Literature DB >> 14502475

On the physical interpretation of QSAR models.

David T Stanton1.   

Abstract

Multidimensional quantitative structure-activity models (QSAR) developed using molecular structure descriptors and regression analysis techniques have found wide utility and acceptance. However, it is often difficult to extract a physical interpretation of such models because of the types of descriptors involved and the multidimensional nature of the model. The work described here illustrates a method of model interpretation that employs partial least squares (PLS) analysis. Structure-activity relationship information is derived from the positions of specific sets of structures in the PLS score plots and the weights for each variable in the PLS components. Using these data, information regarding major structure-activity trends, trend exceptions, and unique or outlying observations is easily obtained. Examples of this methodology are illustrated using QSAR equations developed for the inhibition of quinolone-resistant bacterial DNA gyrase and human topoisomerase-II inhibition by a series of quinolone antibacterial agents.

Entities:  

Mesh:

Substances:

Year:  2003        PMID: 14502475     DOI: 10.1021/ci0340658

Source DB:  PubMed          Journal:  J Chem Inf Comput Sci        ISSN: 0095-2338


  11 in total

1.  On the importance of topological descriptors in understanding structure-property relationships.

Authors:  David T Stanton
Journal:  J Comput Aided Mol Des       Date:  2008-03-13       Impact factor: 3.686

2.  On the interpretation and interpretability of quantitative structure-activity relationship models.

Authors:  Rajarshi Guha
Journal:  J Comput Aided Mol Des       Date:  2008-09-11       Impact factor: 3.686

3.  QSAR models for predicting the activity of non-peptide luteinizing hormone-releasing hormone (LHRH) antagonists derived from erythromycin A using quantum chemical properties.

Authors:  Michael Fernández; Julio Caballero
Journal:  J Mol Model       Date:  2007-01-10       Impact factor: 1.810

4.  www.3d-qsar.com: a web portal that brings 3-D QSAR to all electronic devices-the Py-CoMFA web application as tool to build models from pre-aligned datasets.

Authors:  Rino Ragno
Journal:  J Comput Aided Mol Des       Date:  2019-10-08       Impact factor: 3.686

5.  Modeling of peroxide activation in artemisinin derivatives by serial docking.

Authors:  Roy J Little; Alexis A Pestano; Zaida Parra
Journal:  J Mol Model       Date:  2009-01-14       Impact factor: 1.810

6.  Interpretable correlation descriptors for quantitative structure-activity relationships.

Authors:  Benson M Spowage; Craig L Bruce; Jonathan D Hirst
Journal:  J Cheminform       Date:  2009-12-24       Impact factor: 5.514

Review 7.  Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction.

Authors:  Haiyan Li; Jin Sun; Xiaowen Fan; Xiaofan Sui; Lan Zhang; Yongjun Wang; Zhonggui He
Journal:  J Comput Aided Mol Des       Date:  2008-06-24       Impact factor: 3.686

Review 8.  On exploring structure-activity relationships.

Authors:  Rajarshi Guha
Journal:  Methods Mol Biol       Date:  2013

9.  A survey of quantitative descriptions of molecular structure.

Authors:  Rajarshi Guha; Egon Willighagen
Journal:  Curr Top Med Chem       Date:  2012       Impact factor: 3.295

10.  Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity.

Authors:  Samuel J Webb; Thierry Hanser; Brendan Howlin; Paul Krause; Jonathan D Vessey
Journal:  J Cheminform       Date:  2014-03-25       Impact factor: 5.514

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.