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Current Medicinal Chemistry

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ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

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Review Article

Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches

Author(s): Jelica Vucicevic, Katarina Nikolic* and John B.O. Mitchell*

Volume 26, Issue 21, 2019

Page: [3874 - 3889] Pages: 16

DOI: 10.2174/0929867324666170712115411

Price: $65

Abstract

Background: Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation.

Results: Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile.

Conclusion: In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.

Keywords: Antineoplastic agents, pharmacophore, QSAR, rational drug design, cheminformatics, virtual screening, virtual docking.

« Previous Next »
[1]
Jemal, A.; Bray, F.; Center, M.M.; Ferlay, J.; Ward, E.; Forman, D. Global cancer statistics. CA Cancer J. Clin., 2011, 61(2), 69-90.
[http://dx.doi.org/10.3322/caac.20107] [PMID: 21296855]
[2]
Stearns, V.; Davidson, N.E.; Flockhart, D.A. Pharmacogenetics in the treatment of breast cancer. Pharmacogenomics J., 2004, 4(3), 143-153.
[http://dx.doi.org/10.1038/sj.tpj.6500242] [PMID: 15024382]
[3]
Wilson, G.L.; Lill, M.A. Integrating structure-based and ligand-based approaches for computational drug design. Future Med. Chem., 2011, 3(6), 735-750.
[http://dx.doi.org/10.4155/fmc.11.18] [PMID: 21554079]
[4]
Alvarez, J.C. High-throughput docking as a source of novel drug leads. Curr. Opin. Chem. Biol., 2004, 8(4), 365-370.
[http://dx.doi.org/10.1016/j.cbpa.2004.05.001] [PMID: 15288245]
[5]
Roche, J.; Bertrand, P. Inside HDACs with more selective HDAC inhibitors. Eur. J. Med. Chem., 2016, 121, 451-483.
[http://dx.doi.org/10.1016/j.ejmech.2016.05.047] [PMID: 27318122]
[6]
Ganesan, A. Multitarget drugs: An epigenetic epiphany. ChemMedChem, 2016, 11(12), 1227-1241.
[http://dx.doi.org/10.1002/cmdc.201500394] [PMID: 26891251]
[7]
Choubey, S.K.; Jeyaraman, J. A mechanistic approach to explore novel HDAC1 inhibitor using pharmacophore modeling, 3D- QSAR analysis, molecular docking, density functional and molecular dynamics simulation study. J. Mol. Graph. Model., 2016, 70, 54-69.
[http://dx.doi.org/10.1016/j.jmgm.2016.09.008] [PMID: 27668885]
[8]
Kufareva, I.; Abagyan, R. Type-II kinase inhibitor docking, screening, and profiling using modified structures of active kinase states. J. Med. Chem., 2008, 51(24), 7921-7932.
[http://dx.doi.org/10.1021/jm8010299] [PMID: 19053777]
[9]
Ripphausen, P.; Nisius, B.; Peltason, L.; Bajorath, J. Quo vadis, virtual screening? A comprehensive survey of prospective applications. J. Med. Chem., 2010, 53(24), 8461-8467.
[http://dx.doi.org/10.1021/jm101020z] [PMID: 20929257]
[10]
Hu, B.; Lill, M.A. PharmDock: A pharmacophore-based docking program. J. Cheminform., 2014, 6, 14.
[http://dx.doi.org/10.1186/1758-2946-6-14] [PMID: 24739488]
[11]
Wong, Y.H.; Lin, C.L.; Chen, T.S.; Chen, C.A.; Jiang, P.S.; Lai, Y.H.; Chu, L.; Li, C.W.; Chen, J.J.; Chen, B.S. Multiple target drug cocktail design for attacking the core network markers of four cancers using ligand-based and structure-based virtual screening methods. BMC Med. Genomics, 2015, 8(Suppl. 4), S4.
[http://dx.doi.org/10.1186/1755-8794-8-S4-S4] [PMID: 26680552]
[12]
Cherkasov, A.; Muratov, E.N.; Fourches, D.; Varnek, A.; Baskin, I.I.; Cronin, M.; Dearden, J.; Gramatica, P.; Martin, Y.C.; Todeschini, R.; Consonni, V.; Kuz’min, V.E.; Cramer, R.; Benigni, R.; Yang, C.; Rathman, J.; Terfloth, L.; Gasteiger, J.; Richard, A.; Tropsha, A. QSAR modeling: Where have you been? Where are you going to? J. Med. Chem., 2014, 57(12), 4977-5010.
[http://dx.doi.org/10.1021/jm4004285] [PMID: 24351051]
[13]
Cramer, R.D. The inevitable QSAR renaissance. J. Comput. Aided Mol. Des., 2012, 26(1), 35-38.
[http://dx.doi.org/10.1007/s10822-011-9495-0] [PMID: 22127732]
[14]
Vucicevic, J.; Srdic-Rajic, T.; Pieroni, M.; Laurila, J.M.; Perovic, V.; Tassini, S.; Azzali, E.; Costantino, G.; Glisic, S.; Agbaba, D.; Scheinin, M.; Nikolic, K.; Radi, M.; Veljkovic, N. A combined ligand- and structure-based approach for the identification of rilmenidine-derived compounds which synergize the antitumor effects of doxorubicin. Bioorg. Med. Chem., 2016, 24(14), 3174-3183.
[http://dx.doi.org/10.1016/j.bmc.2016.05.043] [PMID: 27265687]
[15]
Gagic, Z.; Nikolic, K.; Ivkovic, B.; Filipic, S.; Agbaba, D. QSAR studies and design of new analogs of vitamin E with enhanced antiproliferative activity on MCF-7 breast cancer cells. J. Taiwan Inst. Chem. Eng., 2016, 59, 33-44.
[http://dx.doi.org/10.1016/j.jtice.2015.07.019]
[16]
Ivković, B.M.; Nikolic, K.; Ilić, B.B.; Žižak, Z.S.; Novaković, R.B.; Čudina, O.A.; Vladimirov, S.M. Phenylpropiophenone derivatives as potential anticancer agents: Synthesis, biological evaluation and quantitative structure-activity relationship study. Eur. J. Med. Chem., 2013, 63, 239-255.
[http://dx.doi.org/10.1016/j.ejmech.2013.02.013] [PMID: 23501110]
[17]
Shahlaei, M. Descriptor selection methods in quantitative structure-activity relationship studies: A review study. Chem. Rev., 2013, 113(10), 8093-8103.
[http://dx.doi.org/10.1021/cr3004339] [PMID: 23822589]
[18]
Harary, F. Recent results in topological graph theory. Acta Mathem Academ Scient Hung., 1964, 15, 405-412.
[http://dx.doi.org/10.1007/BF01897149]
[19]
Wiener, H. Structural determination of paraffin boiling points. J. Am. Chem. Soc., 1947, 69(1), 17-20.
[http://dx.doi.org/10.1021/ja01193a005] [PMID: 20291038]
[20]
Randic, M. On characterization of molecular branching. J. Am. Chem. Soc., 1975, 97, 6609-6614.
[http://dx.doi.org/10.1021/ja00856a001]
[21]
Kier, L.B. Indexes of molecular shape from chemical graphs. Acta Pharm. Jugosl., 1986, 36, 171-188.
[22]
Balaban, A.T. Highly discriminating distance-based topological index. Chem. Phys. Lett., 1982, 89, 399-404.
[http://dx.doi.org/10.1016/0009-2614(82)80009-2]
[23]
Kier, L.B.; Hall, L.H. Molecular Connectivity in structure activity analysis; Wiley&Sons: New York, 1986.
[24]
Todeschini, R.; Consonni, V. Handbook of molecular descriptors; Wlley-VCH:: New York,, 2000.
[http://dx.doi.org/10.1002/9783527613106]
[25]
Wold, S.; Ruhe, A.; Wold, H.; Dunn, W.J. The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses. SIAM J. Sci. Statist. Comput., 1984, 5, 735-743.
[http://dx.doi.org/10.1137/0905052]
[26]
Taha, M.O.; Bustanji, Y.; Al-Ghussein, M.A.; Mohammad, M.; Zalloum, H.; Al-Masri, I.M.; Atallah, N. Pharmacophore modeling, quantitative structure-activity relationship analysis, and in silico screening reveal potent glycogen synthase kinase-3beta inhibitory activities for cimetidine, hydroxychloroquine, and gemifloxacin. J. Med. Chem., 2008, 51(7), 2062-2077.
[http://dx.doi.org/10.1021/jm7009765] [PMID: 18324764]
[27]
Al-Nadaf, A.; Abu Sheikha, G.; Taha, M.O. Elaborate ligand-based pharmacophore exploration and QSAR analysis guide the synthesis of novel pyridinium-based potent beta-secretase inhibitory leads. Bioorg. Med. Chem., 2010, 18(9), 3088-3115.
[http://dx.doi.org/10.1016/j.bmc.2010.03.043] [PMID: 20378363]
[28]
Al-Sha’er, M.A.; Taha, M.O. Discovery of novel CDK1 inhibitors by combining pharmacophore modeling, QSAR analysis and in silico screening followed by in vitro bioassay. Eur. J. Med. Chem., 2010, 45(9), 4316-4330.
[http://dx.doi.org/10.1016/j.ejmech.2010.06.034] [PMID: 20638755]
[29]
Abdula, A.M.; Khalaf, R.A.; Mubarak, M.S.; Taha, M.O. Discovery of new β-D-galactosidase inhibitors via pharmacophore modeling and QSAR analysis followed by in silico screening. J. Comput. Chem., 2011, 32(3), 463-482.
[http://dx.doi.org/10.1002/jcc.21635] [PMID: 20730780]
[30]
Habash, M.; Taha, M.O. Ligand-based modelling followed by synthetic exploration unveil novel glycogen phosphorylase inhibitory leads. Bioorg. Med. Chem., 2011, 19(16), 4746-4771.
[http://dx.doi.org/10.1016/j.bmc.2011.06.086] [PMID: 21788139]
[31]
Shahin, R.; Alqtaishat, S.; Taha, M.O. Elaborate ligand-based modeling reveal new submicromolar Rho kinase inhibitors. J. Comput. Aided Mol. Des., 2012, 26(2), 249-266.
[http://dx.doi.org/10.1007/s10822-011-9509-y] [PMID: 22167443]
[32]
Suaifan, G.A.; Shehadehh, M.; Al-Ijel, H.; Taha, M.O. Extensive ligand-based modeling and in silico screening reveal nanomolar inducible nitric oxide synthase (iNOS) inhibitors. J. Mol. Graph. Model., 2012, 37, 1-26.
[http://dx.doi.org/10.1016/j.jmgm.2012.04.001] [PMID: 22609742]
[33]
Mitchell, J.B.O. Machine learning methods in chemoinformatics. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2014, 4(5), 468-481.
[http://dx.doi.org/10.1002/wcms.1183] [PMID: 25285160]
[34]
Singh, H.; Singh, S.; Singla, D.; Agarwal, S.M.; Raghava, G.P.S. QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest. Biol. Direct, 2015, 10, 10.
[http://dx.doi.org/10.1186/s13062-015-0046-9] [PMID: 25880749]
[35]
Riddick, G.; Song, H.; Ahn, S.; Walling, J.; Borges-Rivera, D.; Zhang, W.; Fine, H.A. Predicting in vitro drug sensitivity using Random Forests. Bioinformatics, 2011, 27(2), 220-224.
[http://dx.doi.org/10.1093/bioinformatics/btq628] [PMID: 21134890]
[36]
Statnikov, A.; Wang, L.; Aliferis, C.F. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics, 2008, 9, 319.
[http://dx.doi.org/10.1186/1471-2105-9-319] [PMID: 18647401]
[37]
Carlsson, L.; Helgee, E.A.; Boyer, S. Interpretation of nonlinear QSAR models applied to Ames mutagenicity data. J. Chem. Inf. Model., 2009, 49(11), 2551-2558.
[http://dx.doi.org/10.1021/ci9002206] [PMID: 19824682]
[38]
Cramer, R.D.; Patterson, D.E.; Bunce, J.D. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc., 1988, 110(18), 5959-5967.
[http://dx.doi.org/10.1021/ja00226a005] [PMID: 22148765]
[39]
Klebe, G.; Abraham, U.; Mietzner, T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem., 1994, 37(24), 4130-4146.
[http://dx.doi.org/10.1021/jm00050a010] [PMID: 7990113]
[40]
Acharya, C.; Coop, A.; Polli, J.E.; Mackerell, A.D. Jr Recent advances in ligand-based drug design: Relevance and utility of the conformationally sampled pharmacophore approach. Curr. Comput. Aided Drug Des., 2011, 7(1), 10-22.
[http://dx.doi.org/10.2174/157340911793743547] [PMID: 20807187]
[41]
Goodford, P.J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem., 1985, 28(7), 849-857.
[http://dx.doi.org/10.1021/jm00145a002] [PMID: 3892003]
[42]
Dixon, S.L.; Smondyrev, A.M.; Rao, S.N. PHASE: A novel approach to pharmacophore modeling and 3D database searching. Chem. Biol. Drug Des., 2006, 67(5), 370-372.
[http://dx.doi.org/10.1111/j.1747-0285.2006.00384.x] [PMID: 16784462]
[43]
Pastor, M.; Cruciani, G.; McLay, I.; Pickett, S.; Clementi, S. GRid-INdependent descriptors (GRIND): A novel class of alignment-independent three-dimensional molecular descriptors. J. Med. Chem., 2000, 43(17), 3233-3243.
[http://dx.doi.org/10.1021/jm000941m] [PMID: 10966742]
[44]
Durán, A.; Zamora, I.; Pastor, M. Suitability of GRIND-based principal properties for the description of molecular similarity and ligand-based virtual screening. J. Chem. Inf. Model., 2009, 49(9), 2129-2138.
[http://dx.doi.org/10.1021/ci900228x] [PMID: 19728739]
[45]
Ballante, F.; Ragno, R. 3-D QSAutogrid/R: An alternative procedure to build 3-D QSAR models. Methodologies and applications. J. Chem. Inf. Model., 2012, 52(6), 1674-1685.
[http://dx.doi.org/10.1021/ci300123x] [PMID: 22643034]
[46]
Ortiz, A.R.; Pisabarro, M.T.; Gago, F.; Wade, R.C. Prediction of drug binding affinities by comparative binding energy analysis. J. Med. Chem., 1995, 38(14), 2681-2691.
[http://dx.doi.org/10.1021/jm00014a020] [PMID: 7629807]
[47]
Gohlke, H.; Klebe, G. DrugScore meets CoMFA: Adaptation of fields for molecular comparison (AFMoC) or how to tailor knowledge-based pair-potentials to a particular protein. J. Med. Chem., 2002, 45(19), 4153-4170.
[http://dx.doi.org/10.1021/jm020808p] [PMID: 12213058]
[48]
Varela, R.; Walters, W.P.; Goldman, B.B.; Jain, A.N. Iterative refinement of a binding pocket model: Active computational steering of lead optimization. J. Med. Chem., 2012, 55(20), 8926-8942.
[http://dx.doi.org/10.1021/jm301210j] [PMID: 23046104]
[49]
Silvestri, L.; Ballante, F.; Mai, A.; Marshall, G.R.; Ragno, R. Histone deacetylase inhibitors: Structure-based modeling and isoform-selectivity prediction. J. Chem. Inf. Model., 2012, 52(8), 2215-2235.
[http://dx.doi.org/10.1021/ci300160y] [PMID: 22762501]
[50]
Ragno, R.; Simeoni, S.; Castellano, S.; Vicidomini, C.; Mai, A.; Caroli, A.; Tramontano, A.; Bonaccini, C.; Trojer, P.; Bauer, I.; Brosch, G.; Sbardella, G. Small molecule inhibitors of histone arginine methyltransferases: Homology modeling, molecular docking, binding mode analysis, and biological evaluations. J. Med. Chem., 2007, 50(6), 1241-1253.
[http://dx.doi.org/10.1021/jm061213n] [PMID: 17323938]
[51]
Ballante, F.; Caroli, A.; Wickersham, R.B., III; Ragno, R. Hsp90 inhibitors, part 1: Definition of 3-D QSAutogrid/R models as a tool for virtual screening. J. Chem. Inf. Model., 2014, 54(3), 956-969.
[http://dx.doi.org/10.1021/ci400759t] [PMID: 24564321]
[52]
Caroli, A.; Ballante, F.; Wickersham, R.B., III; Corelli, F.; Ragno, R. Hsp90 inhibitors, part 2: Combining ligand-based and structure-based approaches for virtual screening application. J. Chem. Inf. Model., 2014, 54(3), 970-977.
[http://dx.doi.org/10.1021/ci400760a] [PMID: 24555544]
[53]
Zentner, G.E.; Henikoff, S. Regulation of nucleosome dynamics by histone modifications. Nat. Struct. Mol. Biol., 2013, 20(3), 259-266.
[http://dx.doi.org/10.1038/nsmb.2470] [PMID: 23463310]
[54]
Heyn, H.; Esteller, M. DNA methylation profiling in the clinic: Applications and challenges. Nat. Rev. Genet., 2012, 13(10), 679-692.
[http://dx.doi.org/10.1038/nrg3270] [PMID: 22945394]
[55]
Meyer, K.D.; Saletore, Y.; Zumbo, P.; Elemento, O.; Mason, C.E.; Jaffrey, S.R. Comprehensive analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell, 2012, 149(7), 1635-1646.
[http://dx.doi.org/10.1016/j.cell.2012.05.003] [PMID: 22608085]
[56]
Mercer, T.R.; Mattick, J.S. Structure and function of long noncoding RNAs in epigenetic regulation. Nat. Struct. Mol. Biol., 2013, 20(3), 300-307.
[http://dx.doi.org/10.1038/nsmb.2480] [PMID: 23463315]
[57]
Fahy, J.; Jeltsch, A.; Arimondo, P.B. DNA methyltransferase inhibitors in cancer: A chemical and therapeutic patent overview and selected clinical studies. Expert Opin. Ther. Pat., 2012, 22, 1427-1442.
[http://dx.doi.org/10.1517/13543776.2012.729579]
[58]
Huang, H.; Lin, S.; Garcia, B.A.; Zhao, Y. Quantitative proteomic analysis of histone modifications. Chem. Rev., 2015, 115(6), 2376-2418.
[http://dx.doi.org/10.1021/cr500491u] [PMID: 25688442]
[59]
Filippakopoulos, P.; Knapp, S. Targeting bromodomains: Epigenetic readers of lysine acetylation. Nat. Rev. Drug Discov., 2014, 13(5), 337-356.
[http://dx.doi.org/10.1038/nrd4286] [PMID: 24751816]
[60]
Zheng, Y.C.; Ma, J.; Wang, Z.; Li, J.; Jiang, B.; Zhou, W.; Shi, X.; Wang, X.; Zhao, W.; Liu, H.M. systematic review of histone lysine-specific demethylase 1 and its inhibitors. Med. Res. Rev., 2015, 35(5), 1032-1071.
[http://dx.doi.org/10.1002/med.21350] [PMID: 25990136]
[61]
Kaniskan, H.Ü.; Konze, K.D.; Jin, J. Selective inhibitors of protein methyltransferases. J. Med. Chem., 2015, 58(4), 1596-1629.
[http://dx.doi.org/10.1021/jm501234a] [PMID: 25406853]
[62]
Marmorstein, R. Structure of histone deacetylases: Insights into substrate recognition and catalysis. Structure, 2001, 9(12), 1127-1133.
[http://dx.doi.org/10.1016/S0969-2126(01)00690-6] [PMID: 11738039]
[63]
Khan, N.; Jeffers, M.; Kumar, S.; Hackett, C.; Boldog, F.; Khramtsov, N.; Qian, X.; Mills, E.; Berghs, S.C.; Carey, N.; Finn, P.W.; Collins, L.S.; Tumber, A.; Ritchie, J.W.; Jensen, P.B.; Lichenstein, H.S.; Sehested, M. Determination of the class and isoform selectivity of small-molecule histone deacetylase inhibitors. Biochem. J., 2008, 409(2), 581-589.
[http://dx.doi.org/10.1042/BJ20070779] [PMID: 17868033]
[64]
Micelli, C.; Rastelli, G. Histone deacetylases: Structural determinants of inhibitor selectivity. Drug Discov. Today, 2015, 20(6), 718-735.
[http://dx.doi.org/10.1016/j.drudis.2015.01.007] [PMID: 25687212]
[65]
Hai, Y.; Christianson, D.W. Histone deacetylase 6 structure and molecular basis of catalysis and inhibition. Nat. Chem. Biol., 2016, 12(9), 741-747.
[http://dx.doi.org/10.1038/nchembio.2134] [PMID: 27454933]
[66]
Xie, A.; Liao, C.; Li, Z.; Ning, Z.; Hu, W.; Lu, X.; Shi, L.; Zhou, J. Quantitative structure-activity relationship study of histone deacetylase inhibitors. Curr. Med. Chem. Anticancer Agents, 2004, 4(3), 273-299.
[http://dx.doi.org/10.2174/1568011043352948] [PMID: 15134505]
[67]
Dessalew, N. QSAR study on aminophenylbenzamides and acrylamides as histone deacetylase inhibitors: An insight into the structural basis of antiproliferative activity. Med. Chem. Res., 2007, 16, 449-460.
[http://dx.doi.org/10.1007/s00044-007-9085-9]
[68]
Chen, H-F.; Kang, J-H.; Li, Q.; Zeng, B-S.; Yao, X-J.; Fan, B-T.; Yuan, S.G.; Panay, A.; Doucet, J.P. 3D-QSAR study on apicidin inhibit histone deacetylase. Chin. J. Chem., 2003, 21, 1596-1607.
[http://dx.doi.org/10.1002/cjoc.20030211216]
[69]
Guo, Y.; Xiao, J.; Guo, Z.; Chu, F.; Cheng, Y.; Wu, S. Exploration of a binding mode of indole amide analogues as potent histone deacetylase inhibitors and 3D-QSAR analyses. Bioorg. Med. Chem., 2005, 13(18), 5424-5434.
[http://dx.doi.org/10.1016/j.bmc.2005.05.016] [PMID: 15963726]
[70]
Juvale, D.C.; Kulkarni, V.V.; Deokar, H.S.; Wagh, N.K.; Padhye, S.B.; Kulkarni, V.M. 3D-QSAR of histone deacetylase inhibitors: Hydroxamate analogues. Org. Biomol. Chem., 2006, 4(15), 2858-2868.
[http://dx.doi.org/10.1039/b606365a] [PMID: 16855733]
[71]
Liu, B.; Lu, A-J.; Liao, C-Z.; Liu, H-B.; Zhou, J-J. 3D-QSAR of sulfonamide hydroxamic acid HDAC inhibitors. Wuli Huaxue Xuebao, 2005, 21, 333-337.
[72]
Agarwal, N.; Bajpai, A.; Srivastava, V.; Gupta, S.P. A quantitative structure-activity relationship and molecular modeling study on a series of biaryl imidazole derivatives acting as H+/K+-ATPase inhibitors. Biochem Res Int,, 2012, 2013(2013), Article ID 810691.
[73]
Ragno, R.; Simeoni, S.; Valente, S.; Massa, S.; Mai, A. 3-D QSAR studies on histone deacetylase inhibitors. A GOLPE/GRID approach on different series of compounds. J. Chem. Inf. Model., 2006, 46(3), 1420-1430.
[http://dx.doi.org/10.1021/ci050556b] [PMID: 16711762]
[74]
Chen, Y.; Li, H.; Tang, W.; Zhu, C.; Jiang, Y.; Zou, J.; Yu, Q.; You, Q. 3D-QSAR studies of HDACs inhibitors using pharmacophore-based alignment. Eur. J. Med. Chem., 2009, 44(7), 2868-2876.
[http://dx.doi.org/10.1016/j.ejmech.2008.12.008] [PMID: 19136179]
[75]
Mahipal, A.; Tanwar, O.P.; Karthikeyan, C.; Moorthy, N.S.H.N.; Trivedi, P. 3D QSAR of aminophenyl benzamide derivatives as histone deacetylase inhibitors. Med. Chem., 2010, 6(5), 277-285.
[http://dx.doi.org/10.2174/157340610793358846] [PMID: 20977417]
[76]
Clark, J.; Shevchuk, T.; Kho, M.R.; Smith, S.S. Methods for the design and analysis of oligodeoxynucleotide-based DNA (cytosine-5) methyltransferase inhibitors. Anal. Biochem., 2003, 321(1), 50-64.
[http://dx.doi.org/10.1016/S0003-2697(03)00402-0] [PMID: 12963055]
[77]
Aboalhaija, N.H.; Zihlif, M.A.; Taha, M.O. Discovery of new selective cytotoxic agents against Bcl-2 expressing cancer cells using ligand-based modeling. Chem. Biol. Interact., 2016, 250, 12-26.
[http://dx.doi.org/10.1016/j.cbi.2016.03.006] [PMID: 26954606]
[78]
Almerico, A.M.; Tutone, M.; Lauria, A. 3D-QSAR pharmacophore modeling and in silico screening of new Bcl-xl inhibitors. Eur. J. Med. Chem., 2010, 45(11), 4774-4782.
[http://dx.doi.org/10.1016/j.ejmech.2010.07.042] [PMID: 20728251]
[79]
Khanfar, M.A.; Taha, M.O. Elaborate ligand-based modeling coupled with multiple linear regression and k nearest neighbor QSAR analyses unveiled new nanomolar mTOR inhibitors. J. Chem. Inf. Model., 2013, 53(10), 2587-2612.
[http://dx.doi.org/10.1021/ci4003798] [PMID: 24050502]
[80]
Azam, S.S.; Abro, A.; Tanvir, F.; Parvaiz, N. Identification of unique binding site and molecular docking studies for structurally diverse Bcl-xL inhibitors. Med. Chem. Res., 2014, 23, 3765-3783.
[http://dx.doi.org/10.1007/s00044-014-0957-5]
[81]
Mukherjee, P.; Desai, P.; Zhou, Y.D.; Avery, M. Targeting the BH3 domain mediated protein-protein interaction of Bcl-xL through virtual screening. J. Chem. Inf. Model., 2010, 50(5), 906-923.
[http://dx.doi.org/10.1021/ci1000373] [PMID: 20392095]
[82]
Zheng, C.H.; Zhou, Y.J.; Zhu, J.; Ji, H.T.; Chen, J.; Li, Y.W.; Sheng, C.Q.; Lu, J.G.; Jiang, J.H.; Tang, H.; Song, Y.L. Construction of a three-dimensional pharmacophore for Bcl-2 inhibitors by flexible docking and the multiple copy simultaneous search method. Bioorg. Med. Chem., 2007, 15(19), 6407-6417.
[http://dx.doi.org/10.1016/j.bmc.2007.06.052] [PMID: 17629704]
[83]
Pinto, M. Orzaez, Mdel.M.; Delgado-Soler, L.; Perez, J.J.; Rubio-Martinez, J. Rational design of new class of BH3-mimetics as inhibitors of the Bcl-xL protein. J. Chem. Inf. Model., 2011, 51(6), 1249-1258.
[http://dx.doi.org/10.1021/ci100501d] [PMID: 21528891]
[84]
Sivakumar, D.; Gorai, B.; Sivaraman, T. Screening efficient BH3-mimetics to hBcl-B by means of peptidodynmimetic method. Mol. Biosyst., 2013, 9(4), 700-712.
[http://dx.doi.org/10.1039/c2mb25195g] [PMID: 23385522]
[85]
Enyedy, I.J.; Ling, Y.; Nacro, K.; Tomita, Y.; Wu, X.; Cao, Y.; Guo, R.; Li, B.; Zhu, X.; Huang, Y.; Long, Y.Q.; Roller, P.P.; Yang, D.; Wang, S. Discovery of small-molecule inhibitors of Bcl-2 through structure-based computer screening. J. Med. Chem., 2001, 44(25), 4313-4324.
[http://dx.doi.org/10.1021/jm010016f] [PMID: 11728179]
[86]
Wang, J.L.; Liu, D.; Zhang, Z.J.; Shan, S.; Han, X.; Srinivasula, S.M.; Croce, C.M.; Alnemri, E.S.; Huang, Z. Structure-based discovery of an organic compound that binds Bcl-2 protein and induces apoptosis of tumor cells. Proc. Natl. Acad. Sci. USA, 2000, 97(13), 7124-7129.
[http://dx.doi.org/10.1073/pnas.97.13.7124] [PMID: 10860979]
[87]
Levoin, N.; Vo, D.D.; Gautier, F.; Barillé-Nion, S.; Juin, P.; Tasseau, O.; Grée, R. A combination of in silico and SAR studies to identify binding hot spots of Bcl-xL inhibitors. Bioorg. Med. Chem., 2015, 23(8), 1747-1757.
[http://dx.doi.org/10.1016/j.bmc.2015.02.060] [PMID: 25797160]
[88]
Morphy, R.; Rankovic, Z. Designed multiple ligands. An emerging drug discovery paradigm. J. Med. Chem., 2005, 48(21), 6523-6543.
[http://dx.doi.org/10.1021/jm058225d] [PMID: 16220969]
[89]
Hopkins, A.L. Network pharmacology: The next paradigm in drug discovery. Nat. Chem. Biol., 2008, 4(11), 682-690.
[http://dx.doi.org/10.1038/nchembio.118] [PMID: 18936753]
[90]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N. Chemoinformatics in anti-cancer chemotherapy: Multi-target QSAR model for the in silico discovery of anti-breast cancer agents. Eur. J. Pharm. Sci., 2012, 47(1), 273-279.
[http://dx.doi.org/10.1016/j.ejps.2012.04.012] [PMID: 22538055]
[91]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N. Rational drug design for anti-cancer chemotherapy: Multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents. Bioorg. Med. Chem., 2012, 20(15), 4848-4855.
[http://dx.doi.org/10.1016/j.bmc.2012.05.071] [PMID: 22750007]
[92]
Venkatraman, V.; Pérez-Nueno, V.I.; Mavridis, L.; Ritchie, D.W. Comprehensive comparison of ligand-based virtual screening tools against the DUD data set reveals limitations of current 3D methods. J. Chem. Inf. Model., 2010, 50(12), 2079-2093.
[http://dx.doi.org/10.1021/ci100263p] [PMID: 21090728]
[93]
Koeppen, H.; Kriegl, J.; Lessel, U.; Tautermann, C.S.; Wellenzohn, B. Ligand-Based Virtual Screening. In Virtual Screening;; Wiley-VCH Verlag GmbH & Co. KGaA,, 2011, pp. 61-85.
[http://dx.doi.org/10.1002/9783527633326.ch3]
[94]
Schuster, D. 3D pharmacophores as tools for activity profiling. Drug Discov. Today. Technol., 2010, 7(4), e203-e270.
[http://dx.doi.org/10.1016/j.ddtec.2010.11.006] [PMID: 24103796]
[95]
Spitzer, G.M.; Heiss, M.; Mangold, M.; Markt, P.; Kirchmair, J.; Wolber, G.; Liedl, K.R. One concept, three implementations of 3D pharmacophore-based virtual screening: Distinct coverage of chemical search space. J. Chem. Inf. Model., 2010, 50(7), 1241-1247.
[http://dx.doi.org/10.1021/ci100136b] [PMID: 20583761]
[96]
Sanders, M.P.A.; Barbosa, A.J.M.; Zarzycka, B.; Nicolaes, G.A.F.; Klomp, J.P.G.; de Vlieg, J.; Del Rio, A. Comparative analysis of pharmacophore screening tools. J. Chem. Inf. Model., 2012, 52(6), 1607-1620.
[http://dx.doi.org/10.1021/ci2005274] [PMID: 22646988]
[97]
Vadivelan, S.; Sinha, B.N.; Rambabu, G.; Boppana, K.; Jagarlapudi, S.A.R.P. Pharmacophore modeling and virtual screening studies to design some potential histone deacetylase inhibitors as new leads. J. Mol. Graph. Model., 2008, 26(6), 935-946.
[http://dx.doi.org/10.1016/j.jmgm.2007.07.002] [PMID: 17707666]
[98]
Kandakatla, N.; Ramakrishnan, G. Ligand based pharmacophore modeling and virtual screening studies to design novel HDAC2 inhibitors. Adv Bioinform, 2014, 2014(2014), Article ID 812148.
[http://dx.doi.org/10.1155/2014/812148]
[99]
Wang, J.; Chen, L.; Sinha, S.H.; Liang, Z.; Chai, H.; Muniyan, S.; Chou, Y.W.; Yang, C.; Yan, L.; Feng, Y.; Li, K.K.; Lin, M.F.; Jiang, H.; Zheng, Y.G.; Luo, C. Pharmacophore-based virtual screening and biological evaluation of small molecule inhibitors for protein arginine methylation. J. Med. Chem., 2012, 55(18), 7978-7987.
[http://dx.doi.org/10.1021/jm300521m] [PMID: 22928876]
[100]
Drwal, M.N.; Griffith, R. Combination of ligand- and structure-based methods in virtual screening. Drug Discov. Today. Technol., 2013, 10(3), e395-e401.
[http://dx.doi.org/10.1016/j.ddtec.2013.02.002] [PMID: 24050136]
[101]
Drwal, M.N.; Agama, K.; Wakelin, L.P.; Pommier, Y.; Griffith, R. Exploring DNA topoisomerase I ligand space in search of novel anticancer agents. PLoS One, 2011, 6(9)e25150
[http://dx.doi.org/10.1371/journal.pone.0025150] [PMID: 21966440]
[102]
Svensson, F.; Karlén, A.; Sköld, C. Virtual screening data fusion using both structure- and ligand-based methods. J. Chem. Inf. Model., 2012, 52(1), 225-232.
[http://dx.doi.org/10.1021/ci2004835] [PMID: 22148635]
[103]
Swann, S.L.; Brown, S.P.; Muchmore, S.W.; Patel, H.; Merta, P.; Locklear, J.; Hajduk, P.J. A unified, probabilistic framework for structure- and ligand-based virtual screening. J. Med. Chem., 2011, 54(5), 1223-1232.
[http://dx.doi.org/10.1021/jm1013677] [PMID: 21309579]
[104]
Planesas, J.M.; Claramunt, R.M.; Teixidó, J.; Borrell, J.I.; Pérez-Nueno, V.I. Improving VEGFR-2 docking-based screening by pharmacophore postfiltering and similarity search postprocessing. J. Chem. Inf. Model., 2011, 51(4), 777-787.
[http://dx.doi.org/10.1021/ci1002763] [PMID: 21417262]
[105]
Kroemer, R.T. Structure-based drug design: Docking and scoring. Curr. Protein Pept. Sci., 2007, 8(4), 312-328.
[http://dx.doi.org/10.2174/138920307781369382] [PMID: 17696866]
[106]
Meng, X.Y.; Zhang, H.X.; Mezei, M.; Cui, M. Molecular docking: A powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des, 2011, 7(2), 146-157.
[http://dx.doi.org/10.2174/157340911795677602] [PMID: 21534921]
[107]
Warren, G.L.; Andrews, C.W.; Capelli, A.M.; Clarke, B.; LaLonde, J.; Lambert, M.H.; Lindvall, M.; Nevins, N.; Semus, S.F.; Senger, S.; Tedesco, G.; Wall, I.D.; Woolven, J.M.; Peishoff, C.E.; Head, M.S. A critical assessment of docking programs and scoring functions. J. Med. Chem., 2006, 49(20), 5912-5931.
[http://dx.doi.org/10.1021/jm050362n] [PMID: 17004707]
[108]
Wang, R.; Lai, L.; Wang, S. Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J. Comput. Aided Mol. Des., 2002, 16(1), 11-26.
[http://dx.doi.org/10.1023/A:1016357811882] [PMID: 12197663]
[109]
Huang, N.; Kalyanaraman, C.; Bernacki, K.; Jacobson, M.P. Molecular mechanics methods for predicting protein-ligand binding. Phys. Chem. Chem. Phys., 2006, 8(44), 5166-5177.
[http://dx.doi.org/10.1039/B608269F] [PMID: 17203140]
[110]
Ballester, P.J.; Mitchell, J.B.O. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics, 2010, 26(9), 1169-1175.
[http://dx.doi.org/10.1093/bioinformatics/btq112] [PMID: 20236947]
[111]
Yuriev, E.; Ramsland, P.A. Latest developments in molecular docking: 2010-2011 in review. J. Mol. Recognit., 2013, 26(5), 215-239.
[http://dx.doi.org/10.1002/jmr.2266] [PMID: 23526775]
[112]
Ewing, T.J.; Makino, S.; Skillman, A.G.; Kuntz, I.D. DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases. J. Comput. Aided Mol. Des., 2001, 15(5), 411-428.
[http://dx.doi.org/10.1023/A:1011115820450] [PMID: 11394736]
[113]
Morris, G.M.; Goodsell, D.S.; Halliday, R.S.; Huey, R.; Hart, W.E.; Belew, R.K.; Olson, A.J. Automated docking using a Lamarckian genetic algorithm and empirical binding free energy function. J. Comput. Chem., 1998, 19, 1639-1662.
[http://dx.doi.org/10.1002/(SICI)1096-987X(19981115)19:14<1639:AID-JCC10>3.0.CO;2-B]
[114]
Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem., 2009, 30(16), 2785-2791.
[http://dx.doi.org/10.1002/jcc.21256] [PMID: 19399780]
[115]
Jones, G.; Willett, P.; Glen, R.C. Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J. Mol. Biol., 1995, 245(1), 43-53.
[http://dx.doi.org/10.1016/S0022-2836(95)80037-9] [PMID: 7823319]
[116]
Friesner, R.A.; Banks, J.L.; Murphy, R.B.; Halgren, T.A.; Klicic, J.J.; Mainz, D.T.; Repasky, M.P.; Knoll, E.H.; Shelley, M.; Perry, J.K.; Shaw, D.E.; Francis, P.; Shenkin, P.S. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem., 2004, 47(7), 1739-1749.
[http://dx.doi.org/10.1021/jm0306430] [PMID: 15027865]
[117]
Spannhoff, A.; Heinke, R.; Bauer, I.; Trojer, P.; Metzger, E.; Gust, R.; Schüle, R.; Brosch, G.; Sippl, W.; Jung, M. Target-based approach to inhibitors of histone arginine methyltransferases. J. Med. Chem., 2007, 50(10), 2319-2325.
[http://dx.doi.org/10.1021/jm061250e] [PMID: 17432842]
[118]
Spannhoff, A.; Machmur, R.; Heinke, R.; Trojer, P.; Bauer, I.; Brosch, G.; Schüle, R.; Hanefeld, W.; Sippl, W.; Jung, M. A novel arginine methyltransferase inhibitor with cellular activity. Bioorg. Med. Chem. Lett., 2007, 17(15), 4150-4153.
[http://dx.doi.org/10.1016/j.bmcl.2007.05.088] [PMID: 17570663]
[119]
Vidler, L.R.; Filippakopoulos, P.; Fedorov, O.; Picaud, S.; Martin, S.; Tomsett, M.; Woodward, H.; Brown, N.; Knapp, S.; Hoelder, S. Discovery of novel small-molecule inhibitors of BRD4 using structure-based virtual screening. J. Med. Chem., 2013, 56(20), 8073-8088.
[http://dx.doi.org/10.1021/jm4011302] [PMID: 24090311]
[120]
Zhao, H.; Gartenmann, L.; Dong, J.; Spiliotopoulos, D.; Caflisch, A. Discovery of BRD4 bromodomain inhibitors by fragment-based high-throughput docking. Bioorg. Med. Chem. Lett., 2014, 24(11), 2493-2496.
[http://dx.doi.org/10.1016/j.bmcl.2014.04.017] [PMID: 24767840]
[121]
Chen, S.; Wang, Y.; Zhou, W.; Li, S.; Peng, J.; Shi, Z.; Hu, J.; Liu, Y.C.; Ding, H.; Lin, Y.; Li, L.; Cheng, S.; Liu, J.; Lu, T.; Jiang, H.; Liu, B.; Zheng, M.; Luo, C. Identifying novel selective non-nucleoside DNA methyltransferase 1 inhibitors through docking-based virtual screening. J. Med. Chem., 2014, 57(21), 9028-9041.
[http://dx.doi.org/10.1021/jm501134e] [PMID: 25333769]
[122]
Kuck, D.; Singh, N.; Lyko, F.; Medina-Franco, J.L. Novel and selective DNA methyltransferase inhibitors: Docking-based virtual screening and experimental evaluation. Bioorg. Med. Chem., 2010, 18(2), 822-829.
[http://dx.doi.org/10.1016/j.bmc.2009.11.050] [PMID: 20006515]
[123]
Ashour, A.; El-Sharkawy, S.; Amer, M.; Abdel Bar, F.; Katakura, Y.; Miyamoto, T.; Toyota, N.; Bang, T.H.; Kondo, R.; Shimizu, K. Rational design and synthesis of topoisomerase I and II inhibitors based on oleanolic acid moiety for new anti-cancer drugs. Bioorg. Med. Chem., 2014, 22(1), 211-220.
[http://dx.doi.org/10.1016/j.bmc.2013.11.034] [PMID: 24326278]
[124]
Cai, H.; Huang, X.; Xu, S.; Shen, H.; Zhang, P.; Huang, Y.; Jiang, J.; Sun, Y.; Jiang, B.; Wu, X.; Yao, H.; Xu, J. Discovery of novel hybrids of diaryl-1,2,4-triazoles and caffeic acid as dual inhibitors of cyclooxygenase-2 and 5-lipoxygenase for cancer therapy. Eur. J. Med. Chem., 2016, 108, 89-103.
[http://dx.doi.org/10.1016/j.ejmech.2015.11.013] [PMID: 26638042]
[125]
Cardama, G.A.; Comin, M.J.; Hornos, L.; Gonzalez, N.; Defelipe, L.; Turjanski, A.G.; Alonso, D.F.; Gomez, D.E.; Menna, P.L. Preclinical development of novel Rac1-GEF signaling inhibitors using a rational design approach in highly aggressive breast cancer cell lines. Anticancer. Agents Med. Chem., 2014, 14(6), 840-851.
[http://dx.doi.org/10.2174/18715206113136660334] [PMID: 24066799]
[126]
Dutta Gupta, S.; Revathi, B.; Mazaira, G.I.; Galigniana, M.D.; Subrahmanyam, C.V.; Gowrishankar, N.L.; Raghavendra, N.M. 2,4-dihydroxy benzaldehyde derived Schiff bases as small molecule Hsp90 inhibitors: Rational identification of a new anticancer lead. Bioorg. Chem., 2015, 59, 97-105.
[http://dx.doi.org/10.1016/j.bioorg.2015.02.003] [PMID: 25727264]
[127]
Gao, C.; Bourke, E.; Scobie, M.; Famme, M.A.; Koolmeister, T.; Helleday, T.; Eriksson, L.A.; Lowndes, N.F.; Brown, J.A. Rational design and validation of a Tip60 histone acetyltransferase inhibitor. Sci. Rep., 2014, 4, 5372.
[http://dx.doi.org/10.1038/srep05372] [PMID: 24947938]
[128]
Khanfar, M.A.; AbuKhader, M.M.; Alqtaishat, S.; Taha, M.O. Pharmacophore modeling, homology modeling, and in silico screening reveal mammalian target of rapamycin inhibitory activities for sotalol, glyburide, metipranolol, sulfamethizole, glipizide, and pioglitazone. J. Mol. Graph. Model., 2013, 42, 39-49.
[http://dx.doi.org/10.1016/j.jmgm.2013.02.009] [PMID: 23545333]
[129]
Parker, J.P.; Nimir, H.; Griffith, D.M.; Duff, B.; Chubb, A.J.; Brennan, M.P.; Morgan, M.P.; Egan, D.A.; Marmion, C.J. A novel platinum complex of the histone deacetylase inhibitor belinostat: Rational design, development and in vitro cytotoxicity. J. Inorg. Biochem., 2013, 124, 70-77.
[http://dx.doi.org/10.1016/j.jinorgbio.2013.03.011] [PMID: 23603796]
[130]
Xue, W.; Song, B.A.; Zhao, H.J.; Qi, X.B.; Huang, Y.J.; Liu, X.H. Novel myricetin derivatives: Design, synthesis and anticancer activity. Eur. J. Med. Chem., 2015, 97, 155-163.
[http://dx.doi.org/10.1016/j.ejmech.2015.04.063] [PMID: 25965778]
[131]
Wang, Y.; Cheng, F.X.; Yuan, X.L.; Tang, W.J.; Shi, J.B.; Liao, C.Z.; Liu, X.H. Dihydropyrazole derivatives as telomerase inhibitors: Structure-based design, synthesis, SAR and anticancer evaluation in vitro and in vivo. Eur. J. Med. Chem., 2016, 112, 231-251.
[http://dx.doi.org/10.1016/j.ejmech.2016.02.009] [PMID: 26900656]
[132]
Staker, B.L.; Feese, M.D.; Cushman, M.; Pommier, Y.; Zembower, D.; Stewart, L.; Burgin, A.B. Structures of three classes of anticancer agents bound to the human topoisomerase I-DNA covalent complex. J. Med. Chem., 2005, 48(7), 2336-2345.
[http://dx.doi.org/10.1021/jm049146p] [PMID: 15801827]
[133]
Berger, J.M.; Gamblin, S.J.; Harrison, S.C.; Wang, J.C. Structure and mechanism of DNA topoisomerase II. Nature, 1996, 379(6562), 225-232.
[http://dx.doi.org/10.1038/379225a0] [PMID: 8538787]
[134]
Ellenbroek, S.I.; Collard, J.G. Rho GTPases: Functions and association with cancer. Clin. Exp. Metastasis, 2007, 24(8), 657-672.
[http://dx.doi.org/10.1007/s10585-007-9119-1] [PMID: 18000759]
[135]
Fritz, G.; Just, I.; Kaina, B. Rho GTPases are over-expressed in human tumors. Int. J. Cancer, 1999, 81(5), 682-687.
[http://dx.doi.org/10.1002/(SICI)1097-0215(19990531)81:5<682:AID-IJC2>3.0.CO;2-B] [PMID: 10328216]
[136]
Fritz, G.; Brachetti, C.; Bahlmann, F.; Schmidt, M.; Kaina, B. Rho GTPases in human breast tumours: Expression and mutation analyses and correlation with clinical parameters. Br. J. Cancer, 2002, 87(6), 635-644.
[http://dx.doi.org/10.1038/sj.bjc.6600510] [PMID: 12237774]
[137]
Kamai, T.; Yamanishi, T.; Shirataki, H.; Takagi, K.; Asami, H.; Ito, Y.; Yoshida, K. Overexpression of RhoA, Rac1, and Cdc42 GTPases is associated with progression in testicular cancer. Clin. Cancer Res., 2004, 10(14), 4799-4805.
[http://dx.doi.org/10.1158/1078-0432.CCR-0436-03] [PMID: 15269155]
[138]
Ragno, R.; Mai, A.; Massa, S.; Cerbara, I.; Valente, S.; Bottoni, P.; Scatena, R.; Jesacher, F.; Loidl, P.; Brosch, G. 3-(4-Aroyl-1-methyl-1H-pyrrol-2-yl)-N-hydroxy-2-propenamides as a new class of synthetic histone deacetylase inhibitors. 3. Discovery of novel lead compounds through structure-based drug design and docking studies. J. Med. Chem., 2004, 47(6), 1351-1359.
[http://dx.doi.org/10.1021/jm031036f] [PMID: 14998325]
[139]
Subha, K.; Kumar, G.R. Assessment for the identification of better HDAC inhibitor class through binding energy calculations and descriptor analysis. Bioinformation, 2008, 3(5), 218-222.
[http://dx.doi.org/10.6026/97320630003218] [PMID: 19255637]
[140]
Wang, D.F.; Helquist, P.; Wiech, N.L.; Wiest, O. Toward selective histone deacetylase inhibitor design: Homology modeling, docking studies, and molecular dynamics simulations of human class I histone deacetylases. J. Med. Chem., 2005, 48(22), 6936-6947.
[http://dx.doi.org/10.1021/jm0505011] [PMID: 16250652]
[141]
Butler, K.V.; Kalin, J.; Brochier, C.; Vistoli, G.; Langley, B.; Kozikowski, A.P. Rational design and simple chemistry yield a superior, neuroprotective HDAC6 inhibitor, tubastatin A. J. Am. Chem. Soc., 2010, 132(31), 10842-10846.
[http://dx.doi.org/10.1021/ja102758v] [PMID: 20614936]
[142]
Ran, T.; Zhang, Z.; Liu, K.; Lu, Y.; Li, H.; Xu, J.; Xiong, X.; Zhang, Y.; Xu, A.; Lu, S.; Liu, H.; Lu, T.; Chen, Y. Insight into the key interactions of bromodomain inhibitors based on molecular docking, interaction fingerprinting, molecular dynamics and binding free energy calculation. Mol. Biosyst., 2015, 11(5), 1295-1304.
[http://dx.doi.org/10.1039/C4MB00723A] [PMID: 25758752]
[143]
Singh, N.; Dueñas-González, A.; Lyko, F.; Medina-Franco, J.L. Molecular modeling and molecular dynamics studies of hydralazine with human DNA methyltransferase 1. ChemMedChem, 2009, 4(5), 792-799.
[http://dx.doi.org/10.1002/cmdc.200900017] [PMID: 19322801]
[144]
Aldawsari, F.S.; Aguayo-Ortiz, R.; Kapilashrami, K.; Yoo, J.; Luo, M.; Medina-Franco, J.L.; Velázquez-Martínez, C.A. Resveratrol-salicylate derivatives as selective DNMT3 inhibitors and anticancer agents. J. Enzyme Inhib. Med. Chem., 2016, 31(5), 695-703.
[PMID: 26118420]
[145]
Medina-Franco, J.L.; Yoo, J. Docking of a novel DNA methyltransferase inhibitor identified from high-throughput screening: Insights to unveil inhibitors in chemical databases. Mol. Divers., 2013, 17(2), 337-344.
[http://dx.doi.org/10.1007/s11030-013-9428-z] [PMID: 23447100]
[146]
Angeles, E.; Vázquez-Valadéz, V.H.; Vázquez-Valadéz, O.; Velázquez-Sánchez, A.M.; Ramírez, A.; Martínez, L.; Diaz-Barriga, S.; Romero-Rojas, A.; Cabrera, G.; Lopez-Castanares, R.; Duenas-Gonzalez, A. Computational studies of 1-hydrazinophthalazine (hydralazine) as antineoplasic agent. Docking studies on methyltransferase. Lett. Drug Des. Discov., 2005, 2, 282-286.
[http://dx.doi.org/10.2174/1570180054038413]
[147]
Paricharak, S.; Cortés-Ciriano, I.; IJzerman, A.P.; Malliavin, T.E.; Bender, A. Proteochemometric modelling coupled to in silico target prediction: An integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules. J. Cheminform., 2015, 7, 15.
[http://dx.doi.org/10.1186/s13321-015-0063-9] [PMID: 25926892]
[148]
Kinnings, S.L.; Liu, N.; Tonge, P.J.; Jackson, R.M.; Xie, L.; Bourne, P.E. A machine learning-based method to improve docking scoring functions and its application to drug repurposing. J. Chem. Inf. Model., 2011, 51(2), 408-419.
[http://dx.doi.org/10.1021/ci100369f] [PMID: 21291174]
[149]
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]
[150]
Kubinyi, H. Drug research: Myths, hype and reality. Nat. Rev. Drug Discov., 2003, 2(8), 665-668.
[http://dx.doi.org/10.1038/nrd1156] [PMID: 12904816]
[151]
Terstappen, G.C.; Schlüpen, C.; Raggiaschi, R.; Gaviraghi, G. Target deconvolution strategies in drug discovery. Nat. Rev. Drug Discov., 2007, 6(11), 891-903.
[http://dx.doi.org/10.1038/nrd2410] [PMID: 17917669]
[152]
Jenkins, J.L.; Bender, A.; Davies, J.W. In silico target fishing: Predicting biological targets from chemical structure. Drug Discov. Today. Technol., 2006, 3, 413-421.
[http://dx.doi.org/10.1016/j.ddtec.2006.12.008]
[153]
Mavridis, L.; Mitchell, J.B. Predicting the protein targets for athletic performance-enhancing substances. J. Cheminform., 2013, 5(1), 31.
[http://dx.doi.org/10.1186/1758-2946-5-31] [PMID: 23800040]
[154]
Nikolic, K.; Mavridis, L.; Bautista-Aguilera, O.M.; Marco-Contelles, J.; Stark, H.; do Carmo Carreiras, M.; Rossi, I.; Massarelli, P.; Agbaba, D.; Ramsay, R.R.; Mitchell, J.B. Predicting targets of compounds against neurological diseases using cheminformatic methodology. J. Comput. Aided Mol. Des., 2015, 29(2), 183-198.
[http://dx.doi.org/10.1007/s10822-014-9816-1] [PMID: 25425329]
[155]
Martínez-Jiménez, F.; Papadatos, G.; Yang, L.; Wallace, I.M.; Kumar, V.; Pieper, U.; Sali, A.; Brown, J.R.; Overington, J.P.; Marti-Renom, M.A. Target prediction for an open access set of compounds active against Mycobacterium tuberculosis. PLOS Comput. Biol., 2013, 9(10)e1003253
[http://dx.doi.org/10.1371/journal.pcbi.1003253] [PMID: 24098102]
[156]
Gfeller, D.; Grosdidier, A.; Wirth, M.; Daina, A.; Michielin, O.; Zoete, V. SwissTargetPrediction: A web server for target prediction of bioactive small molecules. Nucleic Acids Res., 2014, 2(Web Server issue), W32-8.
[http://dx.doi.org/10.1093/nar/gku293] [PMID: 24792161]
[157]
Cortes-Ciriano, I.; Ain, Q.U.; Subramanian, V.; Lenselink, E.B.; Mendez-Lucio, O.; IJzerman, A.P.; Wohlfahrt, G.; Prusis, P.; Malliavin, T.E.; van Westen, G.J.P.; Bender, A. Polypharmacology modelling using proteochemometrics (PCM): Recent methodological developments, applications to target families, and future prospects. MedChemComm, 2015, 6, 24-50.
[http://dx.doi.org/10.1039/C4MD00216D]
[158]
Glen, R.C.; Allen, S.C. Ligand-protein docking: Cancer research at the interface between biology and chemistry. Curr. Med. Chem., 2003, 10(9), 763-767.
[http://dx.doi.org/10.2174/0929867033457809] [PMID: 12678780]
[159]
Favia, A.D.; Nobeli, I.; Glaser, F.; Thornton, J.M. Molecular docking for substrate identification: The short-chain dehydrogenases/reductases. J. Mol. Biol., 2008, 375(3), 855-874.
[http://dx.doi.org/10.1016/j.jmb.2007.10.065] [PMID: 18036612]
[160]
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]
[161]
Schomburg, K.T.; Bietz, S.; Briem, H.; Henzler, A.M.; Urbaczek, S.; Rarey, M. Facing the challenges of structure-based target prediction by inverse virtual screening. J. Chem. Inf. Model., 2014, 54(6), 1676-1686.
[http://dx.doi.org/10.1021/ci500130e] [PMID: 24851945]
[162]
Emig, D.; Ivliev, A.; Pustovalova, O.; Lancashire, L.; Bureeva, S.; Nikolsky, Y.; Bessarabova, M. Drug target prediction and repositioning using an integrated network-based approach. PLoS One, 2013, 8(4)e60618
[http://dx.doi.org/10.1371/journal.pone.0060618] [PMID: 23593264]
[163]
Nigsch, F.; Mitchell, J.B.O. Toxicological relationships between proteins obtained from protein target predictions of large toxicity databases. Toxicol. Appl. Pharmacol., 2008, 231(2), 225-234.
[http://dx.doi.org/10.1016/j.taap.2008.05.007] [PMID: 18589467]

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