Design of New Chlorochalcone Derivatives as Potential Breast Anticancer Compound Based on QSAR Analysis and Molecular Docking Study

Abstract

Quantitative structure-activity relationships (QSAR) proposes a model that relates the biological activities of drugs to their chemical structures, and the interaction between the drug and its target enzyme is revealed by molecular docking research. These studies were conducted on chalcone to produce a model that could design highly potent breast anticancer MCF7 cells. The compounds were optimized using ab initio using a basis set 6-31G, then their descriptors calculated using this method. Genetic Function Algorithm (GFA) was used to select descriptors and build the model. One of the six models generated was found to be the best with internal and external squared correlation coefficient (R2) of 0.743 and 0.744, respectively, adjusted squared correlation coefficient (adjusted R2) of 0.700, Standard estimate of error (SEE) of 0.198, Fcalc/Ftable of 6.423, and Predicted residual sum of squares (PRESS) of 1.177. The QSAR equation is pIC50 = 3.869 + (1.427 x qC1) + (4. 027 x qC10) + (0.856 x qC15) - (35.900 x ELUMO) + (0.208 x Log P). Hence, it can predict the breast anticancer activities of new chlorochalcones A-F. The compound with the best prediction was chlorochalcone A with pIC50 2.65 and IC50 value of 2.26 μM. The chlorochalcones A-F were able to bind to the main amino acid residues, namely Arg120 and Tyr355, on the active site of the COX-2 enzyme. These results could serve as a model for designing novel chlorochalcone as inhibitors of COX-2 with higher breast anticancer activities. Keywords: Chlorochalcone, COX-2, QSAR, MCF-7, Molecular docking