QSAR study of necroptosis inhibitory activities (EC50) of [1,2,3] thiadiazole and thiophene derivatives using Bayesian regularized artificial neural network and calculated descriptors

Abstract

A Bayesian regularized artificial neural network (BR-ANN) model was developed for modeling and accurate prediction of necroptosis inhibitory activities of [1,2,3] thiadiazole and thiophene derivatives as potent tumor necrosis factor-α-induced necroptosis inhibitors. The chemical structure of each compound was converted to 1,481 molecular descriptors by Dragon software. Among these, only seven descriptors relating the activity data to the molecular structures were selected as the significant ones. The best BR-ANN model was a three layer feed forward network with the 7-4-1 architecture. Prediction ability of the proposed model was evaluated by prediction of pEC50 of some compounds in the external (test) data set. The mean square error, mean absolute error, correlation coefficient (R), and mean relative error for the test set were 0.0214, 0.126, 0.978, and 2.475, respectively. The results obtained show the superior prediction ability of the proposed model in the prediction of necroptosis inhibitory activities.