In silico prediction of the β-cyclodextrin complexation based on Monte Carlo method

International Journal of Pharmaceutics
Aleksandar M VeselinovićGoran M Nikolić

Abstract

In this study QSPR models were developed to predict the complexation of structurally diverse compounds with β-cyclodextrin based on SMILES notation optimal descriptors using Monte Carlo method. The predictive potential of the applied approach was tested with three random splits into the sub-training, calibration, test and validation sets and with different statistical methods. Obtained results demonstrate that Monte Carlo method based modeling is a very promising computational method in the QSPR studies for predicting the complexation of structurally diverse compounds with β-cyclodextrin. The SMILES attributes (structural features both local and global), defined as molecular fragments, which are promoters of the increase/decrease of molecular binding constants were identified. These structural features were correlated to the complexation process and their identification helped to improve the understanding for the complexation mechanisms of the host molecules.

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Citations

Feb 7, 2016·Environmental Toxicology and Pharmacology·Alla P ToropovaAndrey A Toropov
Feb 17, 2017·Chemical Biology & Drug Design·Azizeh AbdolmalekiJahan B Ghasemi

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