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Review Article

In Silico药物发现工具箱：先导物的发现和优化中的应用

卷 26, 期 21, 2019

页: [3838 - 3873] 页: 36

弟呕挨: 10.2174/0929867324666171107101035

价格: $65

摘要

背景：新药的发现和开发是一项漫长而昂贵的旅程，从开始构思到批准和营销新药大约需要20年。尽管过去几年中研发支出一直在不断增加，但是引入市场的新药数量一直在稳步下降。这主要是由于临床前和临床安全性问题，仍然占停药的40％。为了解决这个问题，目前许多计算机技术用于潜在安全问题的早期评估/预测，从而可以提高药物发现的成功率并降低与新药开发相关的成本。 方法：在本综述中，我们将分析药物发现流程的早期步骤，描述从疾病选择到潜在客户优化的步骤顺序，并重点介绍用于评估减员风险和制定缓解计划的最常用的计算机工具。 结果：提供了可以在药物发现管道中有效实施和使用的广泛使用的计算机软件工具，数据库和公共计划的完整列表。还提供了一些有关这些工具如何解决问题以及如何增加药物发现和开发计划成功率的示例。最后，将给出一些示例，这些示例中计算机软件的应用已有效促进了上市药物或临床候选药物的开发。 结论：in silico工具箱在早期药物发现的每个步骤中都有很好的应用：（i）目标识别和验证； （ii）命中识别； （iii）领先（iv）优化销售线索。已详细描述了这些步骤中的每一个步骤，从而有效地概述了计算机软件工具在加快新药发现过程的决策过程中所起的作用。

关键词: 药物发现，计算化学，目标验证，先导物，先导化合物的优化，人类基因组测序。

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[1]
Pammolli, F.; Magazzini, L.; Riccaboni, M. The productivity crisis in pharmaceutical R&D. Nat. Rev. Drug Discov., 2011, 10(6), 428-438.
[http://dx.doi.org/10.1038/nrd3405] [PMID: 21629293]
[2]
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]
[3]
U.S. Food & Drug Administration. Comparison of NMEs Approved in 2010 to Previous Years. Available from: . http://Www.fda.gov/Downloads/Drugs/DevelopmentApprovalProcess/HowDrugsareDevelopedandApproved/DrugandBiologicApprovalReports/UCM242695.pdf
[4]
2017_EvaluatePharma_World-Preview-2017-2022.pdf. Available from. Http://Info.evaluategroup.com/Rs/607-YGS-364/Images/WP2017-SUM.pdf
[5]
2016_12_09_Evaluate_review & Prediction of Pharma Products for 2017.pdf. Available from: . valuategroup.com/Rs/607-YGS-364/Images/EPV2017Prev.pdf
[6]
Munos, B. How Fresh Is Big Pharma’s Freshness Index? http://www.forbes.com/sites/bernardmunos/2013/03/22/how-fresh-is-big-pharmas-freshness-index/ (Accessed Jun 30, 2017).
[7]
Belk, D. True Cost of Health-Care. Available from: . Http://Truecostofhealthcare.net/The_pharmaceutical_industry/
[8]
U.S. Food & Drug Administration. 2014 Novel New Drugs Summary. Available from: . http://Www.fda.gov/Downloads/Drugs/DevelopmentApprovalProcess/Drug Innovation/UCM430299.pdf
[9]
U.S. Food & Drug Administration. 2015 Novel Drugs Summary. Available from: . http://Www.fda.gov/Downloads/Drugs/DevelopmentApprovalProcess/DrugInnovation/UCM485053.pdf
[10]
Chemical & Engineering News c&en. The Year In New Drugs 2014. Available from: . http://Cen.acs.org//Content/Dam/Cen/93/5/09305-Cover.pdf
[11]
Vallance, P. Industry-Academic Relationship in a New Era of Drug Discovery. J. Clin. Oncol., 2016, 34(29), 3570-3575.
[http://dx.doi.org/10.1200/JCO.2016.68.4217] [PMID: 27458309]
[12]
Richter, L.; Ecker, G.F. Medicinal chemistry in the era of big data. Drug Discov. Today. Technol., 2015, 14, 37-41.
[http://dx.doi.org/10.1016/j.ddtec.2015.06.001] [PMID: 26194586]
[13]
Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E.W., Jr Computational methods in drug discovery. Pharmacol. Rev., 2013, 66(1), 334-395.
[http://dx.doi.org/10.1124/pr.112.007336] [PMID: 24381236]
[14]
Wassermann, A.M.; Lounkine, E.; Davies, J.W.; Glick, M.; Camargo, L.M. The opportunities of mining historical and collective data in drug discovery. Drug Discov. Today, 2015, 20(4), 422-434.
[http://dx.doi.org/10.1016/j.drudis.2014.11.004] [PMID: 25463034]
[15]
Terstappen, G.C.; Reggiani, A. In silico research in drug discovery. Trends Pharmacol. Sci., 2001, 22(1), 23-26.
[http://dx.doi.org/10.1016/S0165-6147(00)01584-4] [PMID: 11165668]
[16]
Greenman, C.; Stephens, P.; Smith, R.; Dalgliesh, G.L.; Hunter, C.; Bignell, G.; Davies, H.; Teague, J.; Butler, A.; Stevens, C.; Edkins, S.; O’Meara, S.; Vastrik, I.; Schmidt, E.E.; Avis, T.; Barthorpe, S.; Bhamra, G.; Buck, G.; Choudhury, B.; Clements, J.; Cole, J.; Dicks, E.; Forbes, S.; Gray, K.; Halliday, K.; Harrison, R.; Hills, K.; Hinton, J.; Jenkinson, A.; Jones, D.; Menzies, A.; Mironenko, T.; Perry, J.; Raine, K.; Richardson, D.; Shepherd, R.; Small, A.; Tofts, C.; Varian, J.; Webb, T.; West, S.; Widaa, S.; Yates, A.; Cahill, D.P.; Louis, D.N.; Goldstraw, P.; Nicholson, A.G.; Brasseur, F.; Looijenga, L.; Weber, B.L.; Chiew, Y-E.; DeFazio, A.; Greaves, M.F.; Green, A.R.; Campbell, P.; Birney, E.; Easton, D.F.; Chenevix-Trench, G.; Tan, M-H.; Khoo, S.K.; Teh, B.T.; Yuen, S.T.; Leung, S.Y.; Wooster, R.; Futreal, P.A.; Stratton, M.R. Patterns of somatic mutation in human cancer genomes. Nature, 2007, 446(7132), 153-158.
[http://dx.doi.org/10.1038/nature05610] [PMID: 17344846]
[17]
Chichester, C.; Gaudet, P. Target discovery from protein databases: Challenges for curation. Drug Discov. Today. Technol., 2015, 14, 11-16.
[http://dx.doi.org/10.1016/j.ddtec.2015.01.003] [PMID: 26194582]
[18]
Marx, V. Biology: The big challenges of big data. Nature, 2013, 498(7453), 255-260.
[http://dx.doi.org/10.1038/498255a] [PMID: 23765498]
[19]
Mustata, G.; Muftuoglu, Y. Computational Strategies in Cancer Drug Discovery.Advances in Cancer Management; InTech. , 2012.
[http://dx.doi.org/10.5772/1781] [http://dx.doi.org/10.5772/24177]
[20]
Villoutreix, B.O.; Lagorce, D.; Labbé, C.M.; Sperandio, O.; Miteva, M.A. One hundred thousand mouse clicks down the road: Selected online resources supporting drug discovery collected over a decade. Drug Discov. Today, 2013, 18(21-22), 1081-1089.
[http://dx.doi.org/10.1016/j.drudis.2013.06.013] [PMID: 23831439]
[21]
Van Drie, J.H. Computer-aided drug design: The next 20 years. J. Comput. Aided Mol. Des., 2007, 21(10-11), 591-601.
[http://dx.doi.org/10.1007/s10822-007-9142-y] [PMID: 17989929]
[22]
Briggs, K.; Cases, M.; Heard, D.J.; Pastor, M.; Pognan, F.; Sanz, F.; Schwab, C.H.; Steger-Hartmann, T.; Sutter, A.; Watson, D.K.; Wichard, J.D. Inroads to predict in vivo toxicology-an introduction to the eTOX Project. Int. J. Mol. Sci., 2012, 13(3), 3820-3846.
[http://dx.doi.org/10.3390/ijms13033820] [PMID: 22489185]
[23]
Ratnam, J.; Zdrazil, B.; Digles, D.; Cuadrado-Rodriguez, E.; Neefs, J-M.; Tipney, H.; Siebes, R.; Waagmeester, A.; Bradley, G.; Chau, C.H.; Richter, L.; Brea, J.; Evelo, C.T.; Jacoby, E.; Senger, S.; Loza, M.I.; Ecker, G.F.; Chichester, C. The application of the open pharmacological concepts triple store (open PHACTS) to support drug discovery research. PLoS One, 2014, 9(12)e115460
[http://dx.doi.org/10.1371/journal.pone.0115460] [PMID: 25522365]
[24]
Song, W.; Gardner, S.A.; Hovhannisyan, H.; Natalizio, A.; Weymouth, K.S.; Chen, W.; Thibodeau, I.; Bogdanova, E.; Letovsky, S.; Willis, A.; Nagan, N. Exploring the landscape of pathogenic genetic variation in the ExAC population database: Insights of relevance to variant classification. Genet. Med., 2016, 18(8), 850-854.
[http://dx.doi.org/10.1038/gim.2015.180] [PMID: 26681313]
[25]
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]
[26]
Overington, J.P.; Al-Lazikani, B.; Hopkins, A.L. How many drug targets are there? Nat. Rev. Drug Discov., 2006, 5(12), 993-996.
[http://dx.doi.org/10.1038/nrd2199] [PMID: 17139284]
[27]
Cook, D.; Brown, D.; Alexander, R.; March, R.; Morgan, P.; Satterthwaite, G.; Pangalos, M.N. Lessons learned from the fate of AstraZeneca’s drug pipeline: A five-dimensional framework. Nat. Rev. Drug Discov., 2014, 13(6), 419-431.
[http://dx.doi.org/10.1038/nrd4309] [PMID: 24833294]
[28]
Kitano, H. Computational systems biology. Nature, 2002, 420(6912), 206-210.
[http://dx.doi.org/10.1038/nature01254] [PMID: 12432404]
[29]
Croft, D.; Mundo, A.F.; Haw, R.; Milacic, M.; Weiser, J.; Wu, G.; Caudy, M.; Garapati, P.; Gillespie, M.; Kamdar, M.R.; Jassal, B.; Jupe, S.; Matthews, L.; May, B.; Palatnik, S.; Rothfels, K.; Shamovsky, V.; Song, H.; Williams, M.; Birney, E.; Hermjakob, H.; Stein, L.; D’Eustachio, P. The Reactome pathway knowledgebase. Nucleic Acids Res., 2014, 42(Database issue), D472-D477.
[http://dx.doi.org/10.1093/nar/gkt1102] [PMID: 24243840]
[30]
Fabregat, A.; Sidiropoulos, K.; Garapati, P.; Gillespie, M.; Hausmann, K.; Haw, R.; Jassal, B.; Jupe, S.; Korninger, F.; McKay, S.; Matthews, L.; May, B.; Milacic, M.; Rothfels, K.; Shamovsky, V.; Webber, M.; Weiser, J.; Williams, M.; Wu, G.; Stein, L.; Hermjakob, H.; D’Eustachio, P. The Reactome pathway Knowledgebase. Nucleic Acids Res., 2016, 44(D1), D481-D487.
[http://dx.doi.org/10.1093/nar/gkv1351] [PMID: 26656494]
[31]
Check Hayden, E. A radical revision of human genetics. Nature, 2016, 538(7624), 154-157.
[http://dx.doi.org/10.1038/538154a] [PMID: 27734888]
[32]
Cooper, D.N.; Ball, E.V.; Krawczak, M. The human gene mutation database. Nucleic Acids Res., 1998, 26(1), 285-287.
[http://dx.doi.org/10.1093/nar/26.1.285] [PMID: 9399854]
[33]
Stenson, P.D.; Mort, M.; Ball, E.V.; Howells, K.; Phillips, A.D.; Thomas, N.S.; Cooper, D.N. The human gene mutation database: 2008 update. Genome Med., 2009, 1(1), 13.
[http://dx.doi.org/10.1186/gm13] [PMID: 19348700]
[34]
Bamford, S.; Dawson, E.; Forbes, S.; Clements, J.; Pettett, R.; Dogan, A.; Flanagan, A.; Teague, J.; Futreal, P.A.; Stratton, M.R.; Wooster, R. The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br. J. Cancer, 2004, 91(2), 355-358.
[http://dx.doi.org/10.1038/sj.bjc.6601894] [PMID: 15188009]
[35]
Forbes, S.A.; Beare, D.; Gunasekaran, P.; Leung, K.; Bindal, N.; Boutselakis, H.; Ding, M.; Bamford, S.; Cole, C.; Ward, S.; Kok, C.Y.; Jia, M.; De, T.; Teague, J.W.; Stratton, M.R.; McDermott, U.; Campbell, P.J. COSMIC: Exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res., 2015, 43(Database issue), D805-D811.
[http://dx.doi.org/10.1093/nar/gku1075] [PMID: 25355519]
[36]
Berman, H.M.; Battistuz, T.; Bhat, T.N.; Bluhm, W.F.; Bourne, P.E.; Burkhardt, K.; Feng, Z.; Gilliland, G.L.; Iype, L.; Jain, S.; Fagan, P.; Marvin, J.; Padilla, D.; Ravichandran, V.; Schneider, B.; Thanki, N.; Weissig, H.; Westbrook, J.D.; Zardecki, C. The Protein Data Bank. Acta Crystallogr. D Biol. Crystallogr., 2002, 58(Pt 6 No 1), 899-907..
[http://dx.doi.org/10.1107/S0907444902003451] [PMID: 12037327]
[37]
Berman, H.; Henrick, K.; Nakamura, H. Announcing the worldwide protein data bank. Nat. Struct. Biol., 2003, 10(12), 980-980.
[http://dx.doi.org/10.1038/nsb1203-980] [PMID: 14634627]
[38]
Brown, D.; Superti-Furga, G. Rediscovering the sweet spot in drug discovery. Drug Discov. Today, 2003, 8(23), 1067-1077.
[http://dx.doi.org/10.1016/S1359-6446(03)02902-7] [PMID: 14693466]
[39]
Patel, M.N.; Halling-Brown, M.D.; Tym, J.E.; Workman, P.; Al-Lazikani, B. Objective assessment of cancer genes for drug discovery. Nat. Rev. Drug Discov., 2013, 12(1), 35-50.
[http://dx.doi.org/10.1038/nrd3913] [PMID: 23274470]
[40]
Futreal, P.A.; Coin, L.; Marshall, M.; Down, T.; Hubbard, T.; Wooster, R.; Rahman, N.; Stratton, M.R. A census of human cancer genes. Nat. Rev. Cancer, 2004, 4(3), 177-183.
[http://dx.doi.org/10.1038/nrc1299] [PMID: 14993899]
[41]
Kozakov, D.; Grove, L.E.; Hall, D.R.; Bohnuud, T.; Mottarella, S.E.; Luo, L.; Xia, B.; Beglov, D.; Vajda, S. The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins. Nat. Protoc., 2015, 10(5), 733-755.
[http://dx.doi.org/10.1038/nprot.2015.043] [PMID: 25855957]
[42]
Miao, Y.; Nichols, S.E.; McCammon, J.A. Mapping of allosteric druggable sites in activation-associated conformers of the M2 muscarinic receptor. Chem. Biol. Drug Des., 2014, 83(2), 237-246.
[http://dx.doi.org/10.1111/cbdd.12233] [PMID: 24112716]
[43]
Oswald, C.; Rappas, M.; Kean, J.; Doré, A.S.; Errey, J.C.; Bennett, K.; Deflorian, F.; Christopher, J.A.; Jazayeri, A.; Mason, J.S.; Congreve, M.; Cooke, R.M.; Marshall, F.H. Intracellular allosteric antagonism of the CCR9 receptor. Nature, 2016, 540(7633), 462-465.
[http://dx.doi.org/10.1038/nature20606] [PMID: 27926729]
[44]
Stornaiuolo, M.; Bruno, A.; Botta, L.; La Regina, G.; Cosconati, S.; Silvestri, R.; Marinelli, L.; Novellino, E. Endogenous vs exogenous allosteric modulators in GPCRs: A dispute for shuttling CB1 among different membrane microenvironments. Sci. Rep., 2015, 5, 15453.
[http://dx.doi.org/10.1038/srep15453] [PMID: 26482099]
[45]
Zhang, D.; Gao, Z-G.; Zhang, K.; Kiselev, E.; Crane, S.; Wang, J.; Paoletta, S.; Yi, C.; Ma, L.; Zhang, W.; Han, G.W.; Liu, H.; Cherezov, V.; Katritch, V.; Jiang, H.; Stevens, R.C.; Jacobson, K.A.; Zhao, Q.; Wu, B. Two disparate ligand-binding sites in the human P2Y1 receptor. Nature, 2015, 520(7547), 317-321.
[http://dx.doi.org/10.1038/nature14287] [PMID: 25822790]
[46]
Zheng, Y.; Qin, L.; Zacarías, N.V.O.; de Vries, H.; Han, G.W.; Gustavsson, M.; Dabros, M.; Zhao, C.; Cherney, R.J.; Carter, P.; Stamos, D.; Abagyan, R.; Cherezov, V.; Stevens, R.C.; IJzerman, A.P.; Heitman, L.H.; Tebben, A.; Kufareva, I.; Handel, T.M. Structure of CC chemokine receptor 2 with orthosteric and allosteric antagonists. Nature, 2016, 540(7633), 458-461.
[http://dx.doi.org/10.1038/nature20605] [PMID: 27926736]
[47]
Kurgan, L.A.; Musilek, P. A survey of knowledge discovery and data mining process models. Knowl. Eng. Rev., 2006, 21, 1.
[http://dx.doi.org/10.1017/S0269888906000737]
[48]
CAS, A Division of the American Chemical Society. https://www.cas.org (Accessed, Jun 30, 2017)
[49]
Benson, D.A.; Karsch-Mizrachi, I.; Lipman, D.J.; Ostell, J.; Wheeler, D.L. GenBank. Nucleic Acids Res., 2008, 36(Database issue), D25-D30.
[PMID: 18073190]
[50]
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]
[51]
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]
[52]
Molecular Descriptors for Chemoinformatics,; Todeschini, R.; Consonni, V., Eds.; Methods and Principles in Medicinal Chemistry;. Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim: Germany, 2009, p. 41..
[53]
Akella, L.B.; DeCaprio, D. Cheminformatics approaches to analyze diversity in compound screening libraries. Curr. Opin. Chem. Biol., 2010, 14(3), 325-330.
[http://dx.doi.org/10.1016/j.cbpa.2010.03.017] [PMID: 20457001]
[54]
Willett, P. Similarity-based virtual screening using 2D fingerprints. Drug Discov. Today, 2006, 11(23-24), 1046-1053.
[http://dx.doi.org/10.1016/j.drudis.2006.10.005] [PMID: 17129822]
[55]
Geppert, H.; Vogt, M.; Bajorath, J. Current trends in ligand-based virtual screening: Molecular representations, data mining methods, new application areas, and performance evaluation. J. Chem. Inf. Model., 2010, 50(2), 205-216.
[http://dx.doi.org/10.1021/ci900419k] [PMID: 20088575]
[56]
Al Khalifa, A.; Haranczyk, M.; Holliday, J. Comparison of nonbinary similarity coefficients for similarity searching, clustering and compound selection. J. Chem. Inf. Model., 2009, 49(5), 1193-1201.
[http://dx.doi.org/10.1021/ci8004644] [PMID: 19405526]
[57]
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]
[58]
Kawabata, T. Build-up algorithm for atomic correspondence between chemical structures. J. Chem. Inf. Model., 2011, 51(8), 1775-1787.
[http://dx.doi.org/10.1021/ci2001023] [PMID: 21736325]
[59]
Matters, M. Mechanism matters. Nat. Med., 2010, 16(4), 347-347.
[http://dx.doi.org/10.1038/nm0410-347] [PMID: 20376007]
[60]
Gora-Tybor, J.; Robak, T. Targeted drugs in chronic myeloid leukemia. Curr. Med. Chem., 2008, 15(29), 3036-3051.
[http://dx.doi.org/10.2174/092986708786848578] [PMID: 19075651]
[61]
Tolomeo, M.; Dieli, F.; Gebbia, N.; Simoni, D. Tyrosine kinase inhibitors for the treatment of chronic myeloid leukemia. Anticancer. Agents Med. Chem., 2009, 9(8), 853-863.
[http://dx.doi.org/10.2174/187152009789124637] [PMID: 19538165]
[62]
Lee, T-S.; Potts, S.J.; Kantarjian, H.; Cortes, J.; Giles, F.; Albitar, M. Molecular basis explanation for imatinib resistance of BCR-ABL due to T315I and P-loop mutations from molecular dynamics simulations. Cancer, 2008, 112(8), 1744-1753.
[http://dx.doi.org/10.1002/cncr.23355] [PMID: 18338744]
[63]
Schenone, S.; Bruno, O.; Radi, M.; Botta, M. New insights into small-molecule inhibitors of Bcr-Abl. Med. Res. Rev., 2011, 31(1), 1-41.
[http://dx.doi.org/10.1002/med.20175] [PMID: 19714578]
[64]
Xie, L.; Xie, L.; Bourne, P.E. Structure-based systems biology for analyzing off-target binding. Curr. Opin. Struct. Biol., 2011, 21(2), 189-199.
[http://dx.doi.org/10.1016/j.sbi.2011.01.004] [PMID: 21292475]
[65]
Ilyin, V.A.; Abyzov, A.; Leslin, C.M. Structural alignment of proteins by a novel TOPOFIT method, as a superimposition of common volumes at a topomax point. Protein Sci., 2004, 13(7), 1865-1874.
[http://dx.doi.org/10.1110/ps.04672604] [PMID: 15215530]
[66]
Dias, R.; de Azevedo, W.F. Jr Molecular docking algorithms. Curr. Drug Targets, 2008, 9(12), 1040-1047.
[http://dx.doi.org/10.2174/138945008786949432] [PMID: 19128213]
[67]
Wolber, G.; Langer, T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J. Chem. Inf. Model., 2005, 45(1), 160-169.
[http://dx.doi.org/10.1021/ci049885e] [PMID: 15667141]
[68]
Chen, J.; Lai, L. Pocket v.2: Further developments on receptor-based pharmacophore modeling. J. Chem. Inf. Model., 2006, 46(6), 2684-2691.
[http://dx.doi.org/10.1021/ci600246s] [PMID: 17125208]
[69]
Tintori, C.; Corradi, V.; Magnani, M.; Manetti, F.; Botta, M. Targets looking for drugs: A multistep computational protocol for the development of structure-based pharmacophores and their applications for hit discovery. J. Chem. Inf. Model., 2008, 48(11), 2166-2179.
[http://dx.doi.org/10.1021/ci800105p] [PMID: 18942779]
[70]
Jazayeri, A.; Andrews, S.P.; Marshall, F.H. Structurally enabled discovery of adenosine A2A receptor antagonists. Chem. Rev., 2017, 117(1), 21-37.
[http://dx.doi.org/10.1021/acs.chemrev.6b00119] [PMID: 27333206]
[71]
Manglik, A.; Lin, H.; Aryal, D.K.; McCorvy, J.D.; Dengler, D.; Corder, G.; Levit, A.; Kling, R.C.; Bernat, V.; Hübner, H.; Huang, X-P.; Sassano, M.F.; Giguère, P.M.; Löber, S. Da Duan; Scherrer, G.; Kobilka, B.K.; Gmeiner, P.; Roth, B.L.; Shoichet, B.K. Structure-based discovery of opioid analgesics with reduced side effects. Nature, 2016, 537(7619), 185-190.
[http://dx.doi.org/10.1038/nature19112] [PMID: 27533032]
[72]
Paoletta, S.; Sabbadin, D.; von Kügelgen, I.; Hinz, S.; Katritch, V.; Hoffmann, K.; Abdelrahman, A.; Straßburger, J.; Baqi, Y.; Zhao, Q.; Stevens, R.C.; Moro, S.; Müller, C.E.; Jacobson, K.A. Modeling ligand recognition at the P2Y12 receptor in light of X-ray structural information. J. Comput. Aided Mol. Des., 2015, 29(8), 737-756.
[http://dx.doi.org/10.1007/s10822-015-9858-z] [PMID: 26194851]
[73]
Preti, D.; Baraldi, P.G.; Saponaro, G.; Romagnoli, R.; Aghazadeh Tabrizi, M.; Baraldi, S.; Cosconati, S.; Bruno, A.; Novellino, E.; Vincenzi, F.; Ravani, A.; Borea, P.A.; Varani, K. Design, synthesis, and biological evaluation of novel 2-((2-(4-(substituted)phenylpiperazin-1-yl)ethyl)amino)-5′-N-ethylcarboxamidoadenosines as potent and selective agonists of the A2A adenosine receptor. J. Med. Chem., 2015, 58(7), 3253-3267.
[http://dx.doi.org/10.1021/acs.jmedchem.5b00215] [PMID: 25780876]
[74]
Cozzini, P.; Kellogg, G.E.; Spyrakis, F.; Abraham, D.J.; Costantino, G.; Emerson, A.; Fanelli, F.; Gohlke, H.; Kuhn, L.A.; Morris, G.M.; Orozco, M.; Pertinhez, T.A.; Rizzi, M.; Sotriffer, C.A. Target flexibility: An emerging consideration in drug discovery and design. J. Med. Chem., 2008, 51(20), 6237-6255.
[http://dx.doi.org/10.1021/jm800562d] [PMID: 18785728]
[75]
Totrov, M.; Abagyan, R. Flexible ligand docking to multiple receptor conformations: A practical alternative. Curr. Opin. Struct. Biol., 2008, 18(2), 178-184.
[http://dx.doi.org/10.1016/j.sbi.2008.01.004] [PMID: 18302984]
[76]
Korb, O.; Olsson, T.S.G.; Bowden, S.J.; Hall, R.J.; Verdonk, M.L.; Liebeschuetz, J.W.; Cole, J.C. Potential and limitations of ensemble docking. J. Chem. Inf. Model., 2012, 52(5), 1262-1274.
[http://dx.doi.org/10.1021/ci2005934] [PMID: 22482774]
[77]
Nabuurs, S.B.; Wagener, M.; de Vlieg, J. A flexible approach to induced fit docking. J. Med. Chem., 2007, 50(26), 6507-6518.
[http://dx.doi.org/10.1021/jm070593p] [PMID: 18031000]
[78]
Sherman, W.; Beard, H.S.; Farid, R. Use of an induced fit receptor structure in virtual screening. Chem. Biol. Drug Des., 2006, 67(1), 83-84.
[http://dx.doi.org/10.1111/j.1747-0285.2005.00327.x] [PMID: 16492153]
[79]
Sherman, W.; Day, T.; Jacobson, M.P.; Friesner, R.A.; Farid, R. Novel procedure for modeling ligand/receptor induced fit effects. J. Med. Chem., 2006, 49(2), 534-553.
[http://dx.doi.org/10.1021/jm050540c] [PMID: 16420040]
[80]
De Vivo, M.; Masetti, M.; Bottegoni, G.; Cavalli, A. Role of molecular dynamics and related methods in drug discovery. J. Med. Chem., 2016, 59(9), 4035-4061.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01684] [PMID: 26807648]
[81]
Doerr, S.; Harvey, M.J.; Noé, F.; De Fabritiis, G. HTMD: High-Throughput Molecular Dynamics for Molecular Discovery. J. Chem. Theory Comput., 2016, 12(4), 1845-1852.
[http://dx.doi.org/10.1021/acs.jctc.6b00049] [PMID: 26949976]
[82]
Klepeis, J.L.; Lindorff-Larsen, K.; Dror, R.O.; Shaw, D.E. Long-timescale molecular dynamics simulations of protein structure and function. Curr. Opin. Struct. Biol., 2009, 19(2), 120-127.
[http://dx.doi.org/10.1016/j.sbi.2009.03.004] [PMID: 19361980]
[83]
Martínez-Rosell, G.; Giorgino, T.; Harvey, M.J.; de Fabritiis, G. Drug discovery and molecular dynamics: Methods, applications and perspective beyond the second timescale. Curr. Top. Med. Chem., 2017, 17(23), 2617-2625.
[http://dx.doi.org/10.2174/1568026617666170414142549] [PMID: 28413955]
[84]
Chothia, C.; Lesk, A.M. The relation between the divergence of sequence and structure in proteins. EMBO J., 1986, 5(4), 823-826.
[http://dx.doi.org/10.1002/j.1460-2075.1986.tb04288.x] [PMID: 3709526]
[85]
Costanzi, S.; Modeling, G. Modeling G protein-coupled receptors and their interactions with ligands. Curr. Opin. Struct. Biol., 2013, 23(2), 185-190.
[http://dx.doi.org/10.1016/j.sbi.2013.01.008] [PMID: 23415855]
[86]
Isberg, V.; de Graaf, C.; Bortolato, A.; Cherezov, V.; Katritch, V.; Marshall, F.H.; Mordalski, S.; Pin, J-P.; Stevens, R.C.; Vriend, G.; Gloriam, D.E. Generic GPCR residue numbers - aligning topology maps while minding the gaps. Trends Pharmacol. Sci., 2015, 36(1), 22-31.
[http://dx.doi.org/10.1016/j.tips.2014.11.001] [PMID: 25541108]
[87]
Yarnitzky, T.; Levit, A.; Niv, M.Y. Homology modeling of G-protein-coupled receptors with X-ray structures on the rise. Curr. Opin. Drug Discov. Devel., 2010, 13(3), 317-325.
[PMID: 20443165]
[88]
Gedeck, P.; Lewis, R.A. Exploiting QSAR models in lead optimization. Curr. Opin. Drug Discov. Devel., 2008, 11(4), 569-575.
[PMID: 18600573]
[89]
Walters, W.P.; Stahl, M.T.; Murcko, M.A. Virtual screening—an overview. Drug Discov. Today, 1998, 3, 160-178.
[http://dx.doi.org/10.1016/S1359-6446(97)01163-X]
[90]
Schneider, G.; Fechner, U. Computer-based de novo design of drug-like molecules. Nat. Rev. Drug Discov., 2005, 4(8), 649-663.
[http://dx.doi.org/10.1038/nrd1799] [PMID: 16056391]
[91]
Hartenfeller, M.; Proschak, E.; Schüller, A.; Schneider, G. Concept of combinatorial de novo design of drug-like molecules by particle swarm optimization. Chem. Biol. Drug Des., 2008, 72(1), 16-26.
[http://dx.doi.org/10.1111/j.1747-0285.2008.00672.x] [PMID: 18564216]
[92]
Schneider, G. Trends in virtual combinatorial library design. Curr. Med. Chem., 2002, 9(23), 2095-2101.
[http://dx.doi.org/10.2174/0929867023368755] [PMID: 12470249]
[93]
Schüller, A.; Hähnke, V.; Schneider, G. SmiLib v2.0: A Java-Based Tool for Rapid Combinatorial Library Enumeration. QSAR Comb. Sci., 2007, 26, 407-410.
[http://dx.doi.org/10.1002/qsar.200630101]
[94]
Reymond, J-L.; Ruddigkeit, L.; Blum, L.; van Deursen, R. The Enumeration of Chemical Space. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2012, 2, 717-733.
[http://dx.doi.org/10.1002/wcms.1104]
[95]
Leach, A.R.; Hann, M.M. The in silico world of virtual libraries. Drug Discov. Today, 2000, 5(8), 326-336.
[http://dx.doi.org/10.1016/S1359-6446(00)01516-6] [PMID: 10893546]
[96]
Vincetti, P.; Caporuscio, F.; Kaptein, S.; Gioiello, A.; Mancino, V.; Suzuki, Y.; Yamamoto, N.; Crespan, E.; Lossani, A.; Maga, G.; Rastelli, G.; Castagnolo, D.; Neyts, J.; Leyssen, P.; Costantino, G.; Radi, M. Discovery of multitarget antivirals acting on both the dengue virus NS5-NS3 interaction and the host Src/Fyn kinases. J. Med. Chem., 2015, 58(12), 4964-4975.
[http://dx.doi.org/10.1021/acs.jmedchem.5b00108] [PMID: 26039671]
[97]
Maga, G.; Falchi, F.; Radi, M.; Botta, L.; Casaluce, G.; Bernardini, M.; Irannejad, H.; Manetti, F.; Garbelli, A.; Samuele, A.; Zanoli, S.; Esté, J.A.; Gonzalez, E.; Zucca, E.; Paolucci, S.; Baldanti, F.; De Rijck, J.; Debyser, Z.; Botta, M. Toward the discovery of novel anti-HIV drugs. Second-generation inhibitors of the cellular ATPase DDX3 with improved anti-HIV activity: Synthesis, structure-activity relationship analysis, cytotoxicity studies, and target validation. ChemMedChem, 2011, 6(8), 1371-1389.
[http://dx.doi.org/10.1002/cmdc.201100166] [PMID: 21698775]
[98]
Siegal, G.A.B. E.; Schultz, J. Integration of Fragment Screening and Library Design. Drug Discov. Today, 2007, 12, 1032-1039.
[http://dx.doi.org/10.1016/j.drudis.2007.08.005] [PMID: 18061882]
[99]
Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 2001, 46(1-3), 3-26.
[http://dx.doi.org/10.1016/S0169-409X(00)00129-0] [PMID: 11259830]
[100]
Congreve, M.; Carr, R.; Murray, C.; Jhoti, H.A. ‘rule of three’ for fragment-based lead discovery? Drug Discov. Today, 2003, 8(19), 876-877.
[http://dx.doi.org/10.1016/S1359-6446(03)02831-9] [PMID: 14554012]
[101]
Bickerton, G.R.; Paolini, G.V.; Besnard, J.; Muresan, S.; Hopkins, A.L. Quantifying the chemical beauty of drugs. Nat. Chem., 2012, 4(2), 90-98.
[http://dx.doi.org/10.1038/nchem.1243] [PMID: 22270643]
[102]
Blagg, J. Structure-activity relationships for in vitro and in vivo toxicity. Annu. Rep. Med. Chem., 2006, 41, 353-368.
[http://dx.doi.org/10.1016/S0065-7743(06)41024-1]
[103]
Hughes, J.D.; Blagg, J.; Price, D.A.; Bailey, S.; Decrescenzo, G.A.; Devraj, R.V.; Ellsworth, E.; Fobian, Y.M.; Gibbs, M.E.; Gilles, R.W.; Greene, N.; Huang, E.; Krieger-Burke, T.; Loesel, J.; Wager, T.; Whiteley, L.; Zhang, Y. Physiochemical drug properties associated with in vivo toxicological outcomes. Bioorg. Med. Chem. Lett., 2008, 18(17), 4872-4875.
[http://dx.doi.org/10.1016/j.bmcl.2008.07.071] [PMID: 18691886]
[104]
Baell, J.B.; Holloway, G.A. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J. Med. Chem., 2010, 53(7), 2719-2740.
[http://dx.doi.org/10.1021/jm901137j] [PMID: 20131845]
[105]
Metz, J.T.; Huth, J.R.; Hajduk, P.J. Enhancement of chemical rules for predicting compound reactivity towards protein thiol groups. J. Comput. Aided Mol. Des., 2007, 21(1-3), 139-144.
[http://dx.doi.org/10.1007/s10822-007-9109-z] [PMID: 17340041]
[106]
Schuffenhauer, A.; Popov, M.; Schopfer, U.; Acklin, P.; Stanek, J.; Jacoby, E. Molecular diversity management strategies for building and enhancement of diverse and focused lead discovery compound screening collections. Comb. Chem. High Throughput Screen., 2004, 7(8), 771-781.
[http://dx.doi.org/10.2174/1386207043328238] [PMID: 15578939]
[107]
Truchon, J-F.; Bayly, C.I. Evaluating virtual screening methods: Good and bad metrics for the “early recognition” problem. J. Chem. Inf. Model., 2007, 47(2), 488-508.
[http://dx.doi.org/10.1021/ci600426e] [PMID: 17288412]
[108]
Cosconati, S.; Forli, S.; Perryman, A.L.; Harris, R.; Goodsell, D.S.; Olson, A.J. Virtual screening with autodock: Theory and practice. Expert Opin. Drug Discov., 2010, 5(6), 597-607.
[http://dx.doi.org/10.1517/17460441.2010.484460] [PMID: 21532931]
[109]
Forli, S.; Huey, R.; Pique, M.E.; Sanner, M.F.; Goodsell, D.S.; Olson, A.J. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc., 2016, 11(5), 905-919.
[http://dx.doi.org/10.1038/nprot.2016.051] [PMID: 27077332]
[110]
Grubmüller, H.; Heymann, B.; Tavan, P. Ligand binding: Molecular mechanics calculation of the streptavidin-biotin rupture force. Science, 1996, 271(5251), 997-999.
[http://dx.doi.org/10.1126/science.271.5251.997] [PMID: 8584939]
[111]
Torrie, G.M.; Valleau, J.P. Nonphysical sampling distributions in monte carlo free-energy estimation: Umbrella sampling. J. Comput. Phys., 1977, 23, 187-199.
[http://dx.doi.org/10.1016/0021-9991(77)90121-8]
[112]
Laio, A.; Parrinello, M. Escaping free-energy minima. Proc. Natl. Acad. Sci. USA, 2002, 99(20), 12562-12566.
[http://dx.doi.org/10.1073/pnas.202427399] [PMID: 12271136]
[113]
Wang, L.; Wu, Y.; Deng, Y.; Kim, B.; Pierce, L.; Krilov, G.; Lupyan, D.; Robinson, S.; Dahlgren, M.K.; Greenwood, J.; Romero, D.L.; Masse, C.; Knight, J.L.; Steinbrecher, T.; Beuming, T.; Damm, W.; Harder, E.; Sherman, W.; Brewer, M.; Wester, R.; Murcko, M.; Frye, L.; Farid, R.; Lin, T.; Mobley, D.L.; Jorgensen, W.L.; Berne, B.J.; Friesner, R.A.; Abel, R. Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J. Am. Chem. Soc., 2015, 137(7), 2695-2703.
[http://dx.doi.org/10.1021/ja512751q] [PMID: 25625324]
[114]
Schneider, null; Neidhart, null; Giller, null; Schmid, null. “Scaffold-Hopping” by Topological Pharmacophore Search: A Contribution to Virtual Screening. Angew. Chem. Int. Ed. Engl., 1999, 38, 2894-2896.
[http://dx.doi.org/10.1002/(SICI)1521-3773(19991004)38:19<2894:AID-ANIE2894>3.0.CO;2-F]
[115]
De Vivo, M.; Cavalli, A. Recent Advances in Dynamic Docking for Drug Discovery. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2017.e1320
[http://dx.doi.org/10.1002/wcms.1320]
[116]
Warshel, A.; Dryga, A. Simulating electrostatic energies in proteins: Perspectives and some recent studies of pKas, redox, and other crucial functional properties. Proteins, 2011, 79(12), 3469-3484.
[http://dx.doi.org/10.1002/prot.23125] [PMID: 21910139]
[117]
Warshel, A.; Sharma, P.K.; Kato, M.; Parson, W.W. Modeling electrostatic effects in proteins. Biochim. Biophys. Acta, 2006, 1764(11), 1647-1676.
[http://dx.doi.org/10.1016/j.bbapap.2006.08.007] [PMID: 17049320]
[118]
Bortolato, A.; Deflorian, F.; Weiss, D.R.; Mason, J.S. Decoding the role of water dynamics in ligand-protein unbinding: CRF1R as a test case. J. Chem. Inf. Model., 2015, 55(9), 1857-1866.
[http://dx.doi.org/10.1021/acs.jcim.5b00440] [PMID: 26335976]
[119]
Bortolato, A.; Tehan, B.G.; Bodnarchuk, M.S.; Essex, J.W.; Mason, J.S. Water network perturbation in ligand binding: Adenosine A(2A) antagonists as a case study. J. Chem. Inf. Model., 2013, 53(7), 1700-1713.
[http://dx.doi.org/10.1021/ci4001458] [PMID: 23725291]
[120]
van Vlijmen, H.; Desjarlais, R.L.; Mirzadegan, T. Computational chemistry at Janssen. J. Comput. Aided Mol. Des., 2017, 31(3), 267-273.
[http://dx.doi.org/10.1007/s10822-016-9998-9] [PMID: 27995515]
[121]
Kitchen, D.B. Computer-aided drug discovery research at a global contract research organization. J. Comput. Aided Mol. Des., 2017, 31(3), 309-318.
[http://dx.doi.org/10.1007/s10822-016-9991-3] [PMID: 27804014]
[122]
Tummino, P.J.; Copeland, R.A. Residence time of receptor-ligand complexes and its effect on biological function. Biochemistry, 2008, 47(20), 5481-5492.
[http://dx.doi.org/10.1021/bi8002023] [PMID: 18412369]
[123]
Jarzynski, C. Nonequilibrium equality for free energy differences. Phys. Rev. Lett., 1997, 78, 2690-2693.
[http://dx.doi.org/10.1103/PhysRevLett.78.2690]
[124]
Kumar, S.; Rosenberg, J.M.; Bouzida, D.; Swendsen, R.H.; Kollman, P.A. The weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J. Comput. Chem., 1992, 13, 1011-1021.
[http://dx.doi.org/10.1002/jcc.540130812]
[125]
Valsson, O.; Tiwary, P.; Parrinello, M. Enhancing important fluctuations: Rare events and metadynamics from a conceptual viewpoint. Annu. Rev. Phys. Chem., 2016, 67, 159-184.
[http://dx.doi.org/10.1146/annurev-physchem-040215-112229] [PMID: 26980304]
[126]
Buch, I.; Giorgino, T.; De Fabritiis, G. Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations. Proc. Natl. Acad. Sci. USA, 2011, 108(25), 10184-10189.
[http://dx.doi.org/10.1073/pnas.1103547108] [PMID: 21646537]
[127]
Capelli, A.M.; Bruno, A.; Entrena Guadix, A.; Costantino, G. Unbinding pathways from the glucocorticoid receptor shed light on the reduced sensitivity of glucocorticoid ligands to a naturally occurring, clinically relevant mutant receptor. J. Med. Chem., 2013, 56(17), 7003-7014.
[http://dx.doi.org/10.1021/jm400802b] [PMID: 23927519]
[128]
Decherchi, S.; Berteotti, A.; Bottegoni, G.; Rocchia, W.; Cavalli, A. The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning. Nat. Commun., 2015, 6, 6155.
[http://dx.doi.org/10.1038/ncomms7155] [PMID: 25625196]
[129]
Dickson, A.; Tiwary, P.; Vashisth, H. Kinetics of ligand binding through advanced computational approaches: A. Curr. review Top. Med. Chem., 2017, 17(23), 2626-2641.
[http://dx.doi.org/10.2174/1568026617666170414142908] [PMID: 28413946]
[130]
Dror, R.O.; Pan, A.C.; Arlow, D.H.; Borhani, D.W.; Maragakis, P.; Shan, Y.; Xu, H.; Shaw, D.E. Pathway and mechanism of drug binding to G-protein-coupled receptors. Proc. Natl. Acad. Sci. USA, 2011, 108(32), 13118-13123.
[http://dx.doi.org/10.1073/pnas.1104614108] [PMID: 21778406]
[131]
Ferruz, N.; De Fabritiis, G. Binding kinetics in drug discovery. Mol. Inform., 2016, 35(6-7), 216-226.
[http://dx.doi.org/10.1002/minf.201501018] [PMID: 27492236]
[132]
Limongelli, V.; Bonomi, M.; Parrinello, M. Funnel metadynamics as accurate binding free-energy method. Proc. Natl. Acad. Sci. USA, 2013, 110(16), 6358-6363.
[http://dx.doi.org/10.1073/pnas.1303186110] [PMID: 23553839]
[133]
Mollica, L.; Theret, I.; Antoine, M.; Perron-Sierra, F.; Charton, Y.; Fourquez, J-M.; Wierzbicki, M.; Boutin, J.A.; Ferry, G.; Decherchi, S.; Bottegoni, G.; Ducrot, P.; Cavalli, A. Molecular dynamics simulations and kinetic measurements to estimate and predict protein-ligand residence times. J. Med. Chem., 2016, 59(15), 7167-7176.
[http://dx.doi.org/10.1021/acs.jmedchem.6b00632] [PMID: 27391254]
[134]
Sabbadin, D.; Moro, S. Supervised molecular dynamics (SuMD) as a helpful tool to depict GPCR-ligand recognition pathway in a nanosecond time scale. J. Chem. Inf. Model., 2014, 54(2), 372-376.
[http://dx.doi.org/10.1021/ci400766b] [PMID: 24456045]
[135]
Salvalaglio, M.; Tiwary, P.; Parrinello, M. Assessing the reliability of the dynamics reconstructed from metadynamics. J. Chem. Theory Comput., 2014, 10(4), 1420-1425.
[http://dx.doi.org/10.1021/ct500040r] [PMID: 26580360]
[136]
Shan, Y.; Kim, E.T.; Eastwood, M.P.; Dror, R.O.; Seeliger, M.A.; Shaw, D.E. How does a drug molecule find its target binding site? J. Am. Chem. Soc., 2011, 133(24), 9181-9183.
[http://dx.doi.org/10.1021/ja202726y] [PMID: 21545110]
[137]
Tiwary, P.; Limongelli, V.; Salvalaglio, M.; Parrinello, M. Kinetics of protein-ligand unbinding: Predicting pathways, rates, and rate-limiting steps. Proc. Natl. Acad. Sci. USA, 2015, 112(5), E386-E391.
[http://dx.doi.org/10.1073/pnas.1424461112] [PMID: 25605901]
[138]
Tiwary, P.; Parrinello, M. From metadynamics to dynamics. Phys. Rev. Lett., 2013, 111(23)230602
[http://dx.doi.org/10.1103/PhysRevLett.111.230602] [PMID: 24476246]
[139]
Hu, Y.; Stumpfe, D.; Bajorath, J. Computational exploration of molecular scaffolds in medicinal chemistry. J. Med. Chem., 2016, 59(9), 4062-4076.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01746] [PMID: 26840095]
[140]
Maggiora, G.; Vogt, M.; Stumpfe, D.; Bajorath, J. Molecular similarity in medicinal chemistry. J. Med. Chem., 2014, 57(8), 3186-3204.
[http://dx.doi.org/10.1021/jm401411z] [PMID: 24151987]
[141]
Hu, Y.; Stumpfe, D.; Bajorath, J. Recent Advances in Scaffold Hopping. J. Med. Chem., 2017, 60(4), 1238-1246.
[http://dx.doi.org/10.1021/acs.jmedchem.6b01437] [PMID: 28001064]
[142]
Ertl, P.; Lewis, R. IADE: A system for intelligent automatic design of bioisosteric analogs. J. Comput. Aided Mol. Des., 2012, 26(11), 1207-1215.
[http://dx.doi.org/10.1007/s10822-012-9609-3] [PMID: 23053736]
[143]
Patel, S.; Harris, S.F.; Gibbons, P.; Deshmukh, G.; Gustafson, A.; Kellar, T.; Lin, H.; Liu, X.; Liu, Y.; Liu, Y.; Ma, C.; Scearce-Levie, K.; Ghosh, A.S.; Shin, Y.G.; Solanoy, H.; Wang, J.; Wang, B.; Yin, J.; Siu, M.; Lewcock, J.W. Scaffold-Hopping and Structure-Based Discovery of Potent, Selective, And Brain Penetrant N-(1H-Pyrazol-3-yl)pyridin-2-amine Inhibitors of Dual Leucine Zipper Kinase (DLK, MAP3K12). J. Med. Chem., 2015, 58(20), 8182-8199.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01072] [PMID: 26431428]
[144]
Ratni, H.; Rogers-Evans, M.; Bissantz, C.; Grundschober, C.; Moreau, J-L.; Schuler, F.; Fischer, H.; Alvarez Sanchez, R.; Schnider, P. Discovery of highly selective brain-penetrant vasopressin 1a antagonists for the potential treatment of autism via a chemogenomic and scaffold hopping approach. J. Med. Chem., 2015, 58(5), 2275-2289.
[http://dx.doi.org/10.1021/jm501745f] [PMID: 25654260]
[145]
Frushicheva, M.P.; Mills, M.J.L.; Schopf, P.; Singh, M.K.; Prasad, R.B.; Warshel, A. Computer aided enzyme design and catalytic concepts. Curr. Opin. Chem. Biol., 2014, 21, 56-62.
[http://dx.doi.org/10.1016/j.cbpa.2014.03.022] [PMID: 24814389]
[146]
van der Kamp, M.W.; Mulholland, A. J. Computational enzymology: Insight into biological catalysts from modelling. Nat. Prod. Rep., 2008, 25(6), 1001-1014.
[http://dx.doi.org/10.1039/b600517a] [PMID: 19030602]
[147]
Car, R.; Parrinello, M. Unified approach for molecular dynamics and density-functional theory. Phys. Rev. Lett., 1985, 55(22), 2471-2474.
[http://dx.doi.org/10.1103/PhysRevLett.55.2471] [PMID: 10032153]
[148]
Warshel, A.; Levitt, M. Theoretical studies of enzymic reactions: Dielectric, electrostatic and steric stabilization of the carbonium ion in the reaction of lysozyme. J. Mol. Biol., 1976, 103(2), 227-249.
[http://dx.doi.org/10.1016/0022-2836(76)90311-9] [PMID: 985660]
[149]
Warshel, A.; Weiss, R.M. Empirical valence bond calculations of enzyme catalysis. Ann. N. Y. Acad. Sci., 1981, 367, 370-382.
[http://dx.doi.org/10.1111/j.1749-6632.1981.tb50579.x] [PMID: 6942759]
[150]
Adamczyk, A.J.; Cao, J.; Kamerlin, S.C.L.; Warshel, A. Catalysis by dihydrofolate reductase and other enzymes arises from electrostatic preorganization, not conformational motions. Proc. Natl. Acad. Sci. USA, 2011, 108(34), 14115-14120.
[http://dx.doi.org/10.1073/pnas.1111252108] [PMID: 21831831]
[151]
Olsson, M.H.M.; Siegbahn, P.E.M.; Blomberg, M.R.A.; Warshel, A. Exploring pathways and barriers for coupled ET/PT in cytochrome c oxidase: A general framework for examining energetics and mechanistic alternatives. Biochim. Biophys. Acta, 2007, 1767(3), 244-260.
[http://dx.doi.org/10.1016/j.bbabio.2007.01.015] [PMID: 17350588]
[152]
Frushicheva, M.P.; Cao, J.; Warshel, A. Challenges and advances in validating enzyme design proposals: The case of kemp eliminase catalysis. Biochemistry, 2011, 50(18), 3849-3858.
[http://dx.doi.org/10.1021/bi200063a] [PMID: 21443179]
[153]
Poberžnik, M.; Purg, M.; Repič, M.; Mavri, J.; Vianello, R. Empirical valence bond simulations of the hydride-transfer step in the monoamine oxidase A catalyzed metabolism of noradrenaline. J. Phys. Chem. B, 2016, 120(44), 11419-11427.
[http://dx.doi.org/10.1021/acs.jpcb.6b09011] [PMID: 27734680]
[154]
Segall, M.D.; Yusof, I.; Champness, E.J. Avoiding Missed Opportunities by Analyzing the Sensitivity of Our Decisions. J. Med. Chem., 2016, 59(9), 4267-4277.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01921] [PMID: 26901568]
[155]
Sheppard, G.S.; Wang, J.; Kawai, M.; Fidanze, S.D. BaMaung, N.Y.; Erickson, S.A.; Barnes, D.M.; Tedrow, J.S.; Kolaczkowski, L.; Vasudevan, A.; Park, D.C.; Wang, G.T.; Sanders, W.J.; Mantei, R.A.; Palazzo, F.; Tucker-Garcia, L.; Lou, P.; Zhang, Q.; Park, C.H.; Kim, K.H.; Petros, A.; Olejniczak, E.; Nettesheim, D.; Hajduk, P.; Henkin, J.; Lesniewski, R.; Davidsen, S.K.; Bell, R.L. Discovery and optimization of anthranilic acid sulfonamides as inhibitors of methionine aminopeptidase-2: A structural basis for the reduction of albumin binding. J. Med. Chem., 2006, 49(13), 3832-3849.
[http://dx.doi.org/10.1021/jm0601001] [PMID: 16789740]
[156]
Wendt, M.D.; Shen, W.; Kunzer, A.; McClellan, W.J.; Bruncko, M.; Oost, T.K.; Ding, H.; Joseph, M.K.; Zhang, H.; Nimmer, P.M.; Ng, S-C.; Shoemaker, A.R.; Petros, A.M.; Oleksijew, A.; Marsh, K.; Bauch, J.; Oltersdorf, T.; Belli, B.A.; Martineau, D.; Fesik, S.W.; Rosenberg, S.H.; Elmore, S.W. Discovery and structure-activity relationship of antagonists of B-cell lymphoma 2 family proteins with chemopotentiation activity in vitro and in vivo. J. Med. Chem., 2006, 49(3), 1165-1181.
[http://dx.doi.org/10.1021/jm050754u] [PMID: 16451081]
[157]
Schönfeld, D.L.; Ravelli, R.B.G.; Mueller, U.; Skerra, A. The 1.8-A crystal structure of alpha1-acid glycoprotein (Orosomucoid) solved by UV RIP reveals the broad drug-binding activity of this human plasma lipocalin. J. Mol. Biol., 2008, 384(2), 393-405.
[http://dx.doi.org/10.1016/j.jmb.2008.09.020] [PMID: 18823996]
[158]
Genheden, S.; Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov., 2015, 10(5), 449-461.
[http://dx.doi.org/10.1517/17460441.2015.1032936] [PMID: 25835573]
[159]
Liu, H.; Bao, W.; Ding, H.; Jang, J.; Zou, G. Binding modes of flavones to human serum albumin: Insights from experimental and computational studies. J. Phys. Chem. B, 2010, 114(40), 12938-12947.
[http://dx.doi.org/10.1021/jp102053x] [PMID: 20845951]
[160]
Zsila, F.; Bikadi, Z.; Malik, D.; Hari, P.; Pechan, I.; Berces, A.; Hazai, E. Evaluation of drug-human serum albumin binding interactions with support vector machine aided online automated docking. Bioinformatics, 2011, 27(13), 1806-1813.
[http://dx.doi.org/10.1093/bioinformatics/btr284] [PMID: 21593135]
[161]
Lewis, D.F.; Ito, Y. Human cytochromes P450 in the metabolism of drugs: New molecular models of enzyme-substrate interactions. Expert Opin. Drug Metab. Toxicol., 2008, 4(9), 1181-1186.
[http://dx.doi.org/10.1517/17425255.4.9.1181] [PMID: 18721112]
[162]
Brown, C.M.; Reisfeld, B.; Mayeno, A.N. Cytochromes P450: A structure-based summary of biotransformations using representative substrates. Drug Metab. Rev., 2008, 40(1), 1-100.
[http://dx.doi.org/10.1080/03602530701836662] [PMID: 18259985]
[163]
Hsiao, Y-W.; Petersson, C.; Svensson, M.A.; Norinder, U. A pragmatic approach using first-principle methods to address site of metabolism with implications for reactive metabolite formation. J. Chem. Inf. Model., 2012, 52(3), 686-695.
[http://dx.doi.org/10.1021/ci200523f] [PMID: 22299574]
[164]
Sato, K.; Yamazoe, Y. Unimolecular and bimolecular binding system for the prediction of CYP2D6-mediated metabolism. Drug Metab. Dispos., 2012, 40(3), 486-496.
[http://dx.doi.org/10.1124/dmd.111.043125] [PMID: 22159753]
[165]
Zheng, M.; Luo, X.; Shen, Q.; Wang, Y.; Du, Y.; Zhu, W.; Jiang, H. Site of metabolism prediction for six biotransformations mediated by cytochromes P450. Bioinformatics, 2009, 25(10), 1251-1258.
[http://dx.doi.org/10.1093/bioinformatics/btp140] [PMID: 19286831]
[166]
Tarcsay, A.; Kiss, R.; Keseru, G.M. Site of metabolism prediction on cytochrome P450 2C9: A knowledge-based docking approach. J. Comput. Aided Mol. Des., 2010, 24(5), 399-408.
[http://dx.doi.org/10.1007/s10822-010-9347-3] [PMID: 20361237]
[167]
Santos, R.; Hritz, J.; Oostenbrink, C. Role of water in molecular docking simulations of cytochrome P450 2D6. J. Chem. Inf. Model., 2010, 50(1), 146-154.
[http://dx.doi.org/10.1021/ci900293e] [PMID: 19899781]
[168]
Kirchmair, J.; Williamson, M.J.; Tyzack, J.D.; Tan, L.; Bond, P.J.; Bender, A.; Glen, R.C. Computational prediction of metabolism: Sites, products, SAR, P450 enzyme dynamics, and mechanisms. J. Chem. Inf. Model., 2012, 52(3), 617-648.
[http://dx.doi.org/10.1021/ci200542m] [PMID: 22339582]
[169]
Rudik, A.V.; Dmitriev, A.V.; Lagunin, A.A.; Filimonov, D.A.; Poroikov, V.V. Metabolism site prediction based on xenobiotic structural formulas and PASS prediction algorithm. J. Chem. Inf. Model., 2014, 54(2), 498-507.
[http://dx.doi.org/10.1021/ci400472j] [PMID: 24417355]
[170]
Rudik, A.V.; Bezhentsev, V.M.; Dmitriev, A.V.; Druzhilovskiy, D.S.; Lagunin, A.A.; Filimonov, D.A.; Poroikov, V.V. MetaTox: Web application for predicting structure and toxicity of xenobiotics’ metabolites. J. Chem. Inf. Model., 2017, 57(4), 638-642.
[http://dx.doi.org/10.1021/acs.jcim.6b00662] [PMID: 28345905]
[171]
Langowski, J.; Long, A. Computer systems for the prediction of xenobiotic metabolism. Adv. Drug Deliv. Rev., 2002, 54(3), 407-415.
[http://dx.doi.org/10.1016/S0169-409X(02)00011-X] [PMID: 11922955]
[172]
Caspi, R.; Altman, T.; Billington, R.; Dreher, K.; Foerster, H.; Fulcher, C.A.; Holland, T.A.; Keseler, I.M.; Kothari, A.; Kubo, A.; Krummenacker, M.; Latendresse, M.; Mueller, L.A.; Ong, Q.; Paley, S.; Subhraveti, P.; Weaver, D.S.; Weerasinghe, D.; Zhang, P.; Karp, P.D. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res., 2014, 42(Database issue), D459-D471.
[http://dx.doi.org/10.1093/nar/gkt1103] [PMID: 24225315]
[173]
Afzelius, L.; Arnby, C.H.; Broo, A.; Carlsson, L.; Isaksson, C.; Jurva, U.; Kjellander, B.; Kolmodin, K.; Nilsson, K.; Raubacher, F.; Weidolf, L. State-of-the-art tools for computational site of metabolism predictions: Comparative analysis, mechanistical insights, and future applications. Drug Metab. Rev., 2007, 39(1), 61-86.
[http://dx.doi.org/10.1080/03602530600969374] [PMID: 17364881]
[174]
Hammond, T.G.; Carlsson, L.; Davis, A.S.; Lynch, W.G.; MacKenzie, I.; Redfern, W.S.; Sullivan, A.T.; Camm, A.J. Methods of collecting and evaluating non-clinical cardiac electrophysiology data in the pharmaceutical industry: Results of an international survey. Cardiovasc. Res., 2001, 49(4), 741-750.
[http://dx.doi.org/10.1016/S0008-6363(00)00310-2] [PMID: 11230973]
[175]
Mitcheson, J.S.; Chen, J.; Lin, M.; Culberson, C.; Sanguinetti, M.C. A structural basis for drug-induced long QT syndrome. Proc. Natl. Acad. Sci. USA, 2000, 97(22), 12329-12333.
[http://dx.doi.org/10.1073/pnas.210244497] [PMID: 11005845]
[176]
Pearlstein, R.; Vaz, R.; Rampe, D. Understanding the structure-activity relationship of the human ether-a-go-go-related gene cardiac K+ channel. A model for bad behavior. J. Med. Chem., 2003, 46(11), 2017-2022.
[http://dx.doi.org/10.1021/jm0205651] [PMID: 12747773]
[177]
Osterberg, F.; Aqvist, J. Exploring blocker binding to a homology model of the open hERG K+ channel using docking and molecular dynamics methods. FEBS Lett., 2005, 579(13), 2939-2944.
[http://dx.doi.org/10.1016/j.febslet.2005.04.039] [PMID: 15893317]
[178]
Inanobe, A.; Kamiya, N.; Murakami, S.; Fukunishi, Y.; Nakamura, H.; Kurachi, Y. In silico prediction of the chemical block of human ether-a-go-go-related gene (hERG) K+ current. J. Physiol. Sci., 2008, 58(7), 459-470.
[http://dx.doi.org/10.2170/physiolsci.RV011408] [PMID: 19032804]
[179]
Fernandez, D.; Ghanta, A.; Kauffman, G.W.; Sanguinetti, M.C. Physicochemical features of the HERG channel drug binding site. J. Biol. Chem., 2004, 279(11), 10120-10127.
[http://dx.doi.org/10.1074/jbc.M310683200] [PMID: 14699101]
[180]
Durdagi, S.; Duff, H.J.; Noskov, S.Y. Combined receptor and ligand-based approach to the universal pharmacophore model development for studies of drug blockade to the hERG1 pore domain. J. Chem. Inf. Model., 2011, 51(2), 463-474.
[http://dx.doi.org/10.1021/ci100409y] [PMID: 21241063]
[181]
Obiol-Pardo, C.; Gomis-Tena, J.; Sanz, F.; Saiz, J.; Pastor, M. A multiscale simulation system for the prediction of drug-induced cardiotoxicity. J. Chem. Inf. Model., 2011, 51(2), 483-492.
[http://dx.doi.org/10.1021/ci100423z] [PMID: 21250697]
[182]
Wang, W.; MacKinnon, R. Cryo-EM structure of the open human ether-à-go-go-related K+ channel hERG. Cell, 2017, 169(3), 422-430.e10.
[http://dx.doi.org/10.1016/j.cell.2017.03.048] [PMID: 28431243]
[183]
Muegge, I.; Bergner, A.; Kriegl, J.M. Computer-aided drug design at Boehringer Ingelheim. J. Comput. Aided Mol. Des., 2017, 31(3), 275-285.
[http://dx.doi.org/10.1007/s10822-016-9975-3] [PMID: 27650777]
[184]
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]
[185]
Kuhn, B.; Guba, W.; Hert, J.; Banner, D.; Bissantz, C.; Ceccarelli, S.; Haap, W.; Körner, M.; Kuglstatter, A.; Lerner, C.; Mattei, P.; Neidhart, W.; Pinard, E.; Rudolph, M.G.; Schulz-Gasch, T.; Woltering, T.; Stahl, M. A real-world perspective on molecular design. J. Med. Chem., 2016, 59(9), 4087-4102.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01875] [PMID: 26878596]
[186]
Hillisch, A.; Heinrich, N.; Wild, H. Computational chemistry in the pharmaceutical industry: From childhood to adolescence. ChemMedChem, 2015, 10(12), 1958-1962.
[http://dx.doi.org/10.1002/cmdc.201500346] [PMID: 26358802]
[187]
Stahl, M.; Guba, W.; Kansy, M. Integrating molecular design resources within modern drug discovery research: The Roche experience. Drug Discov. Today, 2006, 11(7-8), 326-333.
[http://dx.doi.org/10.1016/j.drudis.2006.02.008] [PMID: 16580974]
[188]
Jazayeri, A.; Dias, J.M.; Marshall, F.H.; From, G. From G protein-coupled receptor structure resolution to rational drug design. J. Biol. Chem., 2015, 290(32), 19489-19495.
[http://dx.doi.org/10.1074/jbc.R115.668251] [PMID: 26100628]
[189]
Furuhashi, M.; Hotamisligil, G.S. Fatty acid-binding proteins: Role in metabolic diseases and potential as drug targets. Nat. Rev. Drug Discov., 2008, 7(6), 489-503.
[http://dx.doi.org/10.1038/nrd2589] [PMID: 18511927]
[190]
Liang, G.; Aldous, S.; Merriman, G.; Levell, J.; Pribish, J.; Cairns, J.; Chen, X.; Maignan, S.; Mathieu, M.; Tsay, J.; Sides, K.; Rebello, S.; Whitely, B.; Morize, I.; Pauls, H.W. Structure-based library design and the discovery of a potent and selective mast cell β-tryptase inhibitor as an oral therapeutic agent. Bioorg. Med. Chem. Lett., 2012, 22(2), 1049-1054.
[http://dx.doi.org/10.1016/j.bmcl.2011.11.119] [PMID: 22192588]
[191]
Maass, P.; Schulz-Gasch, T.; Stahl, M.; Rarey, M. Recore: A fast and versatile method for scaffold hopping based on small molecule crystal structure conformations. J. Chem. Inf. Model., 2007, 47(2), 390-399.
[http://dx.doi.org/10.1021/ci060094h] [PMID: 17305328]
[192]
Lelimousin, M.; Limongelli, V.; Sansom, M.S.P. Conformational changes in the epidermal growth factor receptor: Role of the transmembrane domain investigated by coarse-grained metadynamics free energy calculations. J. Am. Chem. Soc., 2016, 138(33), 10611-10622.
[http://dx.doi.org/10.1021/jacs.6b05602] [PMID: 27459426]
[193]
Picas, L.; Viaud, J.; Schauer, K.; Vanni, S.; Hnia, K.; Fraisier, V.; Roux, A.; Bassereau, P.; Gaits-Iacovoni, F.; Payrastre, B.; Laporte, J.; Manneville, J-B.; Goud, B. BIN1/M-Amphiphysin2 induces clustering of phosphoinositides to recruit its downstream partner dynamin. Nat. Commun., 2014, 5, 5647.
[http://dx.doi.org/10.1038/ncomms6647] [PMID: 25487648]
[194]
Pinot, M.; Vanni, S.; Pagnotta, S.; Lacas-Gervais, S.; Payet, L-A.; Ferreira, T.; Gautier, R.; Goud, B.; Antonny, B.; Barelli, H. Lipid cell biology. Polyunsaturated phospholipids facilitate membrane deformation and fission by endocytic proteins. Science, 2014, 345(6197), 693-697.
[http://dx.doi.org/10.1126/science.1255288] [PMID: 25104391]
[195]
HealthITAnalytics IBM Patents Machine Learning Model for Pharmaceutical Discovery., https://healthitanalytics.com/news/ibm-patents-machine-learning-model-for-pharmaceutical-discovery (Accessed Jun 27, 2017).
[196]
Langedijk, J.; Mantel-Teeuwisse, A.K.; Slijkerman, D.S.; Schutjens, M-H.D.B. Drug repositioning and repurposing: Terminology and definitions in literature. Drug Discov. Today, 2015, 20(8), 1027-1034.
[http://dx.doi.org/10.1016/j.drudis.2015.05.001] [PMID: 25975957]
[197]
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]
[198]
The UniProt Consortium. UniProt: The universal protein knowledgebase. Nucleic Acids Res., 2017, 45(D1), D158-D169.
[http://dx.doi.org/10.1093/nar/gkw1099] [PMID: 27899622]
[199]
Shen, S-Y.; Yang, J.; Yao, A.; Hwang, P-I. Super pairwise alignment (SPA): An efficient approach to global alignment for homologous sequences. J. Comput. Biol., 2002, 9(3), 477-486.
[http://dx.doi.org/10.1089/106652702760138574] [PMID: 12162887]
[200]
Suchard, M.A.; Redelings, B.D. BAli-Phy: Simultaneous Bayesian inference of alignment and phylogeny. Bioinformatics, 2006, 22(16), 2047-2048.
[http://dx.doi.org/10.1093/bioinformatics/btl175] [PMID: 16679334]
[201]
Chenna, R.; Sugawara, H.; Koike, T.; Lopez, R.; Gibson, T.J.; Higgins, D.G.; Thompson, J.D. Multiple sequence alignment with the Clustal series of programs. Nucleic Acids Res., 2003, 31(13), 3497-3500.
[http://dx.doi.org/10.1093/nar/gkg500] [PMID: 12824352]
[202]
Sadreyev, R.I.; Tang, M.; Kim, B-H.; Grishin, N.V. COMPASS server for homology detection: Improved statistical accuracy, speed and functionality. ucleic Acids Res., 2009, 37(Web Server issue), W90-4.
[http://dx.doi.org/10.1093/nar/gkp360] [PMID: 19435884]
[203]
Edgar, R.C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res., 2004, 32(5), 1792-1797.
[http://dx.doi.org/10.1093/nar/gkh340] [PMID: 15034147]
[204]
Notredame, C.; Higgins, D.G.; Heringa, J. T-Coffee: A novel method for fast and accurate multiple sequence alignment. J. Mol. Biol., 2000, 302(1), 205-217.
[http://dx.doi.org/10.1006/jmbi.2000.4042] [PMID: 10964570]
[205]
Zhang, H.; Gao, S.; Lercher, M.J.; Hu, S.; Chen, W-H. EvolView, an online tool for visualizing, annotating and managing phylogenetic trees. Nucleic Acids Res., 2012, 40(Web Server issue), W569-72.
[http://dx.doi.org/10.1093/nar/gks576] [PMID: 22695796]
[206]
Vaughan, T.G. Rapid Browser-Based Visualization for Phylogenetic Trees and Networks, 2017.
[207]
Pethica, R.; Barker, G.; Kovacs, T.; Gough, J. TreeVector: Scalable, interactive, phylogenetic trees for the web. PLoS One, 2010, 5(1)e8934
[http://dx.doi.org/10.1371/journal.pone.0008934] [PMID: 20126613]
[208]
Smits, S.A.; Ouverney, C.C. jsPhyloSVG: A javascript library for visualizing interactive and vector-based phylogenetic trees on the web. PLoS One, 2010, 5(8)e12267
[http://dx.doi.org/10.1371/journal.pone.0012267] [PMID: 20805892]
[209]
Ranwez, V.; Clairon, N.; Delsuc, F.; Pourali, S.; Auberval, N.; Diser, S.; Berry, V. PhyloExplorer: A web server to validate, explore and query phylogenetic trees. BMC Evol. Biol., 2009, 9, 108.
[http://dx.doi.org/10.1186/1471-2148-9-108] [PMID: 19450253]
[210]
Robinson, O.; Dylus, D.; Dessimoz, C. Phylo.io: Interactive viewing and comparison of large phylogenetic trees on the web. Mol. Biol. Evol., 2016, 33(8), 2163-2166.
[http://dx.doi.org/10.1093/molbev/msw080] [PMID: 27189561]
[211]
Boc, A.; Diallo, A.B.; Makarenkov, V. T-REX A web server for inferring, validating and visualizing phylogenetic trees and networks. Nucleic Acids Res., 2012, 40(Web Server issue), W573-9.
[http://dx.doi.org/10.1093/nar/gks485] [PMID: 22675075]
[212]
Chevenet, F.; Brun, C.; Bañuls, A-L.; Jacq, B.; Christen, R. TreeDyn: Towards dynamic graphics and annotations for analyses of trees. BMC Bioinformatics, 2006, 7, 439.
[http://dx.doi.org/10.1186/1471-2105-7-439] [PMID: 17032440]
[213]
Francisco, A.P.; Vaz, C.; Monteiro, P.T.; Melo-Cristino, J.; Ramirez, M.; Carriço, J.A. PHYLOViZ: Phylogenetic inference and data visualization for sequence based typing methods. BMC Bioinformatics, 2012, 13, 87.
[http://dx.doi.org/10.1186/1471-2105-13-87] [PMID: 22568821]
[214]
Zhang, H.; Lund, O.; Nielsen, M. The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: Application to MHC-peptide binding. Bioinformatics, 2009, 25(10), 1293-1299.
[http://dx.doi.org/10.1093/bioinformatics/btp137] [PMID: 19297351]
[215]
Ravindranath, P.A.; Sanner, M.F. AutoSite: An automated approach for pseudo-ligands prediction-from ligand-binding sites identification to predicting key ligand atoms. Bioinformatics, 2016, 32(20), 3142-3149.
[http://dx.doi.org/10.1093/bioinformatics/btw367] [PMID: 27354702]
[216]
Tseng, Y.Y.; Chen, Z.J.; Li, W-H. fPOP: Footprinting functional pockets of proteins by comparative spatial patterns. Nucleic Acids Res., 2010, 38(Database issue), D288-D295.
[http://dx.doi.org/10.1093/nar/gkp900] [PMID: 19880384]
[217]
Schmidtke, P.; Le Guilloux, V.; Maupetit, J.; Tufféry, P. fpocket: Online tools for protein ensemble pocket detection and tracking. Nucleic Acids Res., 2010, 38(Web Server issue), W582-9.
[http://dx.doi.org/10.1093/nar/gkq383] [PMID: 20478829]
[218]
Halgren, T. New method for fast and accurate binding-site identification and analysis. Chem. Biol. Drug Des., 2007, 69(2), 146-148.
[http://dx.doi.org/10.1111/j.1747-0285.2007.00483.x] [PMID: 17381729]
[219]
Frey, J.G.; Bird, C.L. Web-based services for drug design and discovery. Expert Opin. Drug Discov., 2011, 6(9), 885-895.
[http://dx.doi.org/10.1517/17460441.2011.598924] [PMID: 22646212]
[220]
Hussein, H.A.; Borrel, A.; Geneix, C.; Petitjean, M.; Regad, L.; Camproux, A-C. PockDrug-Server: A new web server for predicting pocket druggability on holo and apo proteins. Nucleic Acids Res., 2015, 43(W1)W436-42
[http://dx.doi.org/10.1093/nar/gkv462] [PMID: 25956651]
[221]
Laurie, A.T.R.; Jackson, R.M. Q-SiteFinder: An energy-based method for the prediction of protein-ligand binding sites. Bioinformatics, 2005, 21(9), 1908-1916.
[http://dx.doi.org/10.1093/bioinformatics/bti315] [PMID: 15701681]
[222]
Zhang, Z.; Li, Y.; Lin, B.; Schroeder, M.; Huang, B. Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction. Bioinformatics, 2011, 27(15), 2083-2088.
[http://dx.doi.org/10.1093/bioinformatics/btr331] [PMID: 21636590]
[223]
Wang, R.; Fang, X.; Lu, Y.; Wang, S. The PDBbind database: Collection of binding affinities for protein-ligand complexes with known three-dimensional structures. J. Med. Chem., 2004, 47(12), 2977-2980.
[http://dx.doi.org/10.1021/jm030580l] [PMID: 15163179]
[224]
Sastry, M.; Lowrie, J.F.; Dixon, S.L.; Sherman, W. Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments. J. Chem. Inf. Model., 2010, 50(5), 771-784.
[http://dx.doi.org/10.1021/ci100062n] [PMID: 20450209]
[225]
Tetko, I.V.; Gasteiger, J.; Todeschini, R.; Mauri, A.; Livingstone, D.; Ertl, P.; Palyulin, V.A.; Radchenko, E.V.; Zefirov, N.S.; Makarenko, A.S.; Tanchuk, V.Y.; Prokopenko, V.V. Virtual computational chemistry laboratory--design and description. J. Comput. Aided Mol. Des., 2005, 19(6), 453-463.
[http://dx.doi.org/10.1007/s10822-005-8694-y] [PMID: 16231203]
[226]
Ballabio, D.; Manganaro, A.; Consonni, V.; Mauri, A.; Todeschini, R. Introduction to MOLE DB-on-Line Molecular Descriptors Database. MATCH Commun. Math. Comput. Chem., 2009, 62, 199-207.
[227]
Sushko, I.; Novotarskyi, S.; Körner, R.; Pandey, A.K.; Rupp, M.; Teetz, W.; Brandmaier, S.; Abdelaziz, A.; Prokopenko, V.V.; Tanchuk, V.Y.; Todeschini, R.; Varnek, A.; Marcou, G.; Ertl, P.; Potemkin, V.; Grishina, M.; Gasteiger, J.; Schwab, C.; Baskin, I.I.; Palyulin, V.A.; Radchenko, E.V.; Welsh, W.J.; Kholodovych, V.; Chekmarev, D.; Cherkasov, A.; Aires-de-Sousa, J.; Zhang, Q-Y.; Bender, A.; Nigsch, F.; Patiny, L.; Williams, A.; Tkachenko, V.; Tetko, I.V. Online chemical modeling environment (OCHEM: web platform for data storage, model development and publishing of chemical information. J. Comput. Aided Mol. Des., 2011, 25(6), 533-554.
[http://dx.doi.org/10.1007/s10822-011-9440-2] [PMID: 21660515]
[228]
Ertl, P. Molecular structure input on the web. J. Cheminform., 2010, 2(1), 1.
[http://dx.doi.org/10.1186/1758-2946-2-1] [PMID: 20298528]
[229]
Willighagen, E.L.; Mayfield, J.W.; Alvarsson, J.; Berg, A.; Carlsson, L.; Jeliazkova, N.; Kuhn, S.; Pluskal, T. The chemistry development kit (CDK) v2.0: Atom typing, depiction, molecular formulas, and substructure searching. J. Cheminformatics,, 2017, 9.
[230]
Mauri, A.; Consonni, V.; Pavan, M.; Todeschini, R. Dragon Software: An Easy Approach to Molecular Descriptor Calculations. Match (Mulh.), 2006, 56, 237-248.
[231]
Carosati, E.; Sciabola, S.; Cruciani, G. Hydrogen bonding interactions of covalently bonded fluorine atoms: From crystallographic data to a new angular function in the GRID force field. J. Med. Chem., 2004, 47(21), 5114-5125.
[http://dx.doi.org/10.1021/jm0498349] [PMID: 15456255]
[232]
Rücker, C.; Rücker, G.; Meringer, M. y-Randomization and its variants in QSPR/QSAR. J. Chem. Inf. Model., 2007, 47(6), 2345-2357.
[http://dx.doi.org/10.1021/ci700157b] [PMID: 17880194]
[233]
Yap, C.W. PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem., 2011, 32(7), 1466-1474.
[http://dx.doi.org/10.1002/jcc.21707] [PMID: 21425294]
[234]
Backman, T.W.H.; Cao, Y.; Girke, T. ChemMine tools: An online service for analyzing and clustering small molecules. Nucleic Acids Res., 2011, 39(Web Server issue), W486-91.
[http://dx.doi.org/10.1093/nar/gkr320] [PMID: 21576229]
[235]
R Core Team. R: A Language and Environment for Statistical Computing,
[236]
Athanasiadis, E.; Cournia, Z.; Spyrou, G. ChemBioServer: A web-based pipeline for filtering, clustering and visualization of chemical compounds used in drug discovery. Bioinformatics, 2012, 28(22), 3002-3003.
[http://dx.doi.org/10.1093/bioinformatics/bts551] [PMID: 22962344]
[237]
Klein, K.; Kriege, N.; Mutzel, P. Scaffold Hunter: Facilitating Drug Discovery by Visual Analysis of Chemical Space.Computer Vision, Imaging and Computer Graphics. Theory and Application; Communications in Computer and Information Science; Springer: Berlin, Heidelberg, 2013, pp. 176-192.
[http://dx.doi.org/10.1007/978-3-642-38241-3_12]
[238]
Friesner, R.A.; Murphy, R.B.; Repasky, M.P.; Frye, L.L.; Greenwood, J.R.; Halgren, T.A.; Sanschagrin, P.C.; Mainz, D.T. Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem., 2006, 49(21), 6177-6196.
[http://dx.doi.org/10.1021/jm051256o] [PMID: 17034125]
[239]
Goodsell, D.S.; Morris, G.M.; Olson, A.J. Automated docking of flexible ligands: Applications of AutoDock. J. Mol. Recognit., 1996, 9(1), 1-5.
[http://dx.doi.org/10.1002/(SICI)1099-1352(199601)9:1<1:AID-JMR241>3.0.CO;2-6] [PMID: 8723313]
[240]
Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461.
[PMID: 19499576]
[241]
Korb, O.; Stützle, T.; Exner, T.E. Empirical scoring functions for advanced protein-ligand docking with PLANTS. J. Chem. Inf. Model., 2009, 49(1), 84-96.
[http://dx.doi.org/10.1021/ci800298z] [PMID: 19125657]
[242]
Kuntz, I.D.; Blaney, J.M.; Oatley, S.J.; Langridge, R.; Ferrin, T.E. A geometric approach to macromolecule-ligand interactions. J. Mol. Biol., 1982, 161(2), 269-288.
[http://dx.doi.org/10.1016/0022-2836(82)90153-X] [PMID: 7154081]
[243]
Ruiz-Carmona, S.; Alvarez-Garcia, D.; Foloppe, N.; Garmendia-Doval, A.B.; Juhos, S.; Schmidtke, P.; Barril, X.; Hubbard, R.E.; Morley, S.D. rDock: A fast, versatile and open source program for docking ligands to proteins and nucleic acids. PLOS Comput. Biol., 2014, 10(4)e1003571
[http://dx.doi.org/10.1371/journal.pcbi.1003571] [PMID: 24722481]
[244]
Dominguez, C.; Boelens, R.; Bonvin, A.M.J.J. HADDOCK: A protein-protein docking approach based on biochemical or biophysical information. J. Am. Chem. Soc., 2003, 125(7), 1731-1737.
[http://dx.doi.org/10.1021/ja026939x] [PMID: 12580598]
[245]
Wang, C.; Bradley, P.; Baker, D. Protein-protein docking with backbone flexibility. J. Mol. Biol., 2007, 373(2), 503-519.
[http://dx.doi.org/10.1016/j.jmb.2007.07.050] [PMID: 17825317]
[246]
London, N.; Miller, R.M.; Krishnan, S.; Uchida, K.; Irwin, J.J.; Eidam, O.; Gibold, L.; Cimermančič, P.; Bonnet, R.; Shoichet, B.K.; Taunton, J. Covalent docking of large libraries for the discovery of chemical probes. Nat. Chem. Biol., 2014, 10(12), 1066-1072.
[http://dx.doi.org/10.1038/nchembio.1666] [PMID: 25344815]
[247]
Grosdidier, A.; Zoete, V.; Michielin, O. SwissDock, a protein- small molecule docking web service based on EADock DSS. Nucleic Acids Res., 2011, 39(Web Server issue), W270-7.
[http://dx.doi.org/10.1093/nar/gkr366] [PMID: 21624888]
[248]
Pierce, B.G.; Wiehe, K.; Hwang, H.; Kim, B-H.; Vreven, T.; Weng, Z. ZDOCK server: Interactive docking prediction of protein-protein complexes and symmetric multimers. Bioinformatics, 2014, 30(12), 1771-1773.
[http://dx.doi.org/10.1093/bioinformatics/btu097] [PMID: 24532726]
[249]
Yan, Y.; Zhang, D.; Zhou, P.; Li, B.; Huang, S-Y. HDOCK: A web server for protein-protein and protein-DNA/RNA docking based on a hybrid strategy. Nucleic Acids Res., 2017, 45(W1), W365-W373.
[http://dx.doi.org/10.1093/nar/gkx407] [PMID: 28521030]
[250]
Watts, K.S.; Dalal, P.; Murphy, R.B.; Sherman, W.; Friesner, R.A.; Shelley, J.C. ConfGen: A conformational search method for efficient generation of bioactive conformers. J. Chem. Inf. Model., 2010, 50(4), 534-546.
[http://dx.doi.org/10.1021/ci100015j] [PMID: 20373803]
[251]
Gasteiger, J.; Sadowski, J.; Schuur, J.; Selzer, P.; Steinhauer, L.; Steinhauer, V. Chemical Information in 3D Space. J. Chem. Inf. Comput. Sci., 1996, 36, 1030-1037.
[http://dx.doi.org/10.1021/ci960343+]
[252]
Giannozzi, P.; Baroni, S.; Bonini, N.; Calandra, M.; Car, R.; Cavazzoni, C.; Ceresoli, D.; Chiarotti, G.L.; Cococcioni, M.; Dabo, I.; Dal Corso, A.; de Gironcoli, S.; Fabris, S.; Fratesi, G.; Gebauer, R.; Gerstmann, U.; Gougoussis, C.; Kokalj, A.; Lazzeri, M.; Martin-Samos, L.; Marzari, N.; Mauri, F.; Mazzarello, R.; Paolini, S.; Pasquarello, A.; Paulatto, L.; Sbraccia, C.; Scandolo, S.; Sclauzero, G.; Seitsonen, A.P.; Smogunov, A.; Umari, P.; Wentzcovitch, R.M. QUANTUM ESPRESSO: A modular and open-source software project for quantum simulations of materials. J. Phys. Condens. Matter, 2009, 21(39)395502
[http://dx.doi.org/10.1088/0953-8984/21/39/395502] [PMID: 21832390]
[253]
SC ’06: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing;; ACM: New York, NY, USA, 2006.
[254]
Sali, A.; Blundell, T.L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol., 1993, 234(3), 779-815.
[http://dx.doi.org/10.1006/jmbi.1993.1626] [PMID: 8254673]
[255]
Jacobson, M.P.; Pincus, D.L.; Rapp, C.S.; Day, T.J.F.; Honig, B.; Shaw, D.E.; Friesner, R.A. A hierarchical approach to all-atom protein loop prediction. Proteins, 2004, 55(2), 351-367.
[http://dx.doi.org/10.1002/prot.10613] [PMID: 15048827]
[256]
Biasini, M.; Bienert, S.; Waterhouse, A.; Arnold, K.; Studer, G.; Schmidt, T.; Kiefer, F.; Gallo Cassarino, T.; Bertoni, M.; Bordoli, L.; Schwede, T. SWISS-MODEL: Modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res., 2014, 42(Web Server issue), W252-8.
[http://dx.doi.org/10.1093/nar/gku340] [PMID: 24782522]
[257]
Wang, Y.; Virtanen, J.; Xue, Z.; Zhang, Y. I-TASSER-MR: Automated molecular replacement for distant-homology proteins using iterative fragment assembly and progressive sequence truncation. Nucleic Acids Res., 2017, 45(W1), W429-W434.
[http://dx.doi.org/10.1093/nar/gkx349] [PMID: 28472524]
[258]
Wang, C.; Zhang, H.; Zheng, W-M.; Xu, D.; Zhu, J.; Wang, B.; Ning, K.; Sun, S.; Li, S.C.; Bu, D. FALCON@home: A high-throughput protein structure prediction server based on remote homologue recognition. Bioinformatics, 2016, 32(3), 462-464.
[http://dx.doi.org/10.1093/bioinformatics/btv581] [PMID: 26454278]
[259]
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]
[260]
Petrey, D.; Honig, B. GRASP2: Visualization, surface properties, and electrostatics of macromolecular structures and sequences. Methods Enzymol., 2003, 374, 492-509.
[http://dx.doi.org/10.1016/S0076-6879(03)74021-X] [PMID: 14696386]
[261]
Chen, J.H.; Linstead, E.; Swamidass, S.J.; Wang, D.; Baldi, P.; Chem, D.B. ChemDB update--full-text search and virtual chemical space. Bioinformatics, 2007, 23(17), 2348-2351.
[http://dx.doi.org/10.1093/bioinformatics/btm341] [PMID: 17599932]
[262]
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]
[263]
Irwin, J.J.; Sterling, T.; Mysinger, M.M.; Bolstad, E.S.; Coleman, R.G. ZINC: A free tool to discover chemistry for biology. J. Chem. Inf. Model., 2012, 52(7), 1757-1768.
[http://dx.doi.org/10.1021/ci3001277] [PMID: 22587354]
[264]
Dolinsky, T.J.; Nielsen, J.E.; McCammon, J.A.; Baker, N.A. PDB2PQR: An automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations. Nucleic Acids Res., 2004, 32(Web Server issue), W665-7.
[http://dx.doi.org/10.1093/nar/gkh381] [PMID: 15215472]
[265]
Michel, J.; Tirado-Rives, J.; Jorgensen, W.L. Prediction of the water content in protein binding sites. J. Phys. Chem. B, 2009, 113(40), 13337-13346.
[http://dx.doi.org/10.1021/jp9047456] [PMID: 19754086]
[266]
Song, C.M.; Bernardo, P.H.; Chai, C.L.L.; Tong, J.C. CLEVER: Pipeline for designing in silico chemical libraries. J. Mol. Graph. Model., 2009, 27(5), 578-583.
[http://dx.doi.org/10.1016/j.jmgm.2008.09.009] [PMID: 18986817]
[267]
Lagorce, D.; Sperandio, O.; Galons, H.; Miteva, M.A.; Villoutreix, B.O. FAF-Drugs2: Free ADME/tox filtering tool to assist drug discovery and chemical biology projects. BMC Bioinformatics, 2008, 9, 396.
[http://dx.doi.org/10.1186/1471-2105-9-396] [PMID: 18816385]
[268]
Truchon, J-F.; Bayly, C.I. GLARE: A new approach for filtering large reagent lists in combinatorial library design using product properties. J. Chem. Inf. Model., 2006, 46(4), 1536-1548.
[http://dx.doi.org/10.1021/ci0504871] [PMID: 16859286]
[269]
Kuhn, T.; Willighagen, E.L.; Zielesny, A.; Steinbeck, C. CDK-Taverna: An open workflow environment for cheminformatics. BMC Bioinformatics, 2010, 11, 159.
[http://dx.doi.org/10.1186/1471-2105-11-159] [PMID: 20346188]
[270]
Wirth, M.; Zoete, V.; Michielin, O.; Sauer, W.H.B. SwissBioisostere: A database of molecular replacements for ligand design. Nucleic Acids Res., 2013, 41(Database issue), D1137-D1143.
[http://dx.doi.org/10.1093/nar/gks1059] [PMID: 23161688]
[271]
Douguet, D. e-LEA3D: A computational-aided drug design web server. Nucleic Acids Res,, 2010, 8(Web Server issue), W615-21.
[http://dx.doi.org/10.1093/nar/gkq322] [PMID: 20444867]
[272]
Lewis, D.F.; Ioannides, C.; Parke, D.V. An improved and updated version of the compact procedure for the evaluation of P450-mediated chemical activation. Drug Metab. Rev., 1998, 30(4), 709-737.
[http://dx.doi.org/10.3109/03602539808996328] [PMID: 9844807]
[273]
Predictive Models for Cytochrome P450 Metabolism - Camitro Corporation - SBIR Source. http://sbirsource.com/sbir/awards/80762-predictive-models-for-cytochrome-p450-metabolism (Accessed Jun 29, 2017).
[274]
Talafous, J.; Sayre, L.M.; Mieyal, J.J.; Klopman, G. META. 2. A dictionary model of mammalian xenobiotic metabolism. J. Chem. Inf. Comput. Sci., 1994, 34(6), 1326-1333.
[http://dx.doi.org/10.1021/ci00022a015] [PMID: 7989398]
[275]
Darvas, F. Metabolexpert: An Expert System for Predicting Metabolism of Substances.QSAR in Environmental Toxicology - II; Kaiser, K.L.E., Ed.; Springer Netherlands, 1987, pp. 71-81.
[http://dx.doi.org/10.1007/978-94-009-3937-0_7]
[276]
Ondetti, M.A.; Rubin, B.; Cushman, D.W. Design of specific inhibitors of angiotensin-converting enzyme: New class of orally active antihypertensive agents. Science, 1977, 196(4288), 441-444.
[http://dx.doi.org/10.1126/science.191908] [PMID: 191908]
[277]
Baldwin, J.J.; Ponticello, G.S.; Anderson, P.S.; Christy, M.E.; Murcko, M.A.; Randall, W.C.; Schwam, H.; Sugrue, M.F.; Springer, J.P.; Gautheron, P. Thienothiopyran-2-sulfonamides: Novel topically active carbonic anhydrase inhibitors for the treatment of glaucoma. J. Med. Chem., 1989, 32(12), 2510-2513.
[http://dx.doi.org/10.1021/jm00132a003] [PMID: 2585439]
[278]
Roberts, N.A.; Martin, J.A.; Kinchington, D.; Broadhurst, A.V.; Craig, J.C.; Duncan, I.B.; Galpin, S.A.; Handa, B.K.; Kay, J.; Kröhn, A. Rational design of peptide-based HIV proteinase inhibitors. Science, 1990, 248(4953), 358-361.
[http://dx.doi.org/10.1126/science.2183354] [PMID: 2183354]
[279]
von Itzstein, M.; Wu, W.Y.; Kok, G.B.; Pegg, M.S.; Dyason, J.C.; Jin, B.; Van Phan, T.; Smythe, M.L.; White, H.F.; Oliver, S.W. Rational design of potent sialidase-based inhibitors of influenza virus replication. Nature, 1993, 363(6428), 418-423.
[http://dx.doi.org/10.1038/363418a0] [PMID: 8502295]
[280]
Wood, J.M.; Maibaum, J.; Rahuel, J.; Grütter, M.G.; Cohen, N-C.; Rasetti, V.; Rüger, H.; Göschke, R.; Stutz, S.; Fuhrer, W.; Schilling, W.; Rigollier, P.; Yamaguchi, Y.; Cumin, F.; Baum, H-P.; Schnell, C.R.; Herold, P.; Mah, R.; Jensen, C.; O’Brien, E.; Stanton, A.; Bedigian, M.P. Structure-based design of aliskiren, a novel orally effective renin inhibitor. Biochem. Biophys. Res. Commun., 2003, 308(4), 698-705.
[http://dx.doi.org/10.1016/S0006-291X(03)01451-7] [PMID: 12927775]
[281]
Njoroge, F.G.; Chen, K.X.; Shih, N-Y.; Piwinski, J.J. Challenges in modern drug discovery: A case study of boceprevir, an HCV protease inhibitor for the treatment of hepatitis C virus infection. Acc. Chem. Res., 2008, 41(1), 50-59.
[http://dx.doi.org/10.1021/ar700109k] [PMID: 18193821]
[282]
Webber, S.E.; Bleckman, T.M.; Attard, J.; Deal, J.G.; Kathardekar, V.; Welsh, K.M.; Webber, S.; Janson, C.A.; Matthews, D.A.; Smith, W.W. Design of thymidylate synthase inhibitors using protein crystal structures: The synthesis and biological evaluation of a novel class of 5-substituted quinazolinones. J. Med. Chem., 1993, 36(6), 733-746.
[http://dx.doi.org/10.1021/jm00058a010] [PMID: 8459400]
[283]
Maskos, K.; Fernandez-Catalan, C.; Huber, R.; Bourenkov, G.P.; Bartunik, H.; Ellestad, G.A.; Reddy, P.; Wolfson, M.F.; Rauch, C.T.; Castner, B.J.; Davis, R.; Clarke, H.R.; Petersen, M.; Fitzner, J.N.; Cerretti, D.P.; March, C.J.; Paxton, R.J.; Black, R.A.; Bode, W. Crystal structure of the catalytic domain of human tumor necrosis factor-alpha-converting enzyme. Proc. Natl. Acad. Sci. USA, 1998, 95(7), 3408-3412.
[http://dx.doi.org/10.1073/pnas.95.7.3408] [PMID: 9520379]
[284]
Liebeschuetz, J.W.; Jones, S.D.; Morgan, P.J.; Murray, C.W.; Rimmer, A.D.; Roscoe, J.M.E.; Waszkowycz, B.; Welsh, P.M.; Wylie, W.A.; Young, S.C.; Martin, H.; Mahler, J.; Brady, L.; Wilkinson, K. PRO_SELECT: Combining structure-based drug design and array-based chemistry for rapid lead discovery. 2. The development of a series of highly potent and selective factor Xa inhibitors. J. Med. Chem., 2002, 45(6), 1221-1232.
[http://dx.doi.org/10.1021/jm010944e] [PMID: 11881991]
[285]
Matthews, D.A.; Dragovich, P.S.; Webber, S.E.; Fuhrman, S.A.; Patick, A.K.; Zalman, L.S.; Hendrickson, T.F.; Love, R.A.; Prins, T.J.; Marakovits, J.T.; Zhou, R.; Tikhe, J.; Ford, C.E.; Meador, J.W.; Ferre, R.A.; Brown, E.L.; Binford, S.L.; Brothers, M.A.; DeLisle, D.M.; Worland, S.T. Structure-assisted design of mechanism-based irreversible inhibitors of human rhinovirus 3C protease with potent antiviral activity against multiple rhinovirus serotypes. Proc. Natl. Acad. Sci. USA, 1999, 96(20), 11000-11007.
[http://dx.doi.org/10.1073/pnas.96.20.11000] [PMID: 10500114]
[286]
Dymock, B.W.; Barril, X.; Brough, P.A.; Cansfield, J.E.; Massey, A.; McDonald, E.; Hubbard, R.E.; Surgenor, A.; Roughley, S.D.; Webb, P.; Workman, P.; Wright, L.; Drysdale, M.J. Novel, potent small-molecule inhibitors of the molecular chaperone Hsp90 discovered through structure-based design. J. Med. Chem., 2005, 48(13), 4212-4215.
[http://dx.doi.org/10.1021/jm050355z] [PMID: 15974572]

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