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

被引：12

作者：

Prakasvudhisarn, Chakguy ^{[2
]}

Wolschann, Peter ^{[3
]}

Lawtrakul, Luckhana ^{[1
]}

机构：

[1] Thammasat Univ, SIIT, Pathum Thani 12121, Thailand

[2] Shinawatra Univ, Sch Technol, Bangkok 10900, Thailand

[3] Univ Vienna, Inst Theoret Chem, A-1090 Vienna, Austria

来源：

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES | 2009年 / 10卷 / 05期

关键词：

Particle Swarm Optimization; Support Vector Machines; QSPR; beta-cyclodextrin inclusion complexes; FREE-ENERGIES; INCLUSION COMPLEXATION; VARIABLE SELECTION; ORGANIC-MOLECULES; QSAR; MODEL; DRUG;

D O I：

10.3390/ijms10052107

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：2107 / 2121

页数：15

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