Qualitative and quantitative pharmacophore-similarity assessment of anthranilamide-based factor Xa inhibitors: applications on similar molecules with identical biological endpoints

References

• Steindl T, Laggner C, Langer T. Human rhinovirus 3C protease: generation of pharmacophore models for peptidic and nonpeptidic inhibitors and their application in virtual screening. J Chem Inf Model 2005;45:716–24
   PubMed Web of Science ®Google Scholar
• Wermuth CG, Ganellin CR, Lindberg P, Mitscher LA. Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1997). Annu Rep Med Chem 1998;33:385–95
   Google Scholar
• Langer T, Hoffmann RD. Pharmacophores and pharmacophore searches. Weinheim: Wiley-VCH; 2006
   Google Scholar
• Schneider G, Neidhart W, Giller T, Schmid G. Scaffold-hopping by topological pharmacophore search: a contribution to virtual screening. Angew Chem Int Ed 1999;38:2894–5
   PubMed Web of Science ®Google Scholar
• Fechner U, Franke L, Renner S, et al. Comparison of correlation vector methods for ligand based similarity searching. J Comput Aided Mol Des 2003;17:687–98
   PubMed Web of Science ®Google Scholar
• Good AC, Kuntz ID. Investigating the extension of pairwise distance pharmacophore measures to triplet-based descriptors. J Comput Aided Mol Des 1995;9:373–9
   PubMed Web of Science ®Google Scholar
• Zhou L, Griffith R, Gaeta B. Combining spatial and chemical information for clustering pharmacophores. BMC Bioinform 2014;15:S5
   PubMed Web of Science ®Google Scholar
• Kumar SP, Jasrai YT, Mehta VP, Pandya HA. Development of pharmacophore similarity-based quantitative activity hypothesis and its applicability domain: applied on a diverse data-set of HIV-1 integrase inhibitors. J Biomol Struct Dyn 2015;33:706–22
   PubMed Web of Science ®Google Scholar
• Kumar SP, Jasrai YT, Pandya HA, Rawal RM. Pharmacophore-similarity-based QSAR (PS-QSAR) for group-specific biological activity predictions. J Biomol Struct Dyn 2015;33:56–69
   Web of Science ®Google Scholar
• Verma J, Khedkar VM, Coutinho EC. 3D-QSAR in drug design – a review. Curr Top Med Chem 2010;10:95–115
   PubMed Web of Science ®Google Scholar
• Topliss JG, Costello RJ. Chance correlations in structure–activity studies using multiple regression analysis. J Med Chem 1972;15:1066–8
   PubMed Web of Science ®Google Scholar
• Unger SH, Hansch C. On model building in structure–activity relationships. A reexamination of adrenergic blocking activity of beta-halo-beta-arylalkylamines. J Med Chem 1973;16:745–9
   PubMed Web of Science ®Google Scholar
• Kubinyi H. Comparative molecular field analysis (CoMFA). In: Gasteiger J, ed. Handbook of chemoinformatics: from data to knowledge in 4 volumes. Weinheim: Wiley-VCH; 2008:1555–74
   Google Scholar
• Varnek A, Trosha A. Chemoinformatics approaches to virtual screening. Cambridge: The Royal Society of Chemistry; 2008
   Google Scholar
• Nendza M. Activity and effects parameters. In: Nezda M, ed. Structure–activity relationships in environmental sciences. London: Chapman & Hall; 1998:47–62
   Google Scholar
• Kim KH. Outliers in SAR and QSAR: 2. Is a flexible binding site a possible source of outliers? J Chem Inf Model 2007;21:421–35
   Google Scholar
• Tropsha A. Best practices for QSAR model development, validation, and exploitation. Mol Inf 2010;29:476–88
   PubMed Web of Science ®Google Scholar
• Grohmann R, Schindler T. Toward robust QSPR models: synergistic utilization of robust regression and variable elimination. J Comput Chem 2008;29:847–60
   PubMed Web of Science ®Google Scholar
• Ghasemi JB, Tavakoli H. Improvement of the prediction power of the CoMFA and CoMSIA models on histamine H3 antagonists by different variable selection methods. Sci Pharm 2012;80:547–66
   PubMedGoogle Scholar
• Li H, Sutter J, Hoffmann R. HypoGen: an automated system for generating 3D predictive pharmacophore models. In: Güner OF, ed. Pharmacophore perception, development, and use in drug design. La Jolla (CA): International University Line; 2000:171–89
   Google Scholar
• Scior T, Medina-Franco JL, Do QT, et al. How to recognize and workaround pitfalls in QSAR studies: a critical review. Curr Med Chem 2009;16:4297–313
   PubMed Web of Science ®Google Scholar
• Zhang P, Bao L, Zuckett JF, et al. Design, synthesis, and SAR of anthranilamide-based factor Xa inhibitors with improved functional activity. Bioorg Med Chem Lett 2004;14:989–93
   PubMed Web of Science ®Google Scholar
• Rameshwar N, Krishna K, Kumar BA, Parthasarathy T. QSAR studies of N1-(5-chloro-2-pyridyl)-2-{[4-(alkyl methyl)benzoyl] amino}-5-chlorobenzamide analogs. Bioorg Med Chem 2006;14:319–25
   PubMed Web of Science ®Google Scholar
• Ghasemi JB, Hooshmand S. 3D-QSAR, docking and molecular dynamics for factor Xa inhibitors as anticoagulant agents. Mol Simul 2013;39:453–71
   Web of Science ®Google Scholar
• Schneidman-Duhovny D, Dror O, Inbar Y, et al. PharmaGist: a webserver for ligand-based pharmacophore detection. Nucleic Acids Res 2008;36:W223–8
   PubMed Web of Science ®Google Scholar
• Jmol: an open-source Java viewer for chemical structures in 3D. Available from: www.jmol.org [last accessed 23 Dec 2014]
   Google Scholar
• Krieger E, Darden T, Nabuurs SB, et al. Making optimal use of empirical energy functions: force-field parameterization in crystal space. Proteins 2004;57:678–83
   PubMed Web of Science ®Google Scholar
• VLifeMDS: Molecular Design Suite. Pune: VLife Sciences Technologies. Available from: www.vlifesciences.com [last accessed 12 Dec 2014]
   Google Scholar
• Wolber G, Seidel T, Bendix F, Langer T. Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discov Today 2008;13:23–9
   PubMed Web of Science ®Google Scholar
• Joachims T. Making large-scale SVM learning practical. In: Schölkopf B, Burges C, Smola A, eds. Advances in kernel methods – support vector learning. Cambridge, MA: MIT Press; 1998: 169–84
   Google Scholar
• Wang L. Support vector machines: theory and applications. Heidelberg: Springer-Verlag; 2005
   Google Scholar
• Hsu CW, Chang CC, Lin CJ. A practical guide to support vector classification. Taiwan: Department of Computer Science and Information Engineering, National Taiwan University; 2003
   Google Scholar
• Sewell M. Portal of support vector machine. Available from: www.svms.org/parameters/ [last accessed 15 Dec 2014]
   Google Scholar
• Rychetsky M. Algorithms and architectures for machine learning based on regularized neural networks and support vector approaches. Berlin: Shaker Verlag GmbH; 2001
   Google Scholar
• Leach AR, Gillet VJ. An introduction to cheminformatics. Dordrecht, Netherlands: Kluwer Academic Publishers; 2003
   Google Scholar
• Bernstein FC, Koetzle TF, Williams GJB. The protein data bank: a computer based archival file for macromolecular structures. J Mol Biol 1977;112:535–42
   PubMed Web of Science ®Google Scholar
• Brandstetter H, Kühne A, Bode W, et al. X-ray structure of active site-inhibited clotting factor Xa. Implications for drug design and substrate recognition. J Biol Chem 1996;271:29988–92
   PubMed Web of Science ®Google Scholar
• Singh UC, Kollman PA. An approach to computing electrostatic charges for molecules. J Comput Chem 1984;5:129–45
   Web of Science ®Google Scholar
• Duan Y, Wu C, Chowdhury S, Lee MC, et al. A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. J Comput Chem 2003;24:1999–2012
   PubMed Web of Science ®Google Scholar
• Jadhav K, Mukhopadhyay S, Ajmani S, et al. GRIP docking. Available from: http://www.vlifesciences.com/support/Whitepaper/GRIPDocking.pdf [last accessed 12 Dec 2014]
   Google Scholar
• Ye B, Arnaiz DO, Chou Y-L, et al. Thiophene-anthranilamides as highly potent and orally available factor Xa inhibitors. J Med Chem 2007;50:2967–80
   PubMed Web of Science ®Google Scholar
• Corte JR, Fang T, Pinto DJP, et al. Structure–activity relationships of anthranilamide-based factor Xa inhibitors containing piperidinone and pyridinone P4 moieties. Bioorg Med Chem Lett 2008;18:2845–9
   PubMed Web of Science ®Google Scholar
• Cramer RD, Patterson DE, Bunce JD. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 1988;110:5959–67
   PubMed Web of Science ®Google Scholar
• Pastor M, Cruciani G, McLay I, et al. GRid-INdependent descriptors (GRIND): a novel class of alignment-independent three-dimensional molecular descriptors. J Med Chem 2000;43:3233–43
   PubMed Web of Science ®Google Scholar
• Kumar SP, George LB, Jasrai YT, Pandya HA. Prioritization of active antimalarials using structural interaction profile of Plasmodium falciparum enoyl-acyl carrier protein reductase (PfENR)-triclosan derivatives. SAR QSAR Environ Res 2015;26:67–77
   Web of Science ®Google Scholar
• Kubinyi H. QSAR: Hansch analysis and related approaches. Weinheim: Wiley-VCH; 1993
   Google Scholar