Parası kalmadığı için otobüse binemiyordur ailesi porno izle ona daha yeni para gönderdiği için tekrar porno istemeye utanınca mecburen otostop çekmek için youporn çantasını alarak yol kenarına gelir etekli porno liseli türk kız yol kenarında dururken yanına yaklaşan porno kibar bir gencin onu gideceği yere kadar bırakmak porno izle istemesine çok mutlu olur arabaya bindiklerinde gideceği yer ile porno arabayı kullanan adamın gittiği yer arasında çok mesafe sex izle farkı olduğunu anlayan türk kız bu yaptığı porno indir iyilik karşısında arabada ona memelerini açar porno sapıklaşan adam yol kenarındaki hotelde durarak porno izle üniversiteli otostop çeken türk kızına odada sakso çektirip sikerQuantitative Structure-Activity Relationship (QSAR) Studies of Some Glutamine Analogues for Possible Anticancer Activity| Abstract

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Abstract

Quantitative Structure-Activity Relationship (QSAR) Studies of Some Glutamine Analogues for Possible Anticancer Activity

Author(s): Elidrissi B, Ousaa A, Ajana MA, Bouachrine M and Lakhlifi T

A quantitative structure-property relationship (QSPR) study was performed to predict anticancer activity in tumor cells of thirty-six 5-N-substituted-2-(substituted benzenesulphonyl) glutamines compounds using the electronic and topologic descriptors computed respectively, with ACD/Chem Sketch and Gaussian 03W programs. The structures of all 36 compounds were optimized using the hybrid density functional theory (DFT) at the B3LYP/6-31G (d) level of theory. In both approaches, 30 compounds were assigned as the training set and the rest as the test set. These compounds were analyzed by the principal components analysis (PCA) method, a descendant multiple linear regression (MLR), multiple nonlinear regression (MNLR) analyses and an artificial neural network (ANN). The robustness of the obtained models was assessed by leave-many-out cross-validation, and external validation through test set. This study shows that the ANN has served marginally better to predict antitumor activity when compared with the results given by predictions made with MLR and MNLR.


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