 25(2), 342-395.
[http://dx.doi.org/10.1016/S0149-2918(03)80085-1] [PMID: 12749503]
[9]
Ghofrani, H.A.; Osterloh, I.H.; Grimminger, F. Sildenafil: from angina to erectile dysfunction to pulmonary hypertension and beyond. Nat. Rev. Drug Discov., 2006, 5(8), 689-702.
[http://dx.doi.org/10.1038/nrd2030] [PMID: 16883306]
[10]
Kushida, C.A. Ropinirole for the treatment of restless legs syndrome. Neuropsychiatr. Dis. Treat., 2006, 2(4), 407-419.
[http://dx.doi.org/10.2147/nedt.2006.2.4.407] [PMID: 19412490]
[11]
Loging, W.; Rodriguez-Esteban, R.; Hill, J.; Freeman, T.; Miglietta, J. Cheminformatic/bioinformatic analysis of large corporate databases: Application to drug repurposing. Drug Discov. Today Ther. Strateg., 2011, 8(3), 109-116.
[http://dx.doi.org/10.1016/j.ddstr.2011.06.004]
[12]
Hurle, M.R.; Yang, L.; Xie, Q.; Rajpal, D.K.; Sanseau, P.; Agarwal, P. Computational drug repositioning: From data to therapeutics. Clin. Pharmacol. Ther., 2013, 93(4), 335-341.
[http://dx.doi.org/10.1038/clpt.2013.1] [PMID: 23443757]
[13]
Oulas, A.; Minadakis, G.; Zachariou, M.; Sokratous, K.; Bourdakou, M.M.; Spyrou, G.M. Systems bioinformatics: Increasing precision of computational diagnostics and therapeutics through network-based approaches. Brief. Bioinform., 2019, 20(3), 806-824.
[PMID: 29186305]
[14]
Hodos, R.A.; Kidd, B.A.; Khader, S.; Readhead, B.P.; Dudley, J.T. Computational approaches to drug repurpos-ing and pharmacology. Wiley Interdiscip. Rev. Syst. Biol. Med., 2016, 8(3), 186-210.
[http://dx.doi.org/10.1002/wsbm.1337] [PMID: 27080087]
[15]
Kinnings, S.L.; Liu, N.; Buchmeier, N.; Tonge, P.J.; Xie, L.; Bourne, P.E. Drug discovery using chemical systems biology: repositioning the safe medicine Comtan to treat multi-drug and extensively drug resistant tuberculosis. PLOS Comput. Biol., 2009, 5(7)e1000423
[http://dx.doi.org/10.1371/journal.pcbi.1000423] [PMID: 19578428]
[16]
Baker, N.C.; Ekins, S.; Williams, A.J.; Tropsha, A. A bibliometric review of drug repurposing. Drug Discov. Today, 2018, 23(3), 661-672.
[http://dx.doi.org/10.1016/j.drudis.2018.01.018] [PMID: 29330123]
[17]
March-Vila, E.; Pinzi, L.; Sturm, N.; Tinivella, A.; Engkvist, O.; Chen, H.; Rastelli, G. On the integration of in silico drug design methods for drug repurposing. Front. Pharmacol., 2017, 8, 298.
[http://dx.doi.org/10.3389/fphar.2017.00298] [PMID: 28588497]
[18]
Oulas, A.; Minadakis, G.; Zachariou, M.; Sokratous, K.; Bourdakou, M.M.; Spyrou, G.M. Systems bioinformatics: Increasing precision of computational diagnostics and therapeutics through network-based approaches. Brief. Bioinform., 2017, bbx151-bbx151.
[19]
Montero-Melendez, T.; Perretti, M. Connections in pharmacology: Innovation serving translational medicine. Drug Discov. Today, 2014, 19(7), 820-823.
[http://dx.doi.org/10.1016/j.drudis.2013.11.022] [PMID: 24316023]
[20]
Wu, Z.; Wang, Y.; Chen, L. Network-based drug repositioning. Mol. Biosyst., 2013, 9(6), 1268-1281.
[http://dx.doi.org/10.1039/c3mb25382a] [PMID: 23493874]
[21]
Hodos, R.A.; Kidd, B.A.; Shameer, K.; Readhead, B.P.; Dudley, J.T. In silico methods for drug repurposing and pharmacology. Wiley Interdiscip. Rev. Syst. Biol. Med., 2016, 8(3), 186-210.
[http://dx.doi.org/10.1002/wsbm.1337] [PMID: 27080087]
[22]
Sahu, N.U.; Kharkar, P.S. Computational drug repositioning: A lateral approach to traditional drug discovery? Curr. Top. Med. Chem., 2016, 16(19), 2069-2077.
[http://dx.doi.org/10.2174/1568026616666160216153249] [PMID: 26881717]
[23]
Seo, H.; Kim, W.; Lee, J.; Youn, B. Network-based approaches for anticancer therapy (Review). Int. J. Oncol., 2013, 43(6), 1737-1744.
[http://dx.doi.org/10.3892/ijo.2013.2114] [PMID: 24085339]
[24]
Hu, Y-S.; Xin, J.; Hu, Y.; Zhang, L.; Wang, J. Analyzing the genes related to Alzheimer’s disease via a network and pathway-based approach. Alzheimers Res. Ther., 2017, 9(1), 29.
[http://dx.doi.org/10.1186/s13195-017-0252-z] [PMID: 28446202]
[25]
Iorio, F.; Rittman, T.; Ge, H.; Menden, M.; Saez-Rodriguez, J. Transcriptional data: A new gateway to drug repositioning? Drug Discov. Today, 2013, 18(7-8), 350-357.
[http://dx.doi.org/10.1016/j.drudis.2012.07.014] [PMID: 22897878]
[26]
Sanseau, P.; Agarwal, P.; Barnes, M.R.; Pastinen, T.; Richards, J.B.; Cardon, L.R.; Mooser, V. Use of genome-wide association studies for drug repositioning. Nat. Biotechnol., 2012, 30(4), 317-320.
[http://dx.doi.org/10.1038/nbt.2151] [PMID: 22491277]
[27]
Lamb, J.; Crawford, E.D.; Peck, D.; Modell, J.W.; Blat, I.C.; Wrobel, M.J.; Lerner, J.; Brunet, J.P.; Subramanian, A.; Ross, K.N.; Reich, M.; Hieronymus, H.; Wei, G.; Armstrong, S.A.; Haggarty, S.J.; Clemons, P.A.; Wei, R.; Carr, S.A.; Lander, E.S.; Golub, T.R. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science, 2006, 313(5795), 1929-1935.
[http://dx.doi.org/10.1126/science.1132939] [PMID: 17008526]
[28]
Lamb, J. The Connectivity Map: a new tool for biomedical research. Nat. Rev. Cancer, 2007, 7(1), 54-60.
[http://dx.doi.org/10.1038/nrc2044] [PMID: 17186018]
[29]
Barrett, T.; Suzek, T.O.; Troup, D.B.; Wilhite, S.E.; Ngau, W.C.; Ledoux, P.; Rudnev, D.; Lash, A.E.; Fujibuchi, W.; Edgar, R. NCBI GEO: mining millions of expression profiles--database and tools. Nucleic Acids Res., 2005, 33(Database issue), D562-D566.
[30]
Qu, X.A.; Rajpal, D.K. Applications of Connectivity Map in drug discovery and development. Drug Discov. Today, 2012, 17(23-24), 1289-1298.
[http://dx.doi.org/10.1016/j.drudis.2012.07.017] [PMID: 22889966]
[31]
Ringwald, M.; Eppig, J.T.; Richardson, J.E. GXD: Integrated access to gene expression data for the laboratory mouse. Trends Genet., 2000, 16(4), 188-190.
[http://dx.doi.org/10.1016/S0168-9525(00)01983-1] [PMID: 10729835]
[32]
Sherlock, G.; Hernandez-Boussard, T.; Kasarskis, A.; Binkley, G.; Matese, J.C.; Dwight, S.S.; Kaloper, M.; Weng, S.; Jin, H.; Ball, C.A.; Eisen, M.B.; Spellman, P.T.; Brown, P.O.; Botstein, D.; Cherry, J.M. The stanford microarray database. Nucleic Acids Res., 2001, 29(1), 152-155.
[http://dx.doi.org/10.1093/nar/29.1.152] [PMID: 11125075]
[33]
Leinonen, R.; Sugawara, H.; Shumway, M. International nucleotide sequence database C. The sequence read archive. Nucleic Acids Res., 2011, 39(Database issue), D19-D21.
[34]
Aurrecoechea, C.; Barreto, A.; Basenko, E.Y.; Brestelli, J.; Brunk, B.P.; Cade, S.; Crouch, K.; Doherty, R.; Falke, D.; Fischer, S.; Gajria, B.; Harb, O.S.; Heiges, M.; Hertz-Fowler, C.; Hu, S.; Iodice, J.; Kissinger, J.C.; Lawrence, C.; Li, W.; Pinney, D.F.; Pulman, J.A.; Roos, D.S.; Shanmugasundram, A.; Silva-Franco, F.; Steinbiss, S.; Stoeckert, C.J., Jr; Spruill, D.; Wang, H.; Warrenfeltz, S.; Zheng, J. EuPathDB: the eukaryotic pathogen genomics database resource. Nucleic Acids Res., 2017, 45(D1), D581-D591.
[http://dx.doi.org/10.1093/nar/gkw1105] [PMID: 27903906]
[35]
Wooller, S.K.; Benstead-Hume, G.; Chen, X.; Ali, Y.; Pearl, F.M.G. Bioinformatics in translational drug discovery. Biosci. Rep., 2017, 37(4)BSR20160180
[http://dx.doi.org/10.1042/BSR20160180] [PMID: 28487472]
[36]
Dudley, J.T.; Sirota, M.; Shenoy, M.; Pai, R.K.; Roedder, S.; Chiang, A.P.; Morgan, A.A.; Sarwal, M.M.; Pasricha, P.J.; Butte, A.J. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci. Transl. Med., 2011, 3(96)96ra76
[http://dx.doi.org/10.1126/scitranslmed.3002648] [PMID: 21849664]
[37]
Tusher, V.G.; Tibshirani, R.; Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA, 2001, 98(9), 5116-5121.
[http://dx.doi.org/10.1073/pnas.091062498] [PMID: 11309499]
[38]
Sirota, M.; Dudley, J.T.; Kim, J.; Chiang, A.P.; Morgan, A.A.; Sweet-Cordero, A.; Sage, J.; Butte, A.J. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci. Transl. Med., 2011, 3(96)96ra77
[http://dx.doi.org/10.1126/scitranslmed.3001318] [PMID: 21849665]
[39]
Konstantopoulos, N.; Foletta, V.C.; Segal, D.H.; Shields, K.A.; Sanigorski, A.; Windmill, K.; Swinton, C.; Connor, T.; Wanyonyi, S.; Dyer, T.D.; Fahey, R.P.; Watt, R.A.; Curran, J.E.; Molero, J.C.; Krippner, G.; Collier, G.R.; James, D.E.; Blangero, J.; Jowett, J.B.; Walder, K.R. A gene expression signature for insulin resistance. Physiol. Genomics, 2011, 43(3), 110-120.
[http://dx.doi.org/10.1152/physiolgenomics.00115.2010] [PMID: 21081660]
[40]
Swinney, D.C.; Anthony, J. How were new medicines discovered? Nat. Rev. Drug Discov., 2011, 10(7), 507-519.
[http://dx.doi.org/10.1038/nrd3480] [PMID: 21701501]
[41]
Chong, C.R.; Chen, X.; Shi, L.; Liu, J.O.; Sullivan, D.J., Jr A clinical drug library screen identifies astemizole as an antimalarial agent. Nat. Chem. Biol., 2006, 2(8), 415-416.
[http://dx.doi.org/10.1038/nchembio806] [PMID: 16816845]
[42]
Xia, X.; Yang, J.; Li, F.; Li, Y.; Zhou, X.; Dai, Y.; Wong, S.T. Image-based chemical screening identifies drug efflux inhibitors in lung cancer cells. Cancer Res., 2010, 70(19), 7723-7733.
[http://dx.doi.org/10.1158/0008-5472.CAN-09-4360] [PMID: 20841476]
[43]
Saporito, M.S.; Reaume, A.G. theraTRACE®: a mechanism unbiased in vivo platform for phenotypic screening and drug repositioning. Drug Discov. Today Ther. Strateg., 2011, 8(3), 89-95.
[http://dx.doi.org/10.1016/j.ddstr.2011.06.002]
[44]
Lipinski, C.A.; Stam, J.G.; Pereira, J.N.; Ackerman, N.R.; Hess, H.J. Bronchodilator and antiulcer phenoxypyrimidinones. J. Med. Chem., 1980, 23(9), 1026-1031.
[http://dx.doi.org/10.1021/jm00183a012] [PMID: 7411545]
[45]
Saporito, M.S.; Ochman, A.R.; Lipinski, C.A.; Handler, J.A.; Reaume, A.G. MLR-1023 is a potent and selective allosteric activator of Lyn kinase in vitro that improves glucose tolerance in vivo. J. Pharmacol. Exp. Ther., 2012, 342(1), 15-22.
[http://dx.doi.org/10.1124/jpet.112.192096] [PMID: 22473614]
[46]
Welter, D.; MacArthur, J.; Morales, J.; Burdett, T.; Hall, P.; Junkins, H.; Klemm, A.; Flicek, P.; Manolio, T.; Hindorff, L.; Parkinson, H. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res., 2014, 42(Database issue), D1001-D1006.
[http://dx.doi.org/10.1093/nar/gkt1229] [PMID: 24316577]
[47]
MacArthur, J.; Bowler, E.; Cerezo, M.; Gil, L.; Hall, P.; Hastings, E.; Junkins, H.; McMahon, A.; Milano, A.; Morales, J.; Pendlington, Z.M.; Welter, D.; Burdett, T.; Hindorff, L.; Flicek, P.; Cunningham, F.; Parkinson, H. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res., 2017, 45(D1), D896-D901.
[http://dx.doi.org/10.1093/nar/gkw1133] [PMID: 27899670]
[48]
Wu, X.; Liu, Q.; Jiang, R. Align human interactome with phenome to identify causative genes and networks underlying disease families. Bioinformatics, 2009, 25(1), 98-104.
[http://dx.doi.org/10.1093/bioinformatics/btn593] [PMID: 19010805]
[49]
Denny, J.C.; Bastarache, L.; Ritchie, M.D.; Carroll, R.J.; Zink, R.; Mosley, J.D.; Field, J.R.; Pulley, J.M.; Ramirez, A.H.; Bowton, E.; Basford, M.A.; Carrell, D.S.; Peissig, P.L.; Kho, A.N.; Pacheco, J.A.; Rasmussen, L.V.; Crosslin, D.R.; Crane, P.K.; Pathak, J.; Bielinski, S.J.; Pendergrass, S.A.; Xu, H.; Hindorff, L.A.; Li, R.; Manolio, T.A.; Chute, C.G.; Chisholm, R.L.; Larson, E.B.; Jarvik, G.P.; Brilliant, M.H.; McCarty, C.A.; Kullo, I.J.; Haines, J.L.; Crawford, D.C.; Masys, D.R.; Roden, D.M. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol., 2013, 31(12), 1102-1110.
[http://dx.doi.org/10.1038/nbt.2749] [PMID: 24270849]
[50]
Issa, N.T.; Byers, S.W.; Dakshanamurthy, S. Big data: The next frontier for innovation in therapeutics and healthcare. Expert Rev. Clin. Pharmacol., 2014, 7(3), 293-298.
[http://dx.doi.org/10.1586/17512433.2014.905201] [PMID: 24702684]
[51]
Paik, H.; Chen, B.; Sirota, M.; Hadley, D.; Butte, A.J. Integrating clinical phenotype and gene expression data to prioritize novel drug uses. Integrating clinical phenotype and gene expression data to prioritize novel drug uses. CPT Pharmacometrics Syst. Pharmacol., 2016, 5(11), 599-607.
[http://dx.doi.org/10.1002/psp4.12108] [PMID: 27860440]
[52]
Bisgin, H.; Liu, Z.; Fang, H.; Kelly, R.; Xu, X.; Tong, W. A phenome-guided drug repositioning through a latent variable model. BMC Bioinformatics, 2014, 15(1), 267.
[http://dx.doi.org/10.1186/1471-2105-15-267] [PMID: 25103881]
[53]
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]
[54]
Lechner, M.; Höhn, V.; Brauner, B.; Dunger, I.; Fobo, G.; Frishman, G.; Montrone, C.; Kastenmüller, G.; Waegele, B.; Ruepp, A. CIDeR: multifactorial interaction networks in human diseases. Genome Biol., 2012, 13(7), R62.
[http://dx.doi.org/10.1186/gb-2012-13-7-r62] [PMID: 22809392]
[55]
Grigoriev, I.; zu Castell, W.; Tsvetkov, P.; Antonov, A.V. AERS spider: An online interactive tool to mine statistical associations in Adverse Event Reporting System. Pharmacoepidemiol. Drug Saf., 2014, 23(8), 795-801.
[http://dx.doi.org/10.1002/pds.3561] [PMID: 24677538]
[56]
Fischer, E. Berichte der Deutschen chemischen Gesellschaft. Ber. Dtsch. Chem. Ges., 1894, 27, 2985-2993.
[http://dx.doi.org/10.1002/cber.18940270364]
[57]
Koshland, D.E. Application of a theory of enzyme specificity to protein synthesis. Proc. Natl. Acad. Sci. USA, 1958, 44(2), 98-104.
[http://dx.doi.org/10.1073/pnas.44.2.98] [PMID: 16590179]
[58]
Duran-Frigola, M.; Aloy, P. Recycling side-effects into clinical markers for drug repositioning. Genome Med., 2012, 4(1), 3-3.
[http://dx.doi.org/10.1186/gm302] [PMID: 22283977]
[59]
Liu, Z.; Fang, H.; Reagan, K.; Xu, X.; Mendrick, D.L.; Slikker, W., Jr; Tong, W. In silico drug repositioning: what we need to know. Drug Discov. Today, 2013, 18(3-4), 110-115.
[http://dx.doi.org/10.1016/j.drudis.2012.08.005] [PMID: 22935104]
[60]
Jin, G.; Wong, S.T. Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. Drug Discov. Today, 2014, 19(5), 637-644.
[http://dx.doi.org/10.1016/j.drudis.2013.11.005] [PMID: 24239728]
[61]
Hu, G.; Agarwal, P. Human disease-drug network based on genomic expression profiles. PLoS One, 2009, 4(8)e6536
[http://dx.doi.org/10.1371/journal.pone.0006536] [PMID: 19657382]
[62]
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(Database issue), D901-D906.
[http://dx.doi.org/10.1093/nar/gkm958] [PMID: 18048412]
[63]
Nagaraj, A.B.; Wang, Q.Q.; Joseph, P.; Zheng, C.; Chen, Y.; Kovalenko, O.; Singh, S.; Armstrong, A.; Resnick, K.; Zanotti, K.; Waggoner, S.; Xu, R.; DiFeo, A. Using a novel computational drug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment. Oncogene, 2018, 37(3), 403-414.
[http://dx.doi.org/10.1038/onc.2017.328] [PMID: 28967908]
[64]
Smith, C.L.; Blake, J.A.; Kadin, J.A.; Richardson, J.E.; Bult, C.J. Mouse genome database group. Mouse Genome Database (MGD)-2018: knowledgebase for the laboratory mouse. Nucleic Acids Res., 2018, 46(D1), D836-D842.
[http://dx.doi.org/10.1093/nar/gkx1006] [PMID: 29092072]
[65]
Krupke, D.M.; Begley, D.A.; Sundberg, J.P.; Bult, C.J.; Eppig, J.T. The mouse tumor biology database. Nat. Rev. Cancer, 2008, 8(6), 459-465.
[http://dx.doi.org/10.1038/nrc2390] [PMID: 18432250]
[66]
Harris, M.A.; Clark, J.; Ireland, A.; Lomax, J.; Ashburner, M.; Foulger, R.; Eilbeck, K.; Lewis, S.; Marshall, B.; Mungall, C.; Richter, J.; Rubin, G.M.; Blake, J.A.; Bult, C.; Dolan, M.; Drabkin, H.; Eppig, J.T.; Hill, D.P.; Ni, L.; Ringwald, M.; Balakrishnan, R.; Cherry, J.M.; Christie, K.R.; Costanzo, M.C.; Dwight, S.S.; Engel, S.; Fisk, D.G.; Hirschman, J.E.; Hong, E.L.; Nash, R.S.; Sethuraman, A.; Theesfeld, C.L.; Botstein, D.; Dolinski, K.; Feierbach, B.; Berardini, T.; Mundodi, S.; Rhee, S.Y.; Apweiler, R.; Bar-rell, D.; Camon, E.; Dimmer, E.; Lee, V.; Chisholm, R.; Gaudet, P.; Kibbe, W.; Kishore, R.; Schwarz, E.M.; Stern-berg, P.; Gwinn, M.; Hannick, L.; Wortman, J.; Berriman, M.; Wood, V.; de la Cruz, N.; Tonellato, P.; Jaiswal, P.; Seigfried, T.; White, R. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res., 2004, 32(Database issue), D258-D261.
[67]
Smith, C.L.; Eppig, J.T. The mammalian phenotype ontology: Enabling robust annotation and comparative analysis. Wiley Interdiscip. Rev. Syst. Biol. Med., 2009, 1(3), 390-399.
[http://dx.doi.org/10.1002/wsbm.44] [PMID: 20052305]
[68]
Köhler, S.; Vasilevsky, N.A.; Engelstad, M.; Foster, E.; McMurry, J.; Aymé, S.; Baynam, G.; Bello, S.M.; Boerkoel, C.F.; Boycott, K.M.; Brudno, M.; Buske, O.J.; Chinnery, P.F.; Cipriani, V.; Connell, L.E.; Dawkins, H.J.S.; DeMare, L.E.; Devereau, A.D.; de Vries, B.B.; Firth, H.V.; Freson, K.; Greene, D.; Hamosh, A.; Helbig, I.; Hum, C.; Jähn, J.A.; James, R.; Krause, R.; F, Laulederkind. S.J.; Lochmüller, H.; Lyon, G.J.; Ogishima, S.; Olry, A.; Ouwehand, W.H.; Pontikos, N.; Rath, A.; Schaefer, F.; Scott, R.H.; Segal, M.; Sergouniotis, P.I.; Sever, R.; Smith, C.L.; Straub, V.; Thompson, R.; Turner, C.; Turro, E.; Veltman, M.W.; Vulliamy, T.; Yu, J.; von Ziegenweidt, J.; Zankl, A.; Züchner, S.; Zemojtel, T.; Jacobsen, J.O.; Groza, T.; Smedley, D.; Mungall, C.J.; Haendel, M.; Robinson, P.N. The human phenotype ontology in 2017. Nucleic Acids Res., 2017, 45(D1), D865-D876.
[http://dx.doi.org/10.1093/nar/gkw1039] [PMID: 27899602]
[69]
Schriml, L.M.; Arze, C.; Nadendla, S.; Chang, Y-W.W.; Mazaitis, M.; Felix, V.; Feng, G.; Kibbe, W.A. Disease ontology: A backbone for disease semantic integration. Nucleic Acids Res., 2012, 40(Database issue), D940-D946.
[http://dx.doi.org/10.1093/nar/gkr972] [PMID: 22080554]
[70]
Kibbe, W.A.; Arze, C.; Felix, V.; Mitraka, E.; Bolton, E.; Fu, G.; Mungall, C.J.; Binder, J.X.; Malone, J.; Vasant, D.; Parkinson, H.; Schriml, L.M. Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res., 2015, 43(Database issue), D1071-D1078.
[http://dx.doi.org/10.1093/nar/gku1011] [PMID: 25348409]
[71]
Natale, D.A.; Arighi, C.N.; Barker, W.C.; Blake, J.A.; Bult, C.J.; Caudy, M.; Drabkin, H.J.; D’Eustachio, P.; Evsikov, A.V.; Huang, H.; Nchoutmboube, J.; Roberts, N.V.; Smith, B.; Zhang, J.; Wu, C.H. The protein ontology: A structured representation of protein forms and complexes. Nucleic Acids Res., 2011, 39(Database issue), D539-D545.
[http://dx.doi.org/10.1093/nar/gkq907] [PMID: 20935045]
[72]
Mohun, T.; Adams, D.J.; Baldock, R.; Bhattacharya, S.; Copp, A.J.; Hemberger, M.; Houart, C.; Hurles, M.E.; Robertson, E.; Smith, J.C.; Weaver, T.; Weninger, W. Deciphering the Mechanisms of Developmental Disorders (DMDD): A new programme for phenotyping embryonic lethal mice. Dis. Model. Mech., 2013, 6(3), 562-566.
[http://dx.doi.org/10.1242/dmm.011957] [PMID: 23519034]
[73]
Koscielny, G.; Yaikhom, G.; Iyer, V.; Meehan, T.F.; Morgan, H.; Atienza-Herrero, J.; Blake, A.; Chen, C-K.; Easty, R.; Di Fenza, A.; Fiegel, T.; Grifiths, M.; Horne, A.; Karp, N.A.; Kurbatova, N.; Mason, J.C.; Matthews, P.; Oakley, D.J.; Qazi, A.; Regnart, J.; Retha, A.; Santos, L.A.; Sneddon, D.J.; Warren, J.; Westerberg, H.; Wilson, R.J.; Melvin, D.G.; Smedley, D.; Brown, S.D.M.; Flicek, P.; Skarnes, W.C.; Mallon, A-M.; Parkinson, H. The international mouse phenotyping consortium web portal, a unified point of access for knockout mice and related phenotyping data. Nucleic Acids Res., 2014, 42(Database issue), D802-D809.
[http://dx.doi.org/10.1093/nar/gkt977] [PMID: 24194600]
[74]
Drysdale, R. FlyBase Consortium. FlyBase: a database for the Drosophila research community. Methods Mol. Biol., 2008, 420, 45-59.
[http://dx.doi.org/10.1007/978-1-59745-583-1_3] [PMID: 18641940]
[75]
Smith, C.L.; Blake, J.A.; Kadin, J.A.; Richardson, J.E.; Bult, C.J. Mouse Genome Database Group. Mouse Genome Database (MGD)-2018: knowledgebase for the laboratory mouse. Nucleic Acids Res., 2018, 46(D1), D836-D842.
[http://dx.doi.org/10.1093/nar/gkx1006] [PMID: 29092072]
[76]
Shimoyama, M.; De Pons, J.; Hayman, G.T.; Laulederkind, S.J.F.; Liu, W.; Nigam, R.; Petri, V.; Smith, J.R.; Tutaj, M.; Wang, S-J.; Worthey, E.; Dwinell, M.; Jacob, H. The Rat Genome Database 2015: genomic, phenotypic and environmental variations and disease. Nucleic Acids Res., 2015, 43(Database issue), D743-D750.
[http://dx.doi.org/10.1093/nar/gku1026] [PMID: 25355511]
[77]
Cherry, J.M.; Hong, E.L.; Amundsen, C.; Balakrishnan, R.; Binkley, G.; Chan, E.T.; Christie, K.R.; Costanzo, M.C.; Dwight, S.S.; Engel, S.R.; Fisk, D.G.; Hirschman, J.E.; Hitz, B.C.; Karra, K.; Krieger, C.J.; Miyasato, S.R.; Nash, R.S.; Park, J.; Skrzypek, M.S.; Simison, M.; Weng, S.; Wong, E.D. Saccharomyces genome database: the genomics resource of budding yeast. Nucleic Acids Res., 2012, 40(Database issue), D700-D705.
[http://dx.doi.org/10.1093/nar/gkr1029] [PMID: 22110037]
[78]
Harris, T.W.; Antoshechkin, I.; Bieri, T.; Blasiar, D.; Chan, J.; Chen, W.J.; De La Cruz, N.; Davis, P.; Duesbury, M.; Fang, R.; Fernandes, J.; Han, M.; Kishore, R.; Lee, R.; Mül-ler, H-M.; Nakamura, C.; Ozersky, P.; Petcherski, A.; Rangarajan, A.; Rogers, A.; Schindelman, G.; Schwarz, E.M.; Tuli, M.A.; Van Auken, K.; Wang, D.; Wang, X.; Wil-liams, G.; Yook, K.; Durbin, R.; Stein, L.D.; Spieth, J.; Sternberg, P.W. WormBase: A comprehensive resource for nematode research. Nucleic Acids Res., 2010, 38(Database issue), D463-D467.
[http://dx.doi.org/10.1093/nar/gkp952]
[79]
Ruzicka, L.; Bradford, Y. M.; Frazer, K.; Howe, D. G.; Pad-dock, H.; Ramachandran, S.; Singer, A.; Toro, S.; Van Slyke, C. E.; Eagle, A. E.; Fashena, D.; Kalita, P.; Knight, J.; Mani, P.; Martin, R.; Moxon, S. A. T.; Pich, C.; Schaper, K.; Shao, X.; Westerfield, M. ZFIN, the Zebrafish Model organism database: Updates and new directions. Genesis (New York, N.Y.: 2000), 2015, 53(8), 498-509.
[80]
Sprague, J.; Bayraktaroglu, L.; Bradford, Y.; Conlin, T.; Dunn, N.; Fashena, D.; Frazer, K.; Haendel, M.; Howe, D.G.; Knight, J.; Mani, P.; Moxon, S.A.; Pich, C.; Ramachan-dran, S.; Schaper, K.; Segerdell, E.; Shao, X.; Singer, A.; Song, P.; Sprunger, B.; Van Slyke, C.E.; Westerfield, M. The zebrafish information network: the zebrafish model organism database provides expanded support for genotypes and phenotypes. Nucleic Acids Res., 2008, 36(Database issue), D768-D772.
[81]
Hoehndorf, R.; Schofield, P.N.; Gkoutos, G.V. PhenomeNET: A whole-phenome approach to disease gene discovery. Nucleic Acids Res., 2011, 39(18), e119-e119.
[http://dx.doi.org/10.1093/nar/gkr538] [PMID: 21737429]
[82]
Sansone, S.A.; Rocca-Serra, P.; Field, D.; Maguire, E.; Taylor, C.; Hofmann, O.; Fang, H.; Neumann, S.; Tong, W.; Amaral-Zettler, L.; Begley, K.; Booth, T.; Bougueleret, L.; Burns, G.; Chapman, B.; Clark, T.; Coleman, L.A.; Copeland, J.; Das, S.; de Daruvar, A.; de Matos, P.; Dix, I.; Edmunds, S.; Evelo, C.T.; Forster, M.J.; Gaudet, P.; Gilbert, J.; Goble, C.; Griffin, J.L.; Jacob, D.; Kleinjans, J.; Harland, L.; Haug, K.; Hermjakob, H.; Ho Sui, S.J.; Laederach, A.; Liang, S.; Marshall, S.; McGrath, A.; Merrill, E.; Reilly, D.; Roux, M.; Shamu, C.E.; Shang, C.A.; Steinbeck, C.; Trefethen, A.; Williams-Jones, B.; Wolstencroft, K.; Xenarios, I.; Hide, W. Toward interoperable bioscience data. Nat. Genet., 2012, 44(2), 121-126.
[http://dx.doi.org/10.1038/ng.1054] [PMID: 22281772]
[83]
Vidović, D.; Koleti, A.; Schürer, S.C. Large-scale integration of small molecule-induced genome-wide transcriptional responses, Kinome-wide binding affinities and cell-growth inhibition profiles reveal global trends characterizing systems-level drug action. Front. Genet., 2014, 5, 342.
[PMID: 25324859]
[84]
Millard, B.L.; Niepel, M.; Menden, M.P.; Muhlich, J.L.; Sorger, P.K. Adaptive informatics for multifactorial and high-content biological data. Nat. Methods, 2011, 8(6), 487-493.
[http://dx.doi.org/10.1038/nmeth.1600] [PMID: 21516115]
[85]
Allan, C.; Burel, J.M.; Moore, J.; Blackburn, C.; Linkert, M.; Loynton, S.; Macdonald, D.; Moore, W.J.; Neves, C.; Patterson, A.; Porter, M.; Tarkowska, A.; Loranger, B.; Avondo, J.; Lagerstedt, I.; Lianas, L.; Leo, S.; Hands, K.; Hay, R.T.; Patwardhan, A.; Best, C.; Kleywegt, G.J.; Zanetti, G.; Swedlow, J.R. OMERO: flexible, model-driven data management for experimental biology. Nat. Methods, 2012, 9(3), 245-253.
[http://dx.doi.org/10.1038/nmeth.1896] [PMID: 22373911]
[86]
Clark, N.A.; Hafner, M.; Kouril, M.; Williams, E.H.; Muhlich, J.L.; Pilarczyk, M.; Niepel, M.; Sorger, P.K.; Medvedovic, M. GRcalculator: an online tool for calculating and mining dose-response data. BMC Cancer, 2017, 17(1), 698.
[http://dx.doi.org/10.1186/s12885-017-3689-3] [PMID: 29065900]
[87]
Hafner, M.; Niepel, M.; Chung, M.; Sorger, P.K. Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nat. Methods, 2016, 13(6), 521-527.
[http://dx.doi.org/10.1038/nmeth.3853] [PMID: 27135972]
[88]
Swinney, D.C.; Anthony, J. How were new medicines discovered? Nat. Rev. Drug Discov., 2011, 10(7), 507-519.
[http://dx.doi.org/10.1038/nrd3480] [PMID: 21701501]
[89]
Sioud, M. Main approaches to target discovery and validation. Methods Mol. Biol., 2007, 360, 1-12.
[PMID: 17172722]
[90]
Kopec, K.K.; Bozyczko-Coyne, D.; Williams, M. Target identification and validation in drug discovery: the role of proteomics. Biochem. Pharmacol., 2005, 69(8), 1133-1139.
[http://dx.doi.org/10.1016/j.bcp.2005.01.004] [PMID: 15794933]
[91]
Katsila, T.; Spyroulias, G.A.; Patrinos, G.P.; Matsoukas, M-T. Computational approaches in target identification and drug discovery. Comput. Struct. Biotechnol. J., 2016, 14, 177-184.
[http://dx.doi.org/10.1016/j.csbj.2016.04.004] [PMID: 27293534]
[92]
Campbell, S.J.; Gaulton, A.; Marshall, J.; Bichko, D.; Martin, S.; Brouwer, C.; Harland, L. Visualizing the drug target landscape. Drug Discov. Today, 2010, 15(1-2), 3-15.
[http://dx.doi.org/10.1016/j.drudis.2009.09.011] [PMID: 19840866]
[93]
Cheng, T.; Li, Q.; Wang, Y.; Bryant, S.H. Identifying compound-target associations by combining bioactivity profile similarity search and public databases mining. J. Chem. Inf. Model., 2011, 51(9), 2440-2448.
[http://dx.doi.org/10.1021/ci200192v] [PMID: 21834535]
[94]
Jin, G.; Wong, S.T.C. Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. Drug Discov. Today, 2014, 19(5), 637-644.
[http://dx.doi.org/10.1016/j.drudis.2013.11.005] [PMID: 24239728]
[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]
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]
[97]
Wang, Y.; Xiao, J.; Suzek, T. O.; Zhang, J.; Wang, J.; Bryant, S. H. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res., 2009, 37(Web Server issue), W623-W633.
[http://dx.doi.org/10.1093/nar/gkp456]
[98]
Pan, Y.; Cheng, T.; Wang, Y.; Bryant, S.H. Pathway analysis for drug repositioning based on public database mining. J. Chem. Inf. Model., 2014, 54(2), 407-418.
[http://dx.doi.org/10.1021/ci4005354] [PMID: 24460210]
[99]
Wang, Y.; Bolton, E.; Dracheva, S.; Karapetyan, K.; Shoemaker, B.A.; Suzek, T.O.; Wang, J.; Xiao, J.; Zhang, J.; Bryant, S.H. An overview of the PubChem BioAssay resource. Nucleic Acids Res., 2010, 38(Database issue), D255-D266.
[http://dx.doi.org/10.1093/nar/gkp965] [PMID: 19933261]
[100]
Frye, S.V. Structure-activity relationship homology (SARAH): A conceptual framework for drug discovery in the genomic era. Chem. Biol., 1999, 6(1), R3-R7.
[http://dx.doi.org/10.1016/S1074-5521(99)80013-1] [PMID: 9889153]
[101]
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]
[102]
James, C.; Weininger, D.; Delany, J. Daylight Theory Manual Daylight Chemical Information Systems Inc., Mission Viejo, CA., 1992, 2005.
[103]
Altschul, S.F.; Gish, W.; Miller, W.; Myers, E.W.; Lipman, D.J. Basic local alignment search tool. J. Mol. Biol., 1990, 215(3), 403-410.
[http://dx.doi.org/10.1016/S0022-2836(05)80360-2] [PMID: 2231712]
[104]
Bellera, C.L.; Balcazar, D.E.; Vanrell, M.C.; Casassa, A.F.; Palestro, P.H.; Gavernet, L.; Labriola, C.A.; Gálvez, J.; Bruno-Blanch, L.E.; Romano, P.S.; Carrillo, C.; Talevi, A. Computer-guided drug repurposing: identification of trypanocidal activity of clofazimine, benidipine and saquinavir. Eur. J. Med. Chem., 2015, 93, 338-348.
[http://dx.doi.org/10.1016/j.ejmech.2015.01.065] [PMID: 25707014]
[105]
DESMOL11 software, Molecular Topology & Drug Design Unit. University of Valencia, Spain.. ,
[106]
Dakshanamurthy, S.; Issa, N.T.; Assefnia, S.; Seshasayee, A.; Peters, O.J.; Madhavan, S.; Uren, A.; Brown, M.L.; Byers, S.W. Predicting new indications for approved drugs using a proteochemometric method. J. Med. Chem., 2012, 55(15), 6832-6848.
[http://dx.doi.org/10.1021/jm300576q] [PMID: 22780961]
[107]
Release, S. 2017-4: QikProp; S., LLC: New York, NY, 2017.
[108]
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]
[109]
Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A.; Wang, J.; Yu, B.; Zhang, J.; Bryant, S.H. PubChem substance and compound databases. Nucleic Acids Res., 2016, 44(D1), D1202-D1213.
[http://dx.doi.org/10.1093/nar/gkv951] [PMID: 26400175]
[110]
Assefnia, S.; Dakshanamurthy, S.; Guidry Auvil, J.M.; Hampel, C.; Anastasiadis, P.Z.; Kallakury, B.; Uren, A.; Foley, D.W.; Brown, M.L.; Shapiro, L.; Brenner, M.; Haigh, D.; Byers, S.W. Cadherin-11 in poor prognosis malignancies and rheumatoid arthritis: common target, common therapies. Oncotarget, 2014, 5(6), 1458-1474.
[http://dx.doi.org/10.18632/oncotarget.1538] [PMID: 24681547]
[111]
Fu, C.; Jin, G.; Gao, J.; Zhu, R.; Ballesteros-Villagrana, E.; Wong, S.T. DrugMap Central: An on-line query and visualization tool to facilitate drug repositioning studies. Bioinformatics, 2013, 29(14), 1834-1836.
[http://dx.doi.org/10.1093/bioinformatics/btt279] [PMID: 23681121]
[112]
Chen, J.; Swamidass, S.J.; Dou, Y.; Bruand, J.; Baldi, P. ChemDB: a public database of small molecules and related chemoinformatics resources. Bioinformatics (Oxford, England), 2005, 21(22), 4133-4139.
[http://dx.doi.org/10.1093/bioinformatics/bti683]
[113]
Pence, H.E.; Williams, A. ChemSpider: An online chemical information resource. J. Chem. Educ., 2010, 87(11), 1123-1124.
[http://dx.doi.org/10.1021/ed100697w]
[114]
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(Database issue), D919-D922.
[PMID: 17942422]
[115]
Hecker, N.; Ahmed, J.; von Eichborn, J.; Dunkel, M.; Ma-cha, K.; Eckert, A.; Gilson, M.K.; Bourne, P.E.; Preissner, R. SuperTarget goes quantitative: update on drug–target in-teractions. Nucleic Acids Res., 2012, 40(Database issue), D1113-D1117.
[http://dx.doi.org/10.1093/nar/gkr912]
[116]
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]
[117]
Lomize, M.A.; Pogozheva, I.D.; Joo, H.; Mosberg, H.I.; Lomize, A.L. OPM database and PPM web server: Resources for positioning of proteins in membranes. Nucleic Acids Res., 2012, 40(Database issue), D370-D376.
[http://dx.doi.org/10.1093/nar/gkr703] [PMID: 21890895]
[118]
Ellrott, K.; Zmasek, C.M.; Weekes, D.; Sri Krishna, S.; Bakolitsa, C.; Godzik, A.; Wooley, J. TOPSAN: A dynamic web database for structural genomics. Nucleic Acids Res., 2011, 39(Database issue), D494-D496.
[http://dx.doi.org/10.1093/nar/gkq902] [PMID: 20961957]
[119]
Taboureau, O.; Baell, J.B.; Fernández-Recio, J.; Villoutreix, B.O. Established and emerging trends in computational drug discovery in the structural genomics era. Chem. Biol., 2012, 19(1), 29-41.
[http://dx.doi.org/10.1016/j.chembiol.2011.12.007] [PMID: 22284352]
[120]
Rognan, D. The impact of in silico screening in the discovery of novel and safer drug candidates. Pharmacol. Ther., 2017, 175, 47-66.
[http://dx.doi.org/10.1016/j.pharmthera.2017.02.034] [PMID: 28223231]
[121]
Vuorinen, A.; Odermatt, A.; Schuster, D. In silico methods in the discovery of endocrine disrupting chemicals. J. Steroid Biochem. Mol. Biol., 2013, 137, 18-26.
[http://dx.doi.org/10.1016/j.jsbmb.2013.04.009] [PMID: 23688835]
[122]
Amaro, R.E.; Li, W.W. Emerging methods for ensemble-based virtual screening. Curr. Top. Med. Chem., 2010, 10(1), 3-13.
[http://dx.doi.org/10.2174/156802610790232279] [PMID: 19929833]
[123]
Tan, L.; Batista, J.; Bajorath, J. Computational methodologies for compound database searching that utilize experimental protein-ligand interaction information. Chem. Biol. Drug Des., 2010, 76(3), 191-200.
[http://dx.doi.org/10.1111/j.1747-0285.2010.01007.x] [PMID: 20636330]
[124]
Bajorath, J. Integration of virtual and high-throughput screening. Nat. Rev. Drug Discov., 2002, 1(11), 882-894.
[http://dx.doi.org/10.1038/nrd941] [PMID: 12415248]
[125]
Muegge, I.; Mukherjee, P. An overview of molecular fingerprint similarity search in virtual screening. Expert Opin. Drug Discov., 2016, 11(2), 137-148.
[http://dx.doi.org/10.1517/17460441.2016.1117070] [PMID: 26558489]
[126]
Franco, P.; Porta, N.; Holliday, J.D.; Willett, P. Molecular similarity considerations in the licensing of orphan drugs. Drug Discov. Today, 2017, 22(2), 377-381.
[http://dx.doi.org/10.1016/j.drudis.2016.11.024] [PMID: 27965161]
[127]
Hert, J.; Willett, P.; Wilton, D.J.; Acklin, P.; Azzaoui, K.; Jacoby, E.; Schuffenhauer, A. Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures. J. Chem. Inf. Comput. Sci., 2004, 44(3), 1177-1185.
[http://dx.doi.org/10.1021/ci034231b] [PMID: 15154787]
[128]
Wang, Y.; Bajorath, J. Advanced fingerprint methods for similarity searching: Balancing molecular complexity effects. Comb. Chem. High Throughput Screen., 2010, 13(3), 220-228.
[http://dx.doi.org/10.2174/138620710790980487] [PMID: 20230370]
[129]
Swamidass, S.J. Mining small-molecule screens to repurpose drugs. Brief. Bioinform., 2011, 12(4), 327-335.
[http://dx.doi.org/10.1093/bib/bbr028] [PMID: 21715466]
[130]
Westermaier, Y.; Barril, X.; Scapozza, L. Virtual screening: An in silico tool for interlacing the chemical universe with the proteome. Methods, 2015, 71, 44-57.
[http://dx.doi.org/10.1016/j.ymeth.2014.08.001] [PMID: 25193260]
[131]
Cheng, T.; Li, Q.; Zhou, Z.; Wang, Y.; Bryant, S.H. Structure-based virtual screening for drug discovery: A problem-centric review. AAPS J., 2012, 14(1), 133-141.
[http://dx.doi.org/10.1208/s12248-012-9322-0] [PMID: 22281989]
[132]
Reddy, A.S.; Pati, S.P.; Kumar, P.P.; Pradeep, H.N.; Sastry, G.N. Virtual screening in drug discovery -- a computational perspective. Curr. Protein Pept. Sci., 2007, 8(4), 329-351.
[http://dx.doi.org/10.2174/138920307781369427] [PMID: 17696867]
[133]
Ferreira, L.G.; Dos Santos, R.N.; Oliva, G.; Andricopulo, A.D. Molecular docking and structure-based drug design strategies. Molecules, 2015, 20(7), 13384-13421.
[http://dx.doi.org/10.3390/molecules200713384] [PMID: 26205061]
[134]
Lionta, E.; Spyrou, G.; Vassilatis, D.K.; Cournia, Z. Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr. Top. Med. Chem., 2014, 14(16), 1923-1938.
[http://dx.doi.org/10.2174/1568026614666140929124445] [PMID: 25262799]
[135]
Chen, Y.C. Beware of docking! Trends Pharmacol. Sci., 2015, 36(2), 78-95.
[http://dx.doi.org/10.1016/j.tips.2014.12.001] [PMID: 25543280]
[136]
Guney, E.; Menche, J.; Vidal, M.; Barábasi, A.L. Network-based in silico drug efficacy screening. Nat. Commun., 2016, 7, 10331.
[http://dx.doi.org/10.1038/ncomms10331] [PMID: 26831545]
[137]
Chen, H.; Zhang, H.; Zhang, Z.; Cao, Y.; Tang, W. Network-based inference methods for drug repositioning. Comput. Math. Methods Med., 2015.2015130620
[http://dx.doi.org/10.1155/2015/130620] [PMID: 25969690]
[138]
Löwer, M.; Geppert, T.; Schneider, P.; Hoy, B.; Wessler, S.; Schneider, G. Inhibitors of helicobacter pylori protease HtrA found by ‘virtual ligand’ screening combat bacterial invasion of epithelia. PLoS One, 2011, 6(3)e17986
[http://dx.doi.org/10.1371/journal.pone.0017986] [PMID: 21483848]
[139]
Musyoka, T.M.; Kanzi, A.M.; Lobb, K.A.; Tastan Bishop, Ö. Structure based docking and molecular dynamic studies of plasmodial cysteine proteases against a south african natural compound and its analogs. Sci. Rep., 2016, 6, 23690.
[http://dx.doi.org/10.1038/srep23690] [PMID: 27030511]
[140]
Sastry, G.M.; Adzhigirey, M.; Day, T.; Annabhimoju, R.; Sherman, W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des., 2013, 27(3), 221-234.
[http://dx.doi.org/10.1007/s10822-013-9644-8] [PMID: 23579614]
[141]
Zhang, J.H.; Chung, T.D.; Oldenburg, K.R. Confirmation of primary active substances from high throughput screening of chemical and biological populations: a statistical approach and practical considerations. J. Comb. Chem., 2000, 2(3), 258-265.
[http://dx.doi.org/10.1021/cc9900706] [PMID: 10827934]
[142]
Day-Richter, J.; Harris, M.A.; Haendel, M.; Lewis, S. Gene ontology OBO-edit working group. OBO-Edit--an ontology editor for biologists. Bioinformatics, 2007, 23(16), 2198-2200.
[http://dx.doi.org/10.1093/bioinformatics/btm112] [PMID: 17545183]
[143]
Carbon, S.; Ireland, A.; Mungall, C.J.; Shu, S.; Marshall, B.; Lewis, S.; Ami, G.O. AmiGO: online access to ontology and annotation data. Bioinformatics, 2009, 25(2), 288-289.
[http://dx.doi.org/10.1093/bioinformatics/btn615] [PMID: 19033274]
[144]
Martin, L.; Hutchens, M.; Hawkins, C.; Radnov, A. How much do clinical trials cost? Nat. Rev. Drug Discov., 2017, 16(6), 381-382.
[http://dx.doi.org/10.1038/nrd.2017.70] [PMID: 28529317]
[145]
Mahajan, R.; Gupta, K. Adaptive design clinical trials: Methodology, challenges and prospect. Indian J. Pharmacol., 2010, 42(4), 201-207.
[http://dx.doi.org/10.4103/0253-7613.68417] [PMID: 20927243]
[146]
Ashburn, T.T.; Thor, K.B. Drug repositioning: Identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov., 2004, 3(8), 673-683.
[http://dx.doi.org/10.1038/nrd1468] [PMID: 15286734]
[147]
Cavalla, D. Predictive methods in drug repurposing: gold mine or just a bigger haystack? Drug Discov. Today, 2013, 18(11-12), 523-532.
[http://dx.doi.org/10.1016/j.drudis.2012.12.009] [PMID: 23270784]
[148]
Sun, P.; Guo, J.; Winnenburg, R.; Baumbach, J. Drug repurposing by integrated literature mining and drug-gene-disease triangulation. Drug Discov. Today, 2017, 22(4), 615-619.
[http://dx.doi.org/10.1016/j.drudis.2016.10.008] [PMID: 27780789]
[149]
Tetko, I.V.; Engkvist, O.; Koch, U.; Reymond, J.L.; Chen, H. BIGCHEM: Challenges and opportunities for big data analysis in chemistry. Mol. Inform., 2016, 35(11-12), 615-621.
[http://dx.doi.org/10.1002/minf.201600073] [PMID: 27464907]


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VOLUME: 26
ISSUE: 28
Year: 2019
Page: [5389 - 5409]
Pages: 21
DOI: 10.2174/0929867325666180530100332
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