Biological study of several Alectinib as Cancer Cells Inhibitor using QSAR and Monte Carlo methods

QSAR investigations were conducted using multiple linear regression (MLR) and artificial neural network (ANN) as modeling tools, along with simulated annealing (SA), genetic algorithm (GA) and Imperialist Competitive Algorithm (ICA) optimization algorithms. In addition CORAL software was used to correlate the biological activity to the structural parameters of the drugs. Comparing the examined non-linear methods revealed that ANN-GA and MLR-ICA were the best approach. According to the results, in GA-ANN method minimum value in BLTA96 (Verhaar model of Algae based-line toxicity from MLOGP (mmol/l)/ Molecular properties) descriptor and maximum value in Mor 02u (indicates that the size of the inhibitor molecule has certain effect on the extent of the interaction between the drug and molecule) descriptor and in ICA-MLR method minimum value in atomic Sanderson electronegativities descriptors and maximum value in polarizibility, weighted by atomic masses, descriptors and in Monte Carlo method the number of Nitrogen atom, presence of double bond and cyclic ring with branching can be used for designing new drugs because reducing the half maximal inhibitory concentration (IC50) value.