Abstract

Thermolysin is a bacterial proteolytic enzyme, considered by many authors 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 such common diseases such as hypertension and heart failure. In this paper was developed a mathematical model of multiple linear regression for ordinary least squares and genetic algorithm for selection of variables, implemented in QSARINS software, with appropriate parameters for fitting of the same. The model was extensively validated according to OECD standards, so that was proven its robustness, stability, low correlation of descriptors and good predictive power. In addition, was found that the model fit is not the product of a random correlation. For evaluation of the compounds identified as potential outliers, molecular docking techniques were used, since the predictions of the model they are unreliable. Finally, were identified 141 compounds very active and 69 extremely active as thermolysin inhibitors, concluding that the proposals computational tools are an efficient methodology for the identification of new drugs that inhibit this enzyme.