Modelling of cytotoxicity data (CC50) of anti-HIV 1-[5-chlorophenyl) sulfonyl]-1H-pyrrole derivatives using calculated molecular descriptors and Levenberg-Marquardt artificial neural network.

A nonlinear quantitative structure anti-HIV activity relationship study was presented for modelling and predicting pyrryl aryl sulfones cytotoxicity data. Levenberg-Marquardt artificial neural network was used to link molecular structures and cytotoxicity data. A data set consisting of 27 derivatives of 1-[5-chlorophenyl) sulfonyl]-1H-pyrrole was used in this study. Among a large number of calculated descriptors, only eight significant molecular descriptors were obtained by stepwise regression, as the most feasible descriptors, and then they were used as inputs for neural network. The data set was randomly divided into 20 training and 7 validation sets and the neural network architecture and its parameters were optimized. The prediction ability of the model was evaluated using the validation data set, leave-one-out cross-validation and response randomization method. The mean square errors and mean absolute errors for the validation data set were 0.0067 and 0.066, respectively, and for the leave-one-out method, they were 0.013 and 0.087, respectively. The results obtained showed the excellent prediction ability and stability of the proposed model in the prediction of cytotoxicity data of the corresponding anti-HIV analogues.