Global and Local PLS Regression Models to Predict Vapor Pressure

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 1,340 organic compounds. Theoretical 2 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, Q 2 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.