MULTIVARIANT QSAR MODEL FOR SOME POTENT COMPOUNDS AS POTENTIAL ANTI-TUMOR INHIBITORS: A COMPUTATIONAL APPROACH

Authors : Shola ELIJAH, Sani UBA, Adamu UZAIRU

Pages : 38-46

Doi:10.33435/tcandtc.458664

View : 34 | Download : 11

Publication Date : 2019-06-15

Article Type : Research Paper

Abstract :ABSTRACT A computational approach was employed to develop multivariate QSAR model to corr e l a t e th e ch e m i c a l structur e s of th e ciprofloxacin a n a logu e s w i th th ei r obs e rv e d a ct i v i t ie s us i ng a th e or e t i c a l a ppro a ch. Genetic Function Algorithm insert ignore into journalissuearticles values(GFA); and Multiple Linear Regression Analysis insert ignore into journalissuearticles values(MLRA); were used to select the descriptors and to generate the correlation QSAR models that relate the activity values against tumor with the molecular structures of the active molecules. The models were validated and the best model selected has squared correlation coefficient insert ignore into journalissuearticles values( R 2 ); of 0.990531, adjusted squared correlation coefficient insert ignore into journalissuearticles values(R adj ); of 0.95962 and Leave one out insert ignore into journalissuearticles values(LOO); cross validation coefficient insert ignore into journalissuearticles values( ); value of 0.942963 . The external validation set used for confirming the predictive power of the model has its R 2 pred of 0.8486. Stability and robustness of the model obtained by the validation test indicate that the model can be used to design and synthesis other ciprofloxacin derivatives with improved anti-tumor activity.
Keywords : Keywords Ciprofloxacin, Descriptors, Genetic Function Algorithm, tumor

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