Larvicidal activity prediction of Essential oils against Culex pipiens pallens using QSAR Modeling

The search for an eco-freindly larvicide suitable for vector control requires a budget and considerable time to carry out experiments. Fortunately, the use of QSAR modeling allows the prediction of larvicidal activity of structurally diverse chemicals against mosquitoes in a way quick and costless. This approach can be helpful to study for making biolarvicide with highest ability to destroy mosquito larvae. We propose a QSAR model using two different statistical methods, multiple linear regression (MLR) and Support vector machine (SVM) for predicting the larvicidal activity of 30 compounds of essential oil (EOs) isolated from the root of Asarum heterotropoides against Culex pipiens pallens. A model with four theoretical descriptors derived from Dragon software was developed applying the genetic algorithm (GA)-variable subset selection (VSS) procedure. The statistical parameters, R² = 0.9716, Q²_LOO = 0.9595, s = 0.1690 of the model developed by MLR showed a good predictive capability for log LC₅₀ values. The comparison between the results of SVM and MLR models showed that the SVM model present a good alternative to construct a QSAR model for the prediction of the larvicidal activity.