Chemometric and chemoinformatic analyses of anabolic and androgenic activities of testosterone and dihydrotestosterone analogues
Abstract
Predictive quantitative structure-activity relationship (QSAR) models of anabolic and androgenic activities for the testosterone and dihydrotestosterone steroid analogues were obtained by means of multiple linear regression using quantum and physicochemical molecular descriptors (MD) as well as a genetic algorithm for the selection of the best subset of variables. Quantitative models found for describing the anabolic (androgenic) activity are significant from a statistical point of view: R(2) of 0.84 (0.72 and 0.70). A leave-one-out cross-validation procedure revealed that the regression models had a fairly good predictability [q(2) of 0.80 (0.60 and 0.59)]. In addition, other QSAR models were developed to predict anabolic/androgenic (A/A) ratios and the best regression equation explains 68% of the variance for the experimental values of AA ratio and has a rather adequate q(2) of 0.51. External validation, by using test sets, was also used in each experiment in order to evaluate the predictive power of the obtained models. The result shows that these QSARs have quite good predictive abilities (R(2) of 0.90, 0.72 (0.55), and 0.53) for anabolic activity, androgenic activity, and A/A ratios, respectively. Last, a Williams plot was used in order to define the domain of applicability of the models as a squared area within +/-2 band for residuals and a leverage threshold of h=0.16. No apparent outliers were detected and the models can be used with high accuracy in this…
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