PURPOSE: In the present paper, linear and nonlinear models for complexation of alpha- beta- and gamma-cyclodextrin with guest molecules are developed, with the aim of free energy prediction and interpretation of the association process. METHODS: Linear and nonlinear regression is used to correlate experimental free energies of complexation with calculated molecular descriptors. Molecular modeling supports the interpretation of the results. RESULTS: Highly predictive models are obtained, although the structural variability of the compounds used for their deduction is large, reaching from synthetic heterocycles to steroids and prostaglandins. CONCLUSIONS: The scaled regression coefficients give insight to the complexation mechanisms, which appear to be different for the three types of cyclodextrins.
PURPOSE: In the present paper, linear and nonlinear models for complexation of alpha- beta- and gamma-cyclodextrin with guest molecules are developed, with the aim of free energy prediction and interpretation of the association process. METHODS: Linear and nonlinear regression is used to correlate experimental free energies of complexation with calculated molecular descriptors. Molecular modeling supports the interpretation of the results. RESULTS: Highly predictive models are obtained, although the structural variability of the compounds used for their deduction is large, reaching from synthetic heterocycles to steroids and prostaglandins. CONCLUSIONS: The scaled regression coefficients give insight to the complexation mechanisms, which appear to be different for the three types of cyclodextrins.