In silico modeling of β-carbonic anhydrase inhibitors from the fungus Malassezia globosa as antidandruff agents

References

• Shuster S. The aetiology if dandruff and mode of action of therapeutic agents. Br J Dermatol 1984;111:235–42
   PubMed Web of Science ®Google Scholar
• Hewitson KS, Vullo D, Scozzafava A, et al. Molecular cloning, characterization, and inhibition studies of a β-carbonic anhydrase from Malassezia globosa, a potential antidandruff target. J Med Chem 2012;55:3513–20
   PubMed Web of Science ®Google Scholar
• BROOD (version 2.0.0). OpenEye Science Software. Santa Fe (NM); 2010
   Google Scholar
• OMEGA (version 2.4.3). OpenEye Science Software. Santa Fe (NM); 2010
   Google Scholar
• Tresadern G, Bemporad D, Howe TA. Comparison of ligand based virtual screening methods and application to corticotropin releasing factor 1 receptor. J Mol Graph Model 2009; 27:860–70
   PubMed Web of Science ®Google Scholar
• Halgre TA. MMFF VI. MMFF94s option for energy minimization studies. J Comput Chem 1999;20:720–9
   Web of Science ®Google Scholar
• Stewart JJP. Optimization of parameters for semi empirical methods. V. Modification of NDDO approximations and application to 70 elements. J Mol Model 2007;l13:1173–213
   Web of Science ®Google Scholar
• Stewart JJP. MOPAC2012 version 12.239W, Stewart Computational Chemistry, Colorado Springs, CO. Available from: http://OpenMOPAC.net [last accessed 26 Aug 2012]
   Google Scholar
• PRECLAV v. 1203. (Documentation included) is available from Center of Organic Chemistry – Bucharest; 2012
   Google Scholar
• Tarko L. QSPR/QSAR computations by PRECLAV software. Rev Chim (Bucuresti) 2005;56:639–48
   Web of Science ®Google Scholar
• Tarko L, Stecoza CE, Ilie C, Chifiriuc MC. QSAR Studies on antibacterial activity of some substituted dihydrodibenzothiepins. Rev Chim (Bucuresti) 2009;60:476–9
   Web of Science ®Google Scholar
• Tarko L. QSAR studies regarding the inhibition of the carbonic anhydrase by the sulfonamides containing a picolinoyl Group. Rev Chim (Bucuresti) 2007;58:191–4
   Web of Science ®Google Scholar
• Singh S, Supuran CT. Chemometric modeling of breast cancer associated carbonic anhydrase IX inhibitors belonging to the ureido-substituted benzene sulfonamide class. J Enzyme Inhib Med Chem 2014;29:877–83
   PubMed Web of Science ®Google Scholar
• Singh S. In silico molecular modeling of carbonic anhydrase inhibitors: inhibition of Plasmodium falciparum carbonic anhydrase with aromatic/heterocyclic sulfonamides. Bioorg Med Chem Lett 2015;25:133–41
   PubMed Web of Science ®Google Scholar
• Singh S. A QSAR study on novel series of carbonic anhydrase inhibitors hCA IX – tumor-associated (Hypoxia). Med Chem 2012;8:656–72
   PubMed Web of Science ®Google Scholar
• Singh S. Comparative QSAR studies on the novel series of thiazolones and tetrazole derivatives as HCV NS5B polymerase allosteric inhibitors. Lett Drug Des Discov 2009;6:286–97
   Web of Science ®Google Scholar
• Singh S, Singh S, Shukla P. Modeling of novel HIV-1 protease inhibitors incorporating N-aryl-oxazolidinone-5-carboxamides as P2 ligands using quantum chemical and topological finger print descriptors. Med Chem Res 2011;20:1556–65
   Web of Science ®Google Scholar
• Singh S, Supuran CT. Chemometric QSAR modeling and in silico design of carbonic anhydrase inhibition of a coral secretory isoform by sulfonamide. Bioorg Med Chem 2013;21:1495–502
   PubMed Web of Science ®Google Scholar
• Singh S, Supuran CT. 3D-QSAR CoMFA studies on sulfonamide inhibitors of the Rv3588c b-carbonic anhydrase from Mycobacterium tuberculosis and design of not yet synthesized new molecules. J Enzyme Inhib Med Chem 2014; 29:449–55
   PubMed Web of Science ®Google Scholar
• Tarko L. Fragmentarea virtual a moleculelor si evaluarea similaritatilor. Rev Chim (Bucuresti) 2004;55:539–46
   Google Scholar
• Tarko L. A procedure for virtual fragmentation of molecules into functional groups. ARKIVOC 2004;xiv:74–82
   Google Scholar
• Eriksson L, Jaworska J, Worth AP, et al. Methods for reliability and uncertainty assessment and for applicability evaluations of classification and regression-based QSARs. Environ Health Perspect 2003;111:1361–75
   PubMed Web of Science ®Google Scholar
• Roy K, Mitra I, Kar S, et al. Comparative studies on some metrics for external validation of QSPR models. J Chem Inf Model 2012;52:396–408
   PubMed Web of Science ®Google Scholar
• Ojha PK, Mitra I, Das RN, Roy K. Further exploring rm2 metrics for validation of QSPR models dataset. Chemom Intell Lab Syst 2011;107:194–205
   Web of Science ®Google Scholar
• Roy K, Chakraborty P, Mitra I, et al. Some case studies on application of “r(m)2” metrics for judging quality of quantitative structure–activity relationship predictions: emphasis on scaling of response data. J Comput Chem 2013;34:1071–82
   PubMed Web of Science ®Google Scholar
• Roy K. On some aspects of validation of predictive quantitative structure–activity relationship models. Expert Opin Drug Discov 2007;2:1567–77
   PubMed Web of Science ®Google Scholar
• Draper NR, Smith H. Applied regression analysis. New York: Wiley; 1981
   Google Scholar
• Golbraikh A, Tropsha A. Beware of q2!. J Mol Graph Model 2002;20:269–76
   PubMed Web of Science ®Google Scholar
• Osten DW. Selection of optimal regression models via cross-validation. J Chemom 1998;2:39–48
   Google Scholar
• Organization for Economic Co-operation and Development Guidance Document on the Validation of (Quantitative) Structure–Activity Relationship QSAR Models. OECD Document ENV/JM/MONO 2; 2007
   Google Scholar
• Worth AP, Aldenberg T, Benjamin I, Cronin MTD. Quantitative structure activity relationships – the report and recommendations of ECVAM workshop 52. ATLA 2005;33:155–73
   Google Scholar
• Tropsha A, Gramatica P, Gombar VK. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. Comb Sci 2003;22:69–77
   Google Scholar
• Weaver S, Gleeson MP. The importance of the domain of applicability in QSAR modeling. J Mol Graph Model 2008;26:1315–26
   PubMed Web of Science ®Google Scholar
• NCSS (Statistical Software Delux package). Kaysville, UT; 2004. Available from: http://www.ncss.com
   Google Scholar
• Cook DR. Residuals and influence in regression. New York: Chapman and Hall; 1982
   Google Scholar
• Drug Theoretics and Cheminformatics Laboratory, Jadavpur University, Kolkata. Available from: http://dtclab.webs.com/software-tools
   Google Scholar
• Van de Waterbeemd H (ed). Statistical validation of QSAR results. In: Chemometric methods in molecular design. Weinheim: Wiley-VCH; 1998:309–18
   Google Scholar
• Everitt B, Landau S, Leese M. Cluster analysis. London: Arnold Press; 2001
   Google Scholar