Quantitative structure activity relationship study of p۳۸α MAP kinase inhibitors

The aim of this study is to investigate QSAR modeling for a set of novel pyridine derivatives as inhibitors of p۳۸α enzyme. After calculating a set of molecular descriptors, the selection of variables was performed using two methods: genetic algorithm (GA) and stepwise regression (SW). To build and evaluate the models, the dataset was divided into two training and test sets based on the clustering method, which included ۳۵ compounds in the training set and ۱۰ compounds in the test set. The results showed that the genetic algorithm-based model (GA-MLR) performed better than the stepwise regression model (SW-MLR). The final GA-MLR model was developed using six descriptors and its statistical values were obtained as R²train = ۰.۸۳۵, RMSEtrain = ۰.۳۸۵, Ftrain = ۲۶.۱۹۹, R²test = ۰.۶۴۵, RMSEtest = ۰.۶۰۱ and Ftest = ۱.۰۶۵. In order to confirm the accuracy of the model, in addition to validation by the test set, cross-validation techniques, domain determination, and Y-randomization test were also performed. These results indicate that the developed model can be used as an effective tool for designing new pyridine derivatives with higher inhibitory potency and predicting their activity before synthesis.

کلیدواژه ها:

QSAR ، Genetic Algorithm ، p۳۸&alpha ، Map ، Pyridin derivative