Development of QSAR model for predicting the inclusion constants of organic chemicals with α-cyclodextrin

Wei, Mengbi; Yang, Xianhai; Watson, Peter; Yang, Feifei; Liu, Huihui

Development of QSAR model for predicting the inclusion constants of organic chemicals with α-cyclodextrin

2018

Wei, Mengbi | Yang, Xianhai | Watson, Peter | Yang, Feifei | Liu, Huihui

Solubility is a crucial limiting factor in pharmaceutical research and contaminated site remediation. Cyclodextrin, with its structure of hydrophilic exterior and hydrophobic cavity, has a potential ability to enhance the hydrophobic chemical’s solubility through the formation of host–guest complex. The stability of host–guest complex is often quantified by the inclusion constant. In this study, the logarithm of 1:1 α-cyclodextrin inclusion constants (log Kα) for 195 organic chemicals was collected. With this parameter as the endpoint, a quantitative structure–activity relationship (QSAR) model was developed using DRAGON descriptors and stepwise multiple linear regression analysis. The model statistics parameters indicated that the established model had a good determination coefficient of 0.857, a high cross-validation coefficient of 0.835, a low root mean square error of 0.380, together with the acceptable results of external validation, which indicate a satisfactory goodness-of-fit, robustness, and predictive ability of the model. Based on the screened eight descriptors, we propose an appropriate mechanism interpretation for the inclusion interaction. Additionally, the applicability domain of the current model was characterized by the Euclidean distance-based method and Williams plot, and results indicated that the model covered a large number of structurally diverse chemicals belonging to 13 different classes. Comparing with the previous reported models, this model has obvious advantages with a larger dataset, a higher value of correlation coefficient, and a wider application domain.

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