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Received: 15 February 2020 Accepted: 26 May 2020 Pesticide use in agriculture can cause undesirable effects on humans and the natural environment. Physicochemical properties of pesticides play an important role in determining its distribution and fate in the environment. Chemometric methods can be used to describe how the physicochemical properties vary according to the characteristics of the molecular structure expressed in terms of appropriate molecular descriptors. Quantitative Structure-Property Relationship (QSPR) models can also provide a general overview of the molecular structure that influences these properties. Henry’s law constant (H) is an important property for predicting the solubility and vaporliquid equilibrium of pesticides. Genetic algorithm/ multilinear hybrid approach was used to model the log H of 48 pesticides belonging to four chemical classes: ureas, triazines, carbamates and aryloxyalkanoic acids. The 5 explanatory variables model selected is robust and has good fitness and good predictive ability. Two influential points which reinforce the model and an outlier were highlighted. The model can be used to predict the Henry’s law constant of pesticides falling in the applicability domain of our model.

Modeling of the Henry Constant of a Series of Pesticides: Quantitative Structure-Property Relationship Approach