International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Comparative Evaluation of Multiple Linear Regression and Support vector Machine aided Linear and Non-linear QSAR Models

Author Affiliations

  • 1Govt. Shaheed Bhagirath Silawat College, Depalpur, Indore, MP, INDIA
  • 2Dept. of Chemistry, Govt. Holkar Science College, Indore, MP, INDIA
  • 3Dept. of Pharmaceutical Chemistry, Softvision College, Indore, MP, INDIA

Res.J.chem.sci., Volume 4, Issue (7), Pages 24-29, July,18 (2014)

Abstract

Type 2 diabetes still remains a major challenge to human health management. Protein tyrosine phosphate 1B has been continuously explored for its therapeutic potential to treat type 2 diabetes as it is linked with negative regulation of insusignal transduction. QSAR studies were performed on derivatives of 2and SVM aided linear and non-linear models were obtained which were further evaluated to identify descriptors revealing underlying structure-activity relationship. QSAR models were validated through a series of validation techniques like Yrandomization and descriptor sensitivity in addition to internal validation parameters. Information content index (IC1) of neighbourhood symmetry of order-1 has beeactivity relationship of 2-arylsulphonylaminobenzothiazoles derivatives. Geary autopolarizability are also actively correlated to biological response of tyrosine phosphate 1B inhibitors

References

  1. Singh S., The Genetics of Type 2 diabetes mellitus: A Review., J. Sci. Res., 55, 35-48 (2011)
  2. Qaseem A., Humphrey L.L., Oral Pharmacologic Treatment of Type 2 Diabetes Mellitus: A Clinical practice Guideline from the American college of Physicians, Ann Intern Med., 156, 218-231 (2012)
  3. Bahare R.S., Gupta J., Malik S., Sharma N., New Emerging Targets for Type-2 Diabetes, Intl. J. Pharm Tech.,3(2), 809-818 (2011)
  4. Johnson T.O., Ermolieff J., Jirousek M.R., Protein tyrosine phosphatise 1b inhibitors for diabetes, Nat. Rev. Drug Discov., 1, 696-709 (2012)
  5. Koren S, Fantus I.G., Inhibition of the protein tyrosine phosphatase PTP1B: potential therapy for obesity, insulin resistance and type 2 diabetes mellitus, Best PractRes Clin Endocrinol Metab., 21, 621-40 (2007)
  6. Goldstein B.J., Protein-tyrosine phosphatase 1B (PTP1B): a novel therapeutic target for type 2 diabetes mellitus, obesity and related states of insulin resistance, Curr Drug Targets Immune Endocr Metabol Disord. 1, 265-75 (2001)
  7. Rosenbloom A.L., Silverstein J.H., Amemiya S., Zeitler P., Klingensmith G.J,. Type 2 diabetes in the child and adolescent, Pediatr Diabetes., , 512526 (2008)
  8. Dearden J.C., In silico prediction of drug toxicity. J. Comput Aided Mol. Des.,17, 119 -27 (2003)
  9. Nantasenamat C., Isarankura-Na-Ayudhya C., Naenna T., Prachayasittikul V., "A practical overview of quantitative structure-activity relationship". Excli J.,, 74-88 (2009)
  10. Nantasenamat C., Isarankura-Na-Ayudhya C., Naenna T., Prachayasittikul V., Advances in computational methods to predict the biological activity of compounds, Expert Opin Drug Discov.,, 63354 (2010)
  11. Poole D., Mackworth A., Goebel R., Computational Intelligence: A Logical Approach, Oxford University Press, USA, (1998)
  12. Yegnanarayana B., Artificial neural networks, PHI Learning Pvt. Ltd. (2009)
  13. Joachims T., Text categorization with support vector machines: Learning with many relevant features. Technical Report 23, LS VIII, University at Dortmund (1997)
  14. Rokach L., Maimon O., Data mining with decision trees: theory and applications. World Scientific Pub Co. Inc. (2008)
  15. Ramoni M., Sebastiani P. "Robust bayes classifiers." Artificial Intelligence., 125: 209-226 (2001)
  16. Montgomery D.C., Peck E.A., Vining G.G., Introduction to linear regression analysis, John Wiley & Sons. 821 (2012)
  17. Navarrete-Vazquez G. et al. Synthesis, in vitro and computational studies of protein tyrosine phosphatise 1B inhibition of a small library of 2-arylsulfonylamino benzothiazole with antihyperglycemic activity. Bioorg Med chem.,17:3332-3341 (2009 18.MarvinSketch version 5.5.1,(2009)
  18. MarvinSketch version 5.5.1,(2009) Chemaxon.http://www.chemaxon.com.
  19. VCCLAB, Virtual Computational Chemistry Laboratory, http://www.vcclab.org. (2005)
  20. SarchitectTM 2.5 Designer/Miner, Strand Life Sciences Pvt. Ltd., Bangalore, India, (2008)
  21. Cortes C., Vapnik V., Support-Vector Networks. Mach. Learn. 20, 273-297 (1995)
  22. Mangasarian O.L., Musicant D.R., Large scale kernel regression via linear programming, Mach. Learn., 46:255-269 (2002)
  23. Klopman G., Kalos A.N., Causality in Structure-Activity Studies. J. Comput. Chem., :492-506 (1985)
  24. Wold S, Eriksson L. Statistical Validation of QSAR Results. In: van de Waterbeemd, H. (Ed.) Chemometric Methods in Molecular Design, Weinheim., 309-318 (1995)
  25. Saltelli A., Tarantola S., Campolongo F., Ratto, M., Sensitivity analysis in practice: a guide to assessing scientific models. John Wiley & Sons, (2004)