Prediction of cytotoxicity data (CC50) of anti-HIV 5-pheny-l-phenylamino-1H-imidazole derivatives by artificial neural network trained with Levenberg–Marquardt algorithm

A Levenberg–Marquardt algorithm trained feed-forward artificial neural network in quantitative structure–activity relationship (QSAR) was developed for modeling of cytotoxicity data for anti-HIV 5-pheny-l-phenylamino-1H-imidazole derivatives. A large number of descriptors were calculated with Dragon software and a subset of calculated descriptors was selected with a stepwise regression as a feature selection technique. The 28 molecular descriptors selected by stepwise regression, as the most feasible descriptors, were used as inputs for feed-forward neural network. The neural network architecture and its parameters were optimized. The data were randomly divided into 31 training and 11 validation sets. The prediction ability of the model was evaluated using validation data set and “one-leave-out” cross validation method. The root mean square errors (RMSE) and mean absolute errors for the validation data set were 0.042 and 0.024, respectively. The prediction ability of ANN model was also statistically compared with results of linear free energy related model. The obtained results show the validity of proposed model in the prediction of cytotoxity data of corresponding anti-HIV drugs.