fulltext.study @t Gmail

Carcinogenicity prediction of noncongeneric chemicals by augmented top priority fragment classification

Paper ID Volume ID Publish Year Pages File Format Full-Text
14939 1362 2016 10 PDF Available
Title
Carcinogenicity prediction of noncongeneric chemicals by augmented top priority fragment classification
Abstract

•The prediction of chemical carcinogenicity using theoretical models is examined.•A non-congeneric dataset is analysed.•A blind approach to modelling carcinogenicity is used warranting an unbiased result.•A comparison with available models is performed.•A discussion concerning the prediction reliability is presented.

Carcinogenicity prediction is an important process that can be performed to cut down experimental costs and save animal lives. The current reliability of the results is however disputed. Here, a blind exercise in carcinogenicity category assessment is performed using augmented top priority fragment classification. The procedure analyses the applicability domain of the dataset, allocates in clusters the compounds using a leading molecular fragment, and a similarity measure. The exercise is applied to three compound datasets derived from the Lois Gold Carcinogenic Database. The results, showing good agreement with experimental data, are compared with published ones. A final discussion on our viewpoint on the possibilities that the carcinogenicity modelling of chemical compounds offers is presented.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slide

Keywords
Carcinogen classes; Functional groups; Molecular fragments; Structural alerts; Structure–activity relationships; Carcinogenicity prediction
First Page Preview
Carcinogenicity prediction of noncongeneric chemicals by augmented top priority fragment classification
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us
Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 61, April 2016, Pages 145–154
Authors
, ,
Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us