Using quantitative structure–activity relationship modeling to quantitatively predict the developmental toxicity of halogenated azole compounds
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Summary
The good predictability of the model suggests that this method may be applicable to the analysis of qualifying compounds whenever developmental toxicity information is lacking or incomplete for risk assessment considerations.
- Type
- article
- Published
- 2014-07-01
- Cited by
- 4
- References
- 25
- OpenAlex
- https://openalex.org/W1886577262
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:25202921
Keywords
Developmental toxicity, Quantitative structure–activity relationship, Toxicity, Applicability domain, Similarity (geometry)
References
- Review of QSAR Models and Software Tools for predicting Developmental and Reproductive Toxicity
- The Toxicity Data Landscape for Environmental Chemicals
- Azole fungicides: occurrence and fate in wastewater and surface waters.
- Integrating (Q)SAR models, expert systems and read-across approaches for the prediction of developmental toxicity.
- Developmental toxicity testing for safety assessment: new approaches and technologies.
- Application of computational toxicological approaches in human health risk assessment. I. A tiered surrogate approach.
- Predictive QSAR modeling workflow, model applicability domains, and virtual screening.
- (Q)SARS: gatekeepers against risk on chemicals?
- Beware of q2!
- Comparison of Different Approaches to Define the Applicability Domain of QSAR Models
- Best Practices for QSAR Model Development, Validation, and Exploitation
- Prediction of Acute Mammalian Toxicity Using QSAR Methods: A Case Study of Sulfur Mustard and Its Breakdown Products
- CAESAR models for developmental toxicity
- Current and future needs for developmental toxicity testing.
- Integrated risk information system (IRIS)
- The acceptance of in silico models for REACH: Requirements, barriers, and perspectives
- Beware of q 2
- Guidelines for Developmental Toxicity Risk Assessment
- Alternatives to animal testing: research, trends, validation, regulatory acceptance.
Cited by
- Ethics of animal research in human disease remediation, its institutional teaching; and alternatives to animal experimentation
- In silico prediction of drug‐induced rhabdomyolysis with machine‐learning models and structural alerts
- A QSAR study for predicting malformation in zebrafish embryo
- In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
- An Introduction to the Basic Concepts in QSAR-Aided Drug Design
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