Determination of lithium cation basicity from molecular structure.

Abstract

A quantitative structure-property relationship (QSPR) is developed to calculate the Lithium Cationic Basicity (LCB) of a large set of 229 compounds, of very different chemical nature. The proposed models derived from multiple linear regression analysis (MLRA) and computational neural networks (CNN) contain seven descriptors calculated solely from the molecular structure of compounds. The models were validated by an external prediction set. Good results were obtained from both methodologies, being the best those from CNN, that give a rms error of 6.54 (R2 = 0.954) and an average error of 3.57% for the training set; for the prediction set the rms error is 8.61 (R2 = 0.914) and the average error 4.39%. The models derived from the two approaches contain descriptors that belong to the same classes, constitutional and electrostatic. The comparison with the results obtained from high level theoretical methods shows that the values obtained from the QSPR approach are very similar and even better, especially when the sets compared are large and contain compounds of different chemical structure. These good results shows that, despite the complexity of Li+-base interactions, the proposed models contain descriptors which encode properly the characteristics of the molecules directly related to their gas-phase basicity against the Li cations.