Literature DB >> 19504577

QSPR modelling with the topological substructural molecular design approach: beta-cyclodextrin complexation.

Alfonso Pérez-Garrido1, Aliuska Morales Helguera, M Natália D S Cordeiro, Amalio Garrido Escudero.   

Abstract

This study aims at developing a quantitative structure-property relationship (QSPR) model for predicting complexation with beta-cyclodextrins (beta-CD) based on a large variety of organic compounds. Molecular descriptors were computed following the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach and correlated with beta-CD complex stability constants by linear multivariate data analysis. This strategy afforded a final QSPR model that was able to explain around 86% of the variance in the experimental activity, along with showing good internal cross-validation statistics, and also good predictivity on external data. Topological substructural information influencing the complexation with beta-CD was extracted from the QSPR model. This revealed that the major driving forces for complexation are hydrophobicity and van der Waals interactions. Therefore, the presence of hydrophobic groups (hydrocarbon chains, aryl groups, etc.) and voluminous species (Cl, Br, I, etc.) in the molecules renders easy their complexity with beta-CDs. To our knowledge, this is the first time a correlation between TOPS-MODE descriptors and complexing abilities of beta-CDs has been reported. 2009 Wiley-Liss, Inc. and the American Pharmacists Association

Entities:  

Mesh:

Substances:

Year:  2009        PMID: 19504577     DOI: 10.1002/jps.21747

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  2 in total

1.  How to simulate affinities for host-guest systems lacking binding mode information: application to the liquid chromatographic separation of hexabromocyclododecane stereoisomers.

Authors:  Vedat Durmaz; Marcus Weber; Roland Becker
Journal:  J Mol Model       Date:  2011-10-12       Impact factor: 1.810

2.  Predicting complexation performance between cyclodextrins and guest molecules by integrated machine learning and molecular modeling techniques.

Authors:  Qianqian Zhao; Zhuyifan Ye; Yan Su; Defang Ouyang
Journal:  Acta Pharm Sin B       Date:  2019-05-08       Impact factor: 11.413

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.