Global and Local PLS Regression Models to Predict Vapor Pressure

Abstract

The vapor pressure is a key property in determining the distribution and fate of environmentally relevant compounds, but experimental determinations are only available for a limited number of the chemicals in current commercial use. Despite experimental efforts there is a need for estimation methods. The liquid or subcooled liquid vapor pressures at 298.15 K were collected from the literature for a diverse set of 1340 organic compounds. Theoretical molecular descriptors were derived after optimization to low-energy conformations and used to investigate the performance of global and local Quantitative Structure–Property Relationships (QSPR). A global PLSR model with ten latent variables was found to be optimal. The predictive performance of this model, within the domain of applicability, was estimated at n=420, Q²_Ext=0.980, and RMSEP=0.410 (log Pa). This model can be used in conjunction with other estimation models to assess the potential for a long range atmospheric transport.