Instead of using the standard molecular descriptors (topological indices) for regression analysis, which are numerically fully determined once a molecule is selected, we outline the use of variable molecular descriptors that are modified during the search for the best regression. The approach is illustrated using boiling points of sulfides. We have transformed the connectivity index 1χ into a function of two variables (x, y) which differentiate carbon and sulfur atoms. The optimal values of the variables (x, y) were determined by minimizing the standard error of the regression. With the values x = +0.25 and y = -0.95 for carbon and sulfur, respectively, we have obtained a regression based on a single descriptor and a standard error of 1.8 °C. With elimination of two outliers (having a deviation of about 4 °C) the standard error is reduced to a remarkable 1.3 °C.
|Original language||English (US)|
|Number of pages||7|
|Journal||Journal of chemical information and computer sciences|
|State||Published - Jan 1 2000|