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The impact of variable selection on the modelling of oestrogenicity
journal contribution
posted on 2023-06-09, 03:26 authored by Taravat Ghafourian, Mark T D CroninMany oestrogenic chemicals exert their activity via specific interactions with the oestrogen receptor (ER). The objective of the present study was to identify significant descriptors associated with the ER binding affinities of a large and diverse set of compounds to drive quantitative structure-activity relationships (QSARs). To this end, a variety of statistical methods were employed for variable selection. These included stepwise regression and partial least squares (PLS) analyses, as well as a non-linear recursive partitioning method (Formal Inference-based Recursive Modelling). A total of 157 molecular descriptors including quantum mechanical, graph theoretical, indicator variables and log P were used in the study. Furthermore, cluster analysis of variables was performed to identify groups of descriptors representing similar molecular features. Hierarchical PLS analyses were performed, where the scores of the significant components of either PLS or principle component analysis (PCA), performed separately on each cluster, were used as the variables for the top model. This reduced the number of the variables representing the larger clusters, leading to a similar number of descriptors for each distinct molecular feature. The results showed that the most important molecular properties for stronger ER binding affinity are molecular size and shape, the presence of a phenol moiety as well as other aromatic groups, hydrophobicity and presence of double bonds. The best PLS model obtained, in terms of predictive ability, was a hierarchical PLS model. However, a rigorous validation study showed that the MLR model using descriptors selected by stepwise regression has greater predictive power than the PLS models. © 2005 Taylor & Francis Ltd.
History
Publication status
- Published
File Version
- Accepted version
Journal
SAR and QSAR in Environmental ResearchISSN
1062-936XPublisher
Taylor & FrancisExternal DOI
Issue
1-2Volume
16Page range
171-190Department affiliated with
- Biochemistry Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2017-11-30First Compliant Deposit (FCD) Date
2017-11-30Usage metrics
Categories
No categories selectedKeywords
estrogen; estrogen receptorbiological model; chemical structure; cluster analysis; comparative study; conference paper; metabolism; pollutant; principal component analysis; quantitative structure activity relation; regression analysis; reproducibilityCluster Analysis; Environmental Pollutants; Estrogens; Least-Squares Analysis; ModelsBiological; Molecular Structure; Principal Component Analysis; Quantitative Structure-Activity Relationship; ReceptorsEstrogen; Reproducibility of Results
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