QSPR prediction of vapor pressure from solely theoretically-derived descriptors

To date, most reported quantitative structure-property relationship (QSPR) methods to predict vapor pressure rely on, at least, some empirical data, such as boiling points, critical pressures, and critical temperatures. This limits their usefulness to available chemicals and incurs the time and expense of experimentation. A model to predict vapor pressure from only computationally derived molecular descriptors, allowing study of hypothetical structures, is described here. Several multilinear regressions and artificial neural network analyses were tested with a range of descriptors (e.g., topological and quantum mechanical) derived solely from computations on molecular structure data. From a set of 479 compounds, a linear regression with an r(2) of 0.960 was achieved using polarizibility and polar functional group counts as descriptors. This new computationally based model also proves to be more accurate and works over a wider range of compound classes than most previously reported models.