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 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.

[1]  P. Buchwald.  Complexation Thermodynamics of Cyclodextrins in the Framework of a Molecular Size-Based Model for Nonassociative Organic Liquids That Includes a Modified Hydration-Shell Hydrogen-Bond Model for Water , 2002 .

[2]  Takahiro Suzuki,et al.  A Nonlinear Group Contribution Method for Predicting the Free Energies of Inclusion Complexation of Organic Molecules with - and -Cyclodextrins , 2001, J. Chem. Inf. Comput. Sci..

[3]  David Hartsough,et al.  Toward an Optimal Procedure for Variable Selection and QSAR Model Building , 2001, J. Chem. Inf. Comput. Sci..

[4]  D. Agrafiotis,et al.  Feature selection for structure-activity correlation using binary particle swarms. , 2002, Journal of medicinal chemistry.

[5]  M Karplus,et al.  Evolutionary optimization in quantitative structure-activity relationship: an application of genetic neural networks. , 1996, Journal of medicinal chemistry.

[6]  Q. Guo,et al.  Association Constant Prediction for the Inclusion of α-Cyclodextrin with Benzene Derivatives by an Artificial Neural Network , 1999 .

[7]  J. Alderete,et al.  The role of charge transfer interactions in the inclusion complexation of anionic guests with α-cyclodextrin , 2005 .

[8]  Peter C. Jurs,et al.  Automated Descriptor Selection for Quantitative Structure-Activity Relationships Using Generalized Simulated Annealing , 1995, J. Chem. Inf. Comput. Sci..

[9]  Y L Loukas,et al.  Quantitative structure-binding relationships (QSBR) and artificial neural networks: improved predictions in drug:cyclodextrin inclusion complexes. , 2001, International journal of pharmaceutics.

[10]  Christian Th. Klein,et al.  A Method for Predicting the Free Energies of Complexation between β-Cyclodextrin and Guest Molecules , 2000 .

[11]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[12]  P. Mura,et al.  Computer-aided molecular modeling techniques for predicting the stability of drug–cyclodextrin inclusion complexes in aqueous solutions , 2002 .

[13]  D. Kalonia,et al.  Development of Improved Empirical Models for Estimating the Binding Constant of a β-Cyclodextrin Inclusion Complex , 2008, Pharmaceutical Research.

[14]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[15]  Thorsten Joachims,et al.  Making large-scale support vector machine learning practical , 1999 .

[16]  Christian Th. Klein,et al.  Predicting the Free Energies of Complexation Between Cyclodextrins and Guest Molecules: Linear versus Nonlinear Models , 2004, Pharmaceutical Research.

[17]  Takahiro Suzuki,et al.  Classical QSAR and comparative molecular field analyses of the host-guest interaction of organic molecules with cyclodextrins , 2000, J. Comput. Aided Mol. Des..

[18]  J. Szejtli.  Introduction and General Overview of Cyclodextrin Chemistry. , 1998, Chemical reviews.

[19]  Dan C. Fara,et al.  Quantitative Structure—Property Relationship Modeling of β-Cyclodextrin Complexation Free Energies. , 2004 .

[20]  Computational Prediction for the Slopes of AL-type Phase Solubility Curves of Organic Compounds Complexed With α-, β-, or γ-cyclodextrins Based on Monte Carlo Docking Simulations , 2006 .

[21]  Yoshihisa Inoue,et al.  Chiral Recognition Thermodynamics of β-Cyclodextrin: The Thermodynamic Origin of Enantioselectivity and the Enthalpy−Entropy Compensation Effect , 2000 .

[22]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[23]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[24]  Luwei Zhao,et al.  Predicting solubility in multiple nonpolar drugs-cyclodextrin system. , 2002, Journal of pharmaceutical sciences.

[25]  Chakguy Prakasvudhisarn,et al.  Correlation Studies of HEPT Derivatives Using Swarm Intelligence and Support Vector Machines , 2005 .

[26]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[27]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[28]  Kimito Funatsu,et al.  GA Strategy for Variable Selection in QSAR Studies: GA-Based PLS Analysis of Calcium Channel Antagonists , 1997, J. Chem. Inf. Comput. Sci..

[29]  Dan C. Fara,et al.  Quantitative Structure-Property Relationship Modeling of beta-Cyclodextrin Complexation Free Energies , 2004, J. Chem. Inf. Model..

[30]  Kenny B. Lipkowitz,et al.  Applications of Computational Chemistry to the Study of Cyclodextrins. , 1998, Chemical reviews.

[31]  Seunho Jung,et al.  Preference Prediction for the Stable Inclusion Complexes between Cyclodextrins and Monocyclic Insoluble Chemicals Based on Monte Carlo Docking Simulations , 2006 .

[32]  Ernesto Estrada,et al.  Combination of 2D-, 3D-Connectivity and Quantum Chemical Descriptors in QSPR. Complexation of - and -Cyclodextrin with Benzene Derivatives , 2001, J. Chem. Inf. Comput. Sci..