Development of machine learning models of β-cyclodextrin and sulfobutylether-β-cyclodextrin complexation free energies


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Abstract

A new set of 142 experimentally determined complexation constants between sulfobutylether-β-cyclodextrin and diverse organic guest molecules, and 78 observations reported in literature, were used for the development of the QSPR models by the two machine learning regression methods – Cubist and Random Forest. Similar models were built for β-cyclodextrin using the 233-compound dataset available in the literature. These results demonstrate that the machine learning regression methods can successfully describe the complex formation between organic molecules and β-cyclodextrin or sulfobutylether-β-cyclodextrin. In particular, the root mean square errors for the test sets predictions by the best models are low, 1.9 and 2.7 kJ/mol, respectively. The developed QSPR models can be used to predict the solubilizing effect of cyclodextrins and to help prioritizing experimental work in drug discovery.

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