Document Type : Research Paper
In this 4D-QSAR study, we obtained pharmacophore identification and biological activity prediction for 50 propoxy methylphenyl oxadiazole derivatives by the Electron Conformational Genetic Algorithm approach. In light of the results given in the data obtained from quantum chemical calculations at HF/3-21 G level, the electron conformational matrices of congruity (ECMC) were built by EMRE software. Considering the pharmacophore atoms, a parameter pool was introduced into the field. To find the theoretical biological activity of the molecules used in this study, the non-linear least squares regression method and genetic algorithm were used to determine the best subset of variables affecting bioactivity. As can be understand from our explanations, it should be noted that the results obtained in this study are in good agreement with the experimental data presented in the literature. The model for the training and test sets attained by the optimum 8 parameters gave highly satisfactory results with R2training= 0.872, q2=0.836 and SEtraining=0.059, q2ext1 = 0.787, q2ext2 = 0.786, q2ext3=0.830, ccctr = 0.933, ccctest = 0.896 and cccall = 0.926.