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

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

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 β-cyclodextrin. A PSO is adopted for descriptor selection in the quantitative structure-property relations...

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Bibliographic Details
Main Authors: Luckhana Lawtrakul, Peter Wolschann, Chakguy Prakasvudhisarn
Format: Article
Language:English
Published: MDPI AG 2009-05-01
Series:International Journal of Molecular Sciences
Subjects:
Particle Swarm Optimization
Support Vector Machines
QSPR
β-cyclo-dextrin inclusion complexes
Online Access:http://www.mdpi.com/1422-0067/10/5/2107/

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