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

Abstract

ABSTRACT A computational approach was employed to develop multivariate QSAR model to correlate the chemical structures of the ciprofloxacin analogues with their observed activities using a theoretical approach. Genetic Function Algorithm (GFA) and Multiple Linear Regression Analysis (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 (R2) of 0.990531, adjusted squared correlation coefficient (Radj) of 0.95962 and Leave one out (LOO) cross validation coefficient () value of 0.942963. The external validation set used for confirming the predictive power of the model has its R2pred 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.