Predictive QSAR modeling of HIV reverse transcriptase inhibitor TIBO derivatives

Comparative quantitative structure-activity relationship (QSAR) studies have been carried out on tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepine (TIBO) derivatives as reverse transcriptase inhibitors (n=70) using topological, structural, physicochemical, electronic and spatial descriptors. The data set was divided into training and test sets using a cluster-based method. Linear models were developed using multiple regression (with stepwise regression, factor analysis and genetic function approximation (GFA) as variable selection tools) and partial least squares (PLS) and combination of factor analysis and partial least squares (FA-PLS). Genetic function approximation (spline) and artificial neural networks (ANN) were used for the development of non-linear models. Using topological and structural descriptors, the best equation was obtained from GFA (spline) based on internal validation (Q(2)=0.737), but the model with the best external validation characteristics was obtained with FA-PLS (R(pred)(2)=0.707). When structural, physicochemical, electronic and spatial descriptors were used, the best Q(2) (0.740) value was obtained from GFA (spline) whereas PLS provided the best R(pred)(2) (0.784) value. When all descriptors were used in combination, the best R(pred)(2) (0.760) value and the best Q(2) (0.800) value were obtained from ANN and GFA (spline), respectively. The majority of the models satisfied the criteria of external validation recommended by Golbraikh and Tropsha (2002) and the criteria of modified r(2) (r(m)(2)) values of the test set for external validation as suggested by Roy and Roy (2008). In order to further validate selected models, an external set of 10 TIBO derivatives, which fall within the applicability domain of the models and are not shared with the compounds of the present data set, was taken from a different source, and reverse transcriptase inhibitory activity of these compounds was predicted. Acceptable values of squared correlation coefficients between the observed and predicted values of the external set compounds were obtained from the selected models suggesting true predictive potential of the models.