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 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.
Prakasvudhisarn
Chakguy
C
School of Technology, Shinawatra University, Shinawatra Tower III, 15th floor, 1010 Viphavadi Rangsit Road, Chatuchak, Bangkok, 10900, Thailand.
Wolschann
Peter
P
Institute of Theoretical Chemistry, University of Vienna, Währinger Straβe 17, Vienna, 1090, Austria.
Lawtrakul
Luckhana
L
0000-0003-1104-2700
Sirindhorn International Institute of Technology (SIIT), Thammasat University, P.O.Box 22 Thammasat Rangsit Post Office, Pathum Thani, 12121, Thailand.
D000465:Algorithms; D015195:Drug Design; D008962:Models, Theoretical; D062105:Molecular Docking Simulation; D021281:Quantitative Structure-Activity Relationship; D060388:Support Vector Machine; D013816:Thermodynamics; D047392:beta-Cyclodextrins / Q000737:chemistry / Q000378:metabolism*
D047392:beta-Cyclodextrins; C031215:betadex