Predicting Complexation Thermodynamic Parameters of β-Cyclodextrin with Chiral Guests by Using Swarm Intelligence and Support Vector Machines

نویسندگان

  • Chakguy Prakasvudhisarn
  • Peter Wolschann
  • Luckhana Lawtrakul
چکیده

The Particle Swarm Optimization (PSO) and Support Vector Machines (SVMs) approaches are used for predicting the thermodynamic parameters for the 1:1 inclusion complexation of chiral guests with beta-cyclodextrin. A PSO is adopted for descriptor selection in the quantitative structure-property relationships (QSPR) of a dataset of 74 chiral guests due to its simplicity, speed, and consistency. The modified PSO is then combined with SVMs for its good approximating properties, to generate a QSPR model with the selected features. Linear, polynomial, and Gaussian radial basis functions are used as kernels in SVMs. All models have demonstrated an impressive performance with R(2) higher than 0.8.

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عنوان ژورنال:
  • International Journal of Molecular Sciences

دوره 10  شماره 

صفحات  -

تاریخ انتشار 2009