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

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

1
School of Technology, Shinawatra University, Shinawatra Tower III, 15th floor, 1010 Viphavadi Rangsit Road, Chatuchak, Bangkok, 10900, Thailand
2
Institute of Theoretical Chemistry, University of Vienna, Währinger Straβe 17, Vienna, 1090, Austria
3
Sirindhorn International Institute of Technology (SIIT), Thammasat University, P.O.Box 22 Thammasat Rangsit Post Office, Pathum Thani, 12121, Thailand
*
Author to whom correspondence should be addressed.
Received: 17 April 2009 / Accepted: 6 May 2009 / Published: 14 May 2009
(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)
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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 β-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 R2 higher than 0.8.
Keywords: Particle Swarm Optimization; Support Vector Machines; QSPR; β-cyclo-dextrin inclusion complexes Particle Swarm Optimization; Support Vector Machines; QSPR; β-cyclo-dextrin inclusion complexes
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Prakasvudhisarn, C.; Wolschann, P.; Lawtrakul, L. Predicting Complexation Thermodynamic Parameters of β-Cyclodextrin with Chiral Guests by Using Swarm Intelligence and Support Vector Machines. Int. J. Mol. Sci. 2009, 10, 2107-2121.

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