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

Chakguy Prakasvudhisarn,Luckhana Lawtrakul,Peter Wolschann

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 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.

Highlights

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 -cyclodextrin
The polynomial kernel, with R2Training between 0.9991 and 0.9994, has better calibration correlation coefficients than the Gaussian radial basis functions (RBF) kernel, whereas the Gaussian RBF gives much better predictions than those obtained with the polynomial SVM
This work demonstrated that the combination of PSO and SVMs can be applied to effectively and efficiently select major features in quantitative structure-property relationships (QSPR) modeling of the thermodynamic parameters of 1:1 inclusion complexation of enantiomeric pairs of chiral guests with -CD

Summary

Introduction

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 -cyclodextrin. -cyclodextrin ( -CD) is a cyclic oligosaccharide that naturally contains seven glucose residues linked by (1-4)-glycosidic bonds, with a hydrophilic outer surface and a relative hydrophobic central cavity, which can form complexes with appropriate guest molecules. It has received increasing attention in the pharmaceutical field for modifying drug physicochemical properties, such as solubility, stability and bio-availability, reducing their toxicity and side effects, and suppressing unpleasant taste or smell [1,2]. The free energies of a larger dataset of compounds complexed with all natural CDs were considered using a molecular-size based model [12,13]. Not much has been done up to now for the prediction of interaction energies with modified CDs, or considering different CD derivatives

Objectives

Methods

Results

Conclusion