Predicting complexation thermodynamic parameters of β-cyclodextrin with chiral guests by using swarm intelligence and support vector machines

Int J Mol Sci. 2009 May 14;10(5):2107-2121. doi: 10.3390/ijms10052107.

Abstract

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.

Keywords: Particle Swarm Optimization; QSPR; Support Vector Machines; β-cyclodextrin inclusion complexes.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Drug Design*
  • Models, Theoretical
  • Molecular Docking Simulation
  • Quantitative Structure-Activity Relationship*
  • Support Vector Machine*
  • Thermodynamics
  • beta-Cyclodextrins / chemistry
  • beta-Cyclodextrins / metabolism*

Substances

  • beta-Cyclodextrins
  • betadex