Quantitative structure–activity relationship (QSAR) analysis of plant-derived compounds with larvicidal activity against Zika Aedes aegypti (Diptera: Culicidae) vector using freely available descriptors

Abstract

BACKGROUND: We have developed a quantitative structure?activity relationship (QSAR) model for predicting the larvicidal activity of 60 plant-derived molecules against Aedes aegypti L. (Diptera: Culicidae), a vector of several diseases such as dengue, yellow fever, chikungunya and Zika. The balanced subsets method (BSM) based on k-means cluster analysis (k-MCA) was employed to split the data set. The replacement method (RM) variable subset selection technique coupled with multivariable linear regression (MLR) proved to be successful for exploring 18 326 molecular descriptors and fingerprints calculated with PaDEL, Mold2 and EPI Suite open-source softwares. RESULTS: A robust QSAR model (R2, Strain = 0.20 and R2, Stest = 0.23) involving five non-conformational descriptors was established. The model was validated and tested through the use of an external test set of compounds, the leave-one-out (LOO) and leave-more-out (LMO) cross-validation methods, Y-randomization and applicability domain (AD) analysis. CONCLUSION: The QSAR model surpasses previously published models based on geometrical descriptors, thereby representing a suitable tool for predicting larvicidal activity against the vector A. aegypti using a conformation-independent approach.