Role of open source tools and resources in virtual screening for drug discovery

Combinatorial Chemistry and High Throughput Screening

Volumn 18, Issue 6, 2015, Pages 528-543

  Karthikeyan, Muthukumarasamy ^a   Vyas, Renu ^a  

^a NATIONAL CHEMICAL LABORATORY   (India)

Author keywords

Chemoinformatics; Kinases; Open source; Scaffold; Virtual screening

Indexed keywords

NEW DRUG; PHOSPHOTRANSFERASE INHIBITOR; MOLECULAR LIBRARY;

CHEMICAL STRUCTURE; COMBINATORIAL CHEMISTRY; COMBINATORIAL LIBRARY; DRUG DESIGN; DRUG SCREENING; DRUG TARGETING; MACHINE LEARNING; MEDICAL LITERATURE; MOLECULAR DYNAMICS; MULTIDRUG RESISTANCE; PATENT; PHARMACOPHORE; PRIORITY JOURNAL; REVIEW; STRUCTURE ACTIVITY RELATION; VIRTUAL DRUG SCREENING; BIOLOGY; CHEMICAL DATABASE; CHEMISTRY; DRUG DEVELOPMENT; HUMAN; MOLECULAR LIBRARY; PRECLINICAL STUDY;

COMPUTATIONAL BIOLOGY; DATABASES, CHEMICAL; DRUG DISCOVERY; DRUG EVALUATION, PRECLINICAL; HUMANS; SMALL MOLECULE LIBRARIES;

EID: 84942097821     PISSN: 13862073     EISSN: 18755402     Source Type: Journal    
DOI: 10.2174/1386207318666150703111911     Document Type: Review

References (138)

1. Karthikeyan, M.; Krishnan, S. Chemoinformatics: A tool for modern drug discovery. Int. J. Info. Techno. Management, 2002, 1(1), 69-82.
2. Jorgenson, W.L. Efficient drug lead discovery and optimization. Acc. Chem. Res., 2009, 724-733.
3. Oprea, T.I.; Marshall, G.R. Perspect. Drug Discov. Design., 1998, 35, 9-11.
4. Takahashi, Y.; Sukekawa, M.; Sasaki, S. Automatic identification of molecular similarity using reduced graph representation of chemical structure. J. Chem. Comput. Sci., 1992, 32, 639-643.
5. Hert, J.; Willet, P.; Wilkon, J.W.; Acklin, P.; Azzaoui, K.; Jacoley, E.; Schuffenhauer A. Comparison of topological descriptors for similarity based virtual screening using multiple bioactive reference structures. Org. Biomol. Chem., 2004, 2, 3256-3266.
6. Bohm, H-J.; Schneider, G. (Eds) Virtual Screening for Bioactive Molecules, Wiley, Weinheim, Germany, 2000.
7. Anang, A. S.; Kiplin, G. Scaffold composition and biological relevance of screening libraries. Nat. Chem. Biol., 2007, 3, 442- 446.
8. Bemis, G.W.; Murcko, M.A. The properties of known drugs, 1: Molecular frameworks. J. Med. Chem., 1996, 39, 2887-2893.
9. Urbanek, D.A.; Proschak, E.; Tanrikulu, Y.; Becker, S.; Karas, M.; Schneider, G. Scaffold-hopping from aminoglycosides to small synthetic inhibitors of bacterial protein biosynthesis using a pseudoreceptor model. Med. Chem. Comm., 2011, 2(3), 181-184.
10. Steindl, T.; Langer, T. Docking Versus pharmacophore model generation: A comparison of high-throughput virtual screening strategies for the search of human rhinovirus coat protein inhibitors. QSAR Comb. Sci., 2005, 24(4), 470-479.
11. Fotouhi, N.; Gillespie, P.; Goodnow, R.A.; So, S.S.; Han, Y.; Babiss, L.E. Application and utilization of chemoinformatics tools in lead generation and optimization. Comb. Chem. High Throughput Screen., 2006, 9, 95-102.
12. Ratsch, G. A. A brief introduction into machine learning in 21st Chaos Communication Congress, Berliner Congress Center, Berlin, Germany 2004.
13. Yang Y.; Adelstein, J.; Kassis, A. Target discovery from data mining approaches. Drug Discov. Today, 2009, 14 (3/4), 147-154.
14. Hopkins, A.L. Network pharmacology: The next paradigm in drug discovery. Nat. Chem. Biol., 2008, 4, 682-690.
15. Wang, F.; Liu, D.; Wang, H.; Luo, C.; Zheng, M.; Liu, H.; Zhu, W.; Luo, X.; Zhang, J.; Jiang, H., Computational Screening for Active Compounds Targeting Protein Sequences: Methodology and Experimental Validation. J. Chem. Inf. Model, 2011, 51, 2821- 2828.
16. Latha, N.; Jayaram, B. A Binding Affinity Based Computational Pathway for Active-Site Directed Lead Molecule Design: Some Promises and Perspectives. Drug Des. Rev., 2005, 2, 145-165.
17. Schlessinger, A.; Geier, E., Fan, H.; Irwin, J.J.; Shoichet, B.K.; Giacomini, K.M.; Sali, A. Structure-based discovery of prescription drugs that interact with the norepinephrine transporter, NET. Proc. Natl. Acad. Sci. USA, 2011, 108, 15810-15815.
18. Feldman, R.; Sanger, J.; Eds. The text mining handbook, Cambridge University Press, 2007.
19. Karthikeyan, M.; Vyas, R. General chemical reactivity fingerprints for virtual screening. Comb. Chem. High Throughput Screen., 2015, 18(6).
20. Karthikeyan, M.; Jain, E.; Rajmohanan, P.R.; Vyas, R. Simulated carbon and proton NMR chemical shift based binary fingerprints for virtual screening. Comb. Chem. High Throughput Screen., 2015, 18(6).
21. Alvarez, J.; Shoichet, B.; (Eds.). Virtual screening in drug discovery, CRC Press: Boca Raton, Florida, USA, 2005.
22. Reddy, A.S.; Pati, P.; Kumar, P.; Pradeep, H.N.; Sastry, G.N. Virtual screening in drug discovery - A computational perspective. Curr. Protein Pept. Sci, 2007, 8, 329-351.
23. Scior, T.; Bender, A.; Tresadern, G.; Medina-Franco, J.L.; Martinez-Mayorga K.; Langer, T., Cuanalo-Contreras, K.; Agrafiotis, D.K. Recognizing pitfalls in virtual screening: A critical review. J. Chem. Inf. Model, 2012, 52(4), 867-881.
24. Perez-Sanchez, H.; Wenzel, W. Optimization methods for virtual screening on novel computational architectures. Curr. Comput Aid Drug Des., 2011, 7(1), 44-52.
25. http://www.java.com(accessed October 3, 2013).
26. Karthikeyan, M.; Pandit, D.; Bhavasar, A.; Vyas, R. Design and Development of ChemInfoCloud : An Integrated cloud enabled platform for virtual screening. Comb. Chem. High Throughput Screen., 2015, 18(6).
27. Karthikeyan, M.; Krishnan, S.; Pandey, A.K.; Bender, A. Harvesting chemical information from the Internet using a distributed approach: ChemXtreme. J. Chem. Inf. Model, 2006, 46, 452-461.
28. Karthikeyan, M.; Krishnan, S.; Pandey, A.K.; Bender, A; Tropsha, A. Distributed chemical computing using ChemStar: An open source java remote method invocation architecture applied to large scale molecular data from PubChem. J. Chem. Inf. Model, 2008, 48(4), 691-703.
29. Varnek, A.; Tropsha, A.; Eds. Chemoinformatics approaches to virtual screening. Royal Society of Chemistry: Cambridge UK, 2008.
30. Sacan, A.; Ekins, S.; Kortagere, S. Applications and limitations of insilico models in drug discovery. Methods Mol. Biol., 2012, 910, 87-124.
31. Karthikeyan, M.; Pandit, Y.; Pandit, D.; Tropsha, A.; Vyas, R. Mega Miner: A tool for hit identification through text mining using chemoinformatics tools and cloud computing environment. Comb. Chem. High Throughput Screen., 2015, 18(6), 591-603.
32. Karthikeyan, M.; Pandit, D.; Vyas, R. ChemScreener: A distributed computing tool for scaffold based virtual screening. Comb. Chem. High Throughput Screen., 2015, 18(6), 544-561.
33. Tiikkainen, P.; Franke, L. Analysis of commercial and public bioactivity databases J. Chem. Inf. Model, 2012, 52, 319-326.
34. http://pubchem.ncbi.nlm.nih.gov/(Accessed September 3, 2014).
35. http://www.ncbi.nlm.nih.gov/pcassay(Accessed August 5, 2015).
36. Gaulton, A.; Bellis, L.J.; Bento, A.P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Bissan Al- Lazikani, B.; Overington, J.P. ChEMBL: A large scale bioactivity database for drug discovery. Nucleic Acid Res., 2012, 40(D1): D1100-D1107.
37. https://www.ebi.ac.uk/chembl/(Accessed September 3, 2014).
38. http://www.ebi.ac.uk/(Accessed September 3, 2014).
39. http://www.accessdata.fda.gov/scripts/cder/drugsatfda/(Accessed September 3, 2014).
40. http://www.drugbank.ca/(Accessed October 3, 2014).
41. http://www.rcsb.org/pdb/home/home.do(Accessed September 3, 2014).
42. http://www.ebi.ac.uk/pdbe-srv/pdbechem/(Accessed September 3, 2014).
43. www.cancer.gov/(Accessed September 3, 2014).
44. Berlow, N.; Davis, L.E.; Cantor, E.L.; Seguin, B.; Keller, C.; Pal, R. A new approach for prediction of tumour sensitivity based on functional data. BMC Bioinformatics, 2013, 14, 239.
45. http://pdsp.med.unc.edu/kidb.phpkidb(Accessed September 3, 2014).
46. http://rapid-i.com/content/view/181/196/bindingdb (Accessed October 3, 2014).
47. http://www.maybridge.com(Accessed September 3, 2014).
48. http://www.specs.net/snpage.php?snpageid=home(Accessed September 3, 2014).
49. http://zinc.docking.org/(Accessed September 3, 2014).
50. Sharma, R.; Lawrenson, A.S.; Fisher, N.E; Warman, A.J.; Shone, A.E.; Hill, A.; Mbekeani, A.; Pidathala, C.; Amewu, R.K.; Leung, S.; Gibbons, P.; Hong, D.W.; Stocks, P.; Nixon, G.L.; Chadwick, J.; Shearer, J.; Gowers, I.; Cronk, D.; Parel, S.P.; O'Neill, P.M.; Ward, S.A.; Biagini, G.A.; Berry, N.G. Identification of novel antimalarial chemotypes via chemoinformatic compound selection methods for a high-throughput screening program against the novel malarial target, PfNDH2: increasing hit rate via virtual screening methods. J. Med. Chem., 2012, 55, 3144-54.
51. http://www.genome.jp/kegg/(Accessed September 3, 2014).
52. http://www.cheminformatics.org/(Accessed September 2, 2014).
53. http://www.osdd.net/(Accessed September 3, 2014).
54. O'Boyle, N.M.; Guha, R.; Willighagen, E.L.; Adams, S.E.; Alvarsson, J.; Bradley, J.C.; Filippov, I.V.; Hanson, R.M.; Hanwell, M.D.; Hutchison, G.R.; James, C.A.; Jeliazkova, N.; Lang, A.S.; Langner, K.M.; Lonie, D.C.; Lowe, D.M.; Pansanel, J.; Pavlov, D.; Spjuth, O.; Steinbeck, C.; Tenderholt, A.L.; Theisen, K.J.; Murray-Rust, P. Open data, open source and open standards in chemistry: The blue obelisk five years on. J. Cheminform., 2011, 3(1), 37.
55. Steinbeck, C.; Hoppe, C.; Kuhn, S.; Matteo, F.; Guha, R.; Willighagen, E.L. Recent development of the CDK (Chemistry Development Kit) an open source JAVA library for chemo and bioinformatics. Curr. Pharm. Design., 2006, 12(17), 2111-2120.
56. http://cran.r-project.org/web/packages/rcdk/index.html(Accessed October 3, 2014).
57. http://www.ra.cs.uni-tuebingen.de/software/joelib/users.html (Accessed October 3, 2014).
58. http://www.rdkit.org/(Accessed October 3, 2014).
59. http://openbabel.org/wiki/Main-Page(Accessed October 3, 2014).
60. Hong, H.; Xie, Q.; Ge, W.; Qian, F.; Fang, H.; Shi, L.; Su, Z.; Perkins, R.; Tong, W. Mold2 Molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. J. Chem. Inf. Model., 2008, 48, 1337-1344.
61. Varnek, A.; Fourches, D.; Horvath, D.; Klimchuk, O.; Gaudin, C.; Vayer, P.; Solovev, V.; Hoonakker, F.; Tetko, I.V.; Marcou, G. ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors. Curr. Comp. Aid Design., 2008, 4(3), 191-198.
62. http://www.bioclipse.net/(Accessed October 3, 2014).
63. Kuhn, T.; Willighagen, E.L.; Zielesny, A.; Steinbeck, C. CDKTaverna: An open workflow environment for chemoinformatics. BMC Bioinformatics, 2010, 11, 159.
64. Schneider, G.; Lee, M.L.; Stahl, M.; Schneider, P. De novo design of molecular architectures by evolutionary assembly of drugderived building blocks. J. Comput. Aid Mol. Des., 2000, 14(5), 487-494.
65. http://pubchem.ncbi.nlm.nih.gov/summary/summary.cgi?cid=471.
66. http://www.chemspider.com/(Accessed September 3, 2014).
67. Schuller, A.; Hahnke, V.; Schneider, G. SmiLib v2.0: A Java- Based Tool for Rapid Combinatorial Library Enumeration, QSAR Comb. Sci., 2007, 3, 407-410.
68. Karthikeyan, M; Vyas, R. Open source tools, techniques and data in chemoinformatics. In: Practical Chemoinformatics, Springer, 2014, p1-89.
69. https://surechem.uservoice.com/knowledgebase/articles/84207-tanimoto-coefficient-and-fingerprint-generation (Accessed October 3, 2013).
70. Jeong, S.; Kim, S-W.; Choi, B-U. Dimensionality reduction for similarity search with the Euclidean distance in high-dimensional applications. Multimed. Tools Appl., 2000, 42, 251-271.
71. https://www.chemaxon.com/jchem/doc/user/ECFP.html (Accessed 27 February, 2015).
72. http://www.molprint.com/(Accessed October 3, 2014).
73. http://www.chemaxon.com/jchem/intro/index.html(Accessed October 3, 2014).
74. Karthikeyan, M.; Jain, E.; Rajmohanan, P.R.; Vyas, R. Simulated carbon and proton NMR chemical shift based binary fingerprints for virtual screening. Comb. Chem. High Throughput Screen., 2015, 18(6), 562-576.
75. Todeschini, R.; Consonni, V. Molecular Descriptors for Chemoinformatics. (2 volumes) Wiley-VCH, 2009.
76. http://www.moleculardescriptors.eu/(Accessed September 3, 2014).
77. Barden, C.J.; Chaefer, H.F. Quantum chemistry in the 21st century. Pure All Chem, 2000, 72(8), 1405-1423.
78. http://www.nwchem-sw.org(Accessed September 3, 2014).
79. http://www.openmx-square.org/(Accessed September 3, 2014).
80. http://www.msg.ameslab.gov/gamess/(Accessed September 3, 2014).
81. http://pyquante.sourceforge.net/(Accessed September 3, 2014).
82. http://inac.cea.fr/L-Sim/BigDFT/(Accessed September 3, 2014).
83. http://www.psicode.org/(Accessed September 3, 2014).
84. http://openmopac.net/(Accessed September 3, 2014).
85. Rucker C, Rucker G, Meringer M. y - randomization and its variants in QSPR/QSAR. J. Chem. Inf. Model, 2007, 47(6), 2345- 2357.
86. Roy, K.; Mandal, K.S. Development of linear and nonlinear predictive QSAR models and their external validation using molecular similarity principle for anti-HIV indolyl aryl sulfones. J. Enzyme Inhib. Med. Chem., 2008, 23(6), 980-995.
87. Oprea, T.I. Virtual screening in lead discovery a viewpoint. Molecules, 2002, 7, 51-62.
88. http://crdd.osdd.net/admet.php(Accessed October 3, 2015).
89. Fujiwara, Y.; Yamashita, Y.; Asao, M.; Osoda, T.; Shimadzu, H.; Asogawa, M.; Nakao, K.; Fukushima, C.; Shimizu, R. Virtual screening system for finding structurally diverse hit by active learning. J. Chem. Inf. Model, 2008, 48(4), 930-940.
90. Guha, R.; Jurs, P.C. Interpreting computational neural network QSAR models : A measure of descriptor importance. J. Chem. Inf. Model, 2005, 45, 800-806.
91. Kirew, D.B.; Chretien, J.R.; Bernard, P.; Ros, F. Application of kohonen neural networks in classification of biologically active compounds. SAR QSAR Environ. Res., 1998, 1-2, 93-107.
92. Vert, J.P. In: Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques; Lodhi, H., Yamanishi, Y., Eds. IGI Global, 2010, pp. 35-45.
93. Svetnik, V.; Liaw, A.; Tong, C.; Culberson, C.; Sheridan, R. P.; Feuston, B. P. Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling. J. Chem. Inf. Comp. Sci., 2003, 43, 1947-1958.
94. Mitchelle, T.M. Decision tree learning in Machine Learning, McGraw-Hill 1997; pp. 52-80.
95. Klon, A.E. Bayesian modeling in virtual high throughput screening. Comb. Chem. High Throughput Screen., 2009, 12, 469-483.
96. Labute, P. Binary QSAR: A new method for the determination of quantitative structure activity relationships. Pac. Symp. Biocomput., 1999, pp. 444-55.
97. Gertrudes, J.C.; Maltarollo, V.G.; Silva, R.A.; Oliveira, P.R.; Honorio, K.M.; da Silva A.B. Machine learning techniques and drug design. Curr. Med. Chem., 2012, 19(25), 4289-4297.
98. Tan, L.; Bajorath, J. Utilizing Target-Ligand Interaction Information in Fingerprint Searching for Ligands of Related Targets. Chem. Biol. Drug Des., 2009, 74, 25-32.
99. Marti-Renom M.A.; Stuart, A.C.; Fiser, A.; Sanchez, R.; Melo, F.; Sali, A. Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct., 2000, 29, 291-325.
100. Maiorov, V.; Sheridan, R.P. Enhanced Virtual Screening by Combined Use of Two Docking Methods: Getting the Most on a Limited Budget. J. Chem. Inf. Model, 2005, 45(4), 1017-1023.
101. Davis, C.; Harris, H.J.; Hu, K. In silico directed mutagenesis identifies the CD81/claudin-1 hepatitis C virus receptor interface. Cell Microbiol., 2012, 14, 1892-1903.
102. Jones, S.; Thornton, J.M. Principles of Protein-Protein Interactions. Proc. Natl. Acad Sci. USA, 1996, 93, 13-20.
103. Huang, Z.; Malcolm, J.; McGregor; Zhaowen, L.; Jiang, X. Virtual Screening in Drug Discovery in Drug Discovery Research: New Frontiers in Post-Genomic Era. Wiley, 2006.
104. Terstappen, G.C.; Schlupen, C.; Raggiaschi, R.; Gavigraghi, G. Target deconvolution strategies in drug discovery. Nat. Rev. Drug Discov., 2007, 6(11), 891-903.
105. http://www.modelling.leeds.ac.uk/pocketfinder/help.html (Accessed September 3, 2014).
106. http://xray.bmc.uu.se/usf/voidoo.html(Accessed September 3, 2014).
107. http://www2.nbcr.net/data/sw/hosted/POVME/(Accessed September 3, 2014).
108. http://fpocket.sourceforge.net/(Accessed September 3, 2014).
109. Karthikeyan, M.; Deshmukh, Y.; Vyas, R. Active site features mediated virtual screening of leads. Unpublished Results.
110. Taylor, R.D.; Jewsbury, P.J.; Essex, J.W. A review of protein-small molecule docking methods. J. Comput. Aid Mol. Des., 2002, 16, 151-166.
111. 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, 455- 461.
112. http://hex.loria.fr/(Accessed September 3, 2014).
113. http://www.arguslab.com/arguslab.com/Welcome.html (Accessed September 3, 2014).
114. Karthikeyan, M.; Nainaru, G, Muthukrishnan, M.; Vyas, R. Pharmacophore and docking based virtual screening of validated MTb targets. Comb. Chem. High Throughput Screen., 2015, 18(6).
115. Ma, B.; Shatsky, M.; Wolfson, H.J.; Nussinov, R. Multiple diverse ligands binding at a single protein site. Proteins Sci., 2002, 11(2), 184-197.
116. Rodon, J.; Perez, J.; Kurzrock, R. Combining targeted therapies: practical issues to consider at the bench and bedside. Oncologist, 2010, 15, 37-50.
117. Jackman, A.; Kaye, S.; Workman, P. The combination of cytotoxic and molecularly targeted therapies - can it be done? Drug Discov. Today, 2004, 1, 445-454.
118. Al-Lazikani, B.; Banerji, U.; Workman, P. Combinatorial drug therapy for cancer in the post genomic era. Nat. Biotechnol., 2012, 30(7) 679-692.
119. Makley, L.N.; Gestwicki, J.E. Expanding the number of druggable targets: Non-enzymes and protein protein interactions. Chem. Biol. Drug Des., 2013, 81, 22-32.
120. http://www.millipore.com/life-sciences/flx4/ld-kinaseprofiler-targets (Accessed September 3, 2014).
121. Veljkovic, N.; Glisic, S.; Prljic, J.; Perovic, V.; Veljkovic, V. Simple and General Criterion for "in silico" screening of candidate HIV drugs. Curr. Pharm. Biotechnol., 2012, 14, 561-9.
122. Lewis, S.N.; Bassaganya-Riera J.; Bevan, D.R. Virtual screening as a technique for PPAR modulator discovery. PPAR Res., 2010. Doi:10.1155/2010/861238.
123. http://gpcr.scripps.edu/gpcr-targets.htm(Accessed on September 2, 2014).
124. Rollinger, J.M.; Haupt, S.; Stuppner, H.; Langer, T. Combining ethnopharmacology and virtual screening for lead structure discovery: COX inhibitors as examples. J. Chem. Inf. Comput. Sci., 2004, 44, 480-488.
125. http://www.who.int/tb/challenges/mdr/en/(Accessed September 3, 2014).
126. Krishna, R.; Mayer L.D. Multidrug resistance (MDR) in cancer. Mechanisms, reversal using modulators of MDR and the role of MDR modulators in influencing the pharmacokinetics of anticancer drugs. Eur. J. Pharm. Sci., 2000, 11(4), 265-283.
127. Da Silva PEA.; Polomino J.C. Molecular basis and mechanisms of drug resistance in mycobacterium tuberculosis: classical and new drugs. J. Antimicrob. Chemother., 2011, 67, 1417-30.
128. Hanks, S.K.; Quinn, A.M.; Hunter, T. The protein kinase family: conserved features and deduced phylogeny of the catalytic domains. Science, 1988, 241, 42-52.
129. Zhang, J.; Yang, P.L.; Gray, N.S. Targeting cancer with small molecular kinase inhibitors. Nat. Rev. Cancer, 2009, 9(1) 28-39.
130. Santiago, D.N.; Pevzner, Y.; Durand, A.A.; Tran, M.; Scheerer, R.R.; Daniel, K.; Sung, S.S.; Woodcock, L.H.; Guida, W.C.; Brooks, W.H. Virtual target screening validation using kinase inhibitors. J. Chem. Inf. Model, 2012, 52(8), 2192-2203.
131. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schnikowski, B.; Idekar, T. Cytoscape: A software environment for integrated models of biomolecular interaction network. Genome Res., 2003, 13, 2498-2504.
132. http://www.megasoftware.net/(Accessed September 3, 2014).
133. Fu, J.; Bian, M.; Jiang, Q.; Zhang, C. Roles of Aurora Kinases in mitosis and tumorogenesis. Mol. Cancer Res., 2007, 5, 1-10.
134. Myrianthopoulos, V.; Magiatis, P.; Ferandin, Y.; Skaltsounis, A.L.; Meijer, L.; Mikros, E. An integrated computational approach to the phenomenon of potent and selective inhibition of aurora kinases B and C by a series of 7-substituted indirubins. J. Med. Chem., 2007, 50, 4027-4037.
135. Zhong, M.; Bui, M.; Shen, W.; Baskaran, S.; Allen, D.A.; Eiling, R.A. 2-Aminobenzimidazoles as potent aurora kinase inhibitors. Bioorg. Med. Chem. Lett., 2009, 19, 5158-5161.
136. Hanessian, S.; Moitessier, N.; Therrian, E. A comparative docking study and the design of potentially selective MMP inhibitors. J. Comput. Aid Mol. Des., 2001, 15, 873-881.
137. Tiwari, R.; Mahasenan, K.; Pavlovicz, R.; Li, C.; Tjarks, W. Carborane clusters in computational drug design: A comparative docking evaluation using Autodock, Flexx, Glide and Surflex. J. Chem. Inf. Model, 2009, 49(6), 1581-1589.
138. Reymond, J.L.; Awale, M. Exploring chemical space for drug discovery using the chemical universe database. ACS Chem. Neurosci., 2012, 3(9), 649-657.