Abstract
The association constant (logK) of 53 chelates of new macrocycles (hemispherands, cryptahemispherands, and bridged calix-4) with sodium cation is predicted by a statistically validated QSPR modeling approach. The applied multiple linear regression is based on a variety of theoretical molecular descriptor selected from 6 classes of Dragon software with a forward stepwise multiple linear regression as a feature selection technique. For external validation we applied self organizing maps (SOM) to split the original data set into training and test set. The best four-dimensional model is developed on a training set of 40 macrocycles. The external validation was performed on test set of 13 macrocycles. The QSPR model presented in this study showed good predictions with the leave one out cross validated variance (Q 2 loo-cv = 0.94) and the external-validated variance (Q 2 ext = 0.92). The applicability domain (AD) of the model is analysed by leverage method.
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