MOLECULAR PARAMETERS IN QSAR OF METHYLPHENYL QUINOLIN TRIAZOL DERIVATIVES BY MOLINSPIRATION

Authors

  • Alok Kumar Dash Institute of Pharmacy V.B.S.P.University Jaunpur, Uttarpradesh, India, 222003
  • Jhansee Mishra Institute of Pharmacy V.B.S.P.University Jaunpur, Uttarpradesh, India, 222003

Abstract

Various physiochemical parameters are known to be cross-correlated. Therefore, only variables or their combinations that have little co-variance should be used in a QSAR analysis. Molinspiration is perhaps best known as the distributor of the Java Molecular Editor (JME) in various forms, created by Peter Ertl at Novartis. It also publishes a wide range of chemical informatics software including mitools, a java program for calculating molecular properties. The chemical descriptor space whose convex hull is generated by a particular training set of chemicals is called the training set's applicability domain. Prediction of properties of novel chemicals that are located outside the applicability domain uses extrapolation, and so is less reliable (on average) than prediction within the applicability domain. For the methylphenylquinoline Triazol derivatives the best suitable receptor is Kinase inhibitor because all the derivative the value of Kinase comes out positive and it also near the ‘O’ value that means low energy is required.

Key Words: Molinspiration, QSAR, Kinase inhibitor, drug design.

References

Nantasenamat C, Isarankura-Na-Ayudhya C, Naenna T, Prachayasittikul V (2009). "A practical overview of quantitative structure-activity relationship". Excli J. 8: 74–88.

Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V (Jul 2010). "Advances in computational methods to predict the biological activity of compounds". Expert Opinion on Drug Discovery. 5 (7): 633–54. doi:10.1517/17460441.2010.492827. PMID 22823204.

Yousefinejad S, Hemmateenejad B (2015). "Chemometrics tools in QSAR/QSPR studies: A historical perspective". Chemometrics and Intelligent Laboratory Systems. 149, Part B: 177–204. doi:10.1016/j.chemolab.2015.06.016.

Tropsha A, Gramatica P, Gombar VJ (2003). "The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models". QSAR &Comb. Sci. 22: 69–77. doi:10.1002/qsar.200390007.

Gramatica P (2007). "Principles of QSAR models validation: internal and external". QSAR &Comb. Sci. 26: 694–701. doi:10.1002/qsar.200610151.

Chirico N, Gramatica P (Aug 2012). "Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection". Journal of Chemical Information and Modeling. 52 (8): 2044–58. doi:10.1021/ci300084j. PMID 22721530.

Tropsha, Alexander (2010). "Best Practices for QSAR Model Development, Validation, and Exploitation". Molecular Informatics. 29 (6-7): 476–488. doi:10.1002/minf.201000061. ISSN 1868-1743.

Patani GA, LaVoie EJ (Dec 1996). "Bioisosterism: A Rational Approach in Drug Design". Chemical Reviews. 96 (8): 3147–3176. doi:10.1021/cr950066q. PMID 11848856.

Nathan Brown. Bioisosteres in Medicinal Chemistry. Wiley-VCH, 2012, p. 237. ISBN 978-3-527-33015-7

Thompson SJ, Hattotuwagama CK, Holliday JD, Flower DR (2006). "On the hydrophobicity of peptides: Comparing empirical predictions of peptide log P values". Bioinformation. 1 (7): 237–41. doi:10.6026/97320630001237. PMC 1891704. PMID 17597897.

Wildman SA, Crippen GM (1999). "Prediction of physicochemical parameters by atomic contributions". J. Chem. Inf. Comput. Sci. 39 (5): 868–873. doi:10.1021/ci990307l.

Downloads

Published

2018-12-30

Issue

Section

Review Article