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

[Display omitted] 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 int...

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Bibliographic Details
Published in:International journal of pharmaceutics 2015-11, Vol.495 (1), p.404-409
Main Authors: Veselinović, Aleksandar M., Veselinović, Jovana B., Toropov, Andrey A., Toropova, Alla P., Nikolić, Goran M.
Format: Article
Language:English
Subjects:
beta-Cyclodextrins - chemistry
Computer Simulation
CORAL software
Models, Molecular
Molecular Structure
Monte Carlo Method
QSAR/QSPR
Quantitative Structure-Activity Relationship
Reproducibility of Results
SMILES
β-Cyclodextrin
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Summary:[Display omitted] 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.
ISSN:0378-5173
1873-3476
DOI:10.1016/j.ijpharm.2015.08.078