The quantitative structure–activity relationship (QSAR) analyses were carried out in a series of novel sulfonamide derivatives as the procollagen C-proteinase inhibitors for treatment of fibrotic conditions. Sphere exclusion method was used to classify data set into categories of train and test set at different radii ranging from 0.9 to 0.5. Multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) were used as the regression methods and stepwise, Genetic algorithm (GA), and simulated annealing (SA) were used as the feature selection methods. Three of the statistically best significant models were chosen from the results for discussion. Model 1 was obtained by MLR–SA methodology at a radius of 1.6. This model with a coefficient of determination (r2)= 0.71 can well predict the real inhibitor activities. Crossvalidated q2 of this model, 0.64, indicates good internal predictive power of the model. External validation of the model (pred_r2= 0.85) showed that the model can well predict activity of novel PCP inhibitors. The model 2 which developed using PLS–SW explains 72% (r2= 0.72) of the total variance in the training set as well as it has internal (q2) and external (pred_r2) predictive ability of 67% and 71% respectively. The last developed model by PCR–SA has a correlation coefficient (r2) of 0.68 which can explains 68% of the variance in the observed activity values. In this case internal and external validations are 0.61 and 0.75, respectively. Alignment Independent (AI) and atomicvalence connectivity index (chiv) have the greatest effect on the biological activities. Developed models can be useful in designing and synthesis of effective and optimized novel PCP inhibitors which can be used for treatment of fibrotic conditions.
