Back to Search
Start Over
Automated and standardized workflows in the OECD QSAR Toolbox
- Source :
- Computational Toxicology. 10:89-104
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- Toxicological understanding and computational possibilities progress every year. The OECD QSAR Toolbox, an in silico system for predicting toxicity of chemicals, is regularly updated with up to data knowledge and functionalities. This expansion has made the Toolbox increasingly complex and difficult to use for non-experts. Recent activities addressed this issue and enhanced the user-friendliness of the system. In a streamlining exercise, we developed automated and standardized workflows for acute aquatic toxicity and skin sensitization. These endpoints have been chosen based on understanding of the mechanisms of action, availability of experimental data and regulatory relevance. The workflows are designed to produce rational and reliable predictions, or no prediction if acceptance criteria are not met. The automated workflow is a predefined algorithm that generates a prediction without further interaction with the user after activation. The standardised workflow uses a similar algorithm but leaves to the user the key choices among a selection of suitable options. In the present work, we described in detail the algorithms for both the automated and standardized workflows. Using examples, the rationale behind each step of the two processes is explained.
- Subjects :
- 0303 health sciences
Quantitative structure–activity relationship
Computer science
business.industry
Health, Toxicology and Mutagenesis
Skin sensitization
Experimental data
010501 environmental sciences
Toxicology
01 natural sciences
Toolbox
Computer Science Applications
03 medical and health sciences
Workflow
Acceptance testing
Key (cryptography)
Relevance (information retrieval)
Software engineering
business
030304 developmental biology
0105 earth and related environmental sciences
Subjects
Details
- ISSN :
- 24681113
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- Computational Toxicology
- Accession number :
- edsair.doi...........00fa42f8ed3b4a8c6f9f270fb4ab7a98
- Full Text :
- https://doi.org/10.1016/j.comtox.2019.01.006