مشخصات پژوهش

عنوان	Quantitative Structure–Retention Relationship Modeling of Morphine and Its Derivatives on OV‑1 Column in Gas–Liquid Chromatography Using Genetic Algorithm
نوع پژوهش	مقاله چاپ‌شده در مجلات علمی
کلیدواژه‌ها	Morphine derivatives , Genetic algorithm Artificial neural network, Quantitative structure–retention relationship
چکیده	In this paper, a back propagation artificial neural network (BP-ANN) was used for quantitative structure-retention relationship (QSRR) modeling of retention time (tR) of 57 morphine and its derivatives. The molecular descriptors were calculated for each compound. By applying a genetic algorithm, the most relevant descriptors were selected to build the QSRR model. The selected descriptors were: Hosoya Index, kappa1, and most negative potential. The prediction results from the BP-ANN were in good agreement with the experimental values. The optimal QSRR model was developed based on a 3-3-1 artificial neural network architecture using molecular descriptors calculated from molecular structure alone. The root-meansquare error (RMSE) and squared correlation coefficient (R2) for the ANN model were 0.3996 and 0.9559 for the training set (42 molecules) and 0.6052 and 0.9540 for the prediction set (15 molecules), respectively.
پژوهشگران	امین رستمی (نفر سوم)، سعدی سعید پور (نفر دوم)، ئه سرین بهمنی (نفر اول)