Computational evaluation of some compounds as potential anti-breast cancer agents

Abstract : Quantitative Structure Activity Relationship (QSAR) and molecular Docking studies were carried out on some novel compounds to generate a good QSAR models that relate the anti-breast cancer activity values with the molecular structure of the compounds. Genetic Function Algorithm (GFA) and Multiple Linear Regression Analysis (MLRA) were used to select the descriptors that were used to build the models. The best model built was found to have statistical validation values of squared correlation coefficient ( R 2 ) = 0.999, adjusted squared correlation coefficient (  = 0.998, cross validation coefficient  = 0.998 and an external squared correlation coefficient = 0.879 which was used to confirm the validation of the model. The docking results showed that ligands 6 and 5 with binding energy (-9.2kcalmol -1 and -9.0kcalmol -1 ) respectively have the highest binding affinity when compared to the reference drug doxorubicin with binding energy (-6.8kcalmol -1 ). The stability and robustness of the built model showed that new anti-breast cancer agents can be design from these derivatives.

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Abechi Stephehe Eyije

Papers

QSAR and Molecular Docking Studies of novel thiophene, pyrimidine, coumarin, pyrazole and pyridine derivatives as Potential Anti-Breast Cancer Agent