Ranking strategies to support toxicity prediction: A case study on potential LXR binders
- Submitting institution
-
The University of Bradford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 15
- Type
- D - Journal article
- DOI
-
10.1016/j.comtox.2019.01.004
- Title of journal
- Computational Toxicology
- Article number
- -
- First page
- 130
- Volume
- 10
- Issue
- -
- ISSN
- 2468-1113
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2019
- URL
-
https://www.sciencedirect.com/science/article/abs/pii/S2468111318301026?via%3Dihub
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This research develops a priority setting strategy, by means of in silico approaches and chemometric tools, for the screening and ranking of chemicals according to their toxicity potential. The paper reports contributions to methodologies and processes developed in the flagship EC SEURAT-1 project COSMOS for cosmetics safety assessment, proposing sustainable in silico computational methods for cosmetics safety assessment, to rank chemicals based on their potential binding to Liver X Receptors (LXR). The paper progresses the innovative COSMOS predictive toxicity data base and computational models as internationally renowned references for industry including Cosmetics Europe, regulatory bodies, SMEs, pharma, and research groups.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
