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Multiple Linear Regression Model of Thermolysin Inhibitors
Juan Castillo-Garit 1 , Yudith Cañizares-Carmenate 2 , Karel Mena-Ulecia 3 , Yunier Perera-Sardiña 4 , Francisco Torrens 5

1  Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Department of Pharmacy, Faculty of Chemical-Pharmacy. Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba,Bioinformatic Research in Systems & Computer Engineering, Carleton University, Ottawa, Canada
2  Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Department of Pharmacy, Faculty of Chemical-Pharmacy. Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba
3  Centro Interdisciplinario de Neurociencia de Valparaiso, Facultad de Ciencias, Universidad de Valparaiso, Valparaiso, Chile
4  Doctorado en Fisicoquimica Molecular, Center of Applied Nanosciences (CENAP), Universidad Andres Bello, Ave. Republica 275, Santiago, Chile
5  Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, P. O. Box 22085, 46071 Valencia, Spain

Published: 18 January 2017 by MDPI AG in Proceedings of MOL2NET 2016, International Conference on Multidisciplinary Sciences, 2nd edition in MOL2NET 2016, International Conference on Multidisciplinary Sciences, 2nd edition
MDPI AG, 10.3390/mol2net-02-03872
Abstract:

Thermolysin is a bacterial proteolytic enzyme, considered by many authors as a pharmacological and biological model of other mammalian enzymes, with similar structural characteristics, such as Angiotensin Converting Enzyme and Neutral Endopeptidase. Inhibitors of these enzymes are considered therapeutic targets for common diseases, such as hypertension and heart failure. In this report, a mathematical model of Multiple Linear Regression, for ordinary least squares, and genetic algorithm, for selection of variables, are developed and implemented in QSARINS software, with appropriate parameters for its fitting. The model is extensively validated according to OECD standards, so that its robustness, stability, low correlation of descriptors and good predictive power are proven. In addition, it is found that the model fit is not the product of a random correlation. Two possible outliers are identified in the model application domain but, in a molecular docking study, they show good activity, so we decide to keep both in our database. The obtained model can be used for the virtual screening of compounds, in order to identify new active molecules.


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