| 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 derivedfrom multiple linear regression analysis (MLRA) and computational neural networks (CNN) contain sevendescriptors calculated solely from the molecular structure of compounds. The models were validated by anexternal prediction set. Good results were obtained from both methodologies, being the best those fromCNN, that give a rms error of 6.54 (R2 = 0.954) and an average error of 3.57% for the training set; for theprediction set the rms error is 8.61 (R2 = 0.914) and the average error 4.39%. The models derived from thetwo approaches contain descriptors that belong to the same classes, constitutional and electrostatic. Thecomparison with the results obtained from high level theoretical methods shows that the values obtainedfrom the QSPR approach are very similar and even better, especially when the sets compared are large andcontain compounds of different chemical structure. These good results shows that, despite the complexityof Li+-base interactions, the proposed models contain descriptors which encode properly the characteristicsof the molecules directly related to their gas-phase basicity against the Li cations.
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