Quantitative structure-property relationship studies for the prediction of the vapor pressure of volatile organic compounds

Abstract

A theoretical model (QSPR) using multiple linear regression analysis for predicting the vapor pressure (pv) of volatile organic compounds (VOCs) has been developed. A series of 51 compounds were analyzed by multiple linear regression analysis. First, the data set was separated arbitrarily into a training set (39 chemicals) and a test set (12 chemicals) for statistical external validation. A four-dimensional model was developed using as independent variables theoretical descriptors derived from Dragon software when applying the GA (genetic algorithm)–VSS (variable subset selection) procedure. The obtained model was used to predict the vapor pressure of the test set compounds, and an agreement between experimental and predicted values was verified. This model, with high statistical significance (R2 = 0.9090, Q2 LOO = 0.8748, Q2 ext = 0.8307, s = 0.24), could be used adequately for the prediction and description of the log pv value of other VOCs. The applicability domain of MLR model was investigated using a William‘s plot to detect outliers and outsides compounds.

Keywords

• molecular descriptors
• vocs
• log pv
• multiple linear regression