Computational Toxicology Methods and Protocols /
| Institutionell upphovsman: | |
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| Övriga upphovsmän: | |
| Sammanfattning: | XIV, 445 p. 3 illus. text |
| Språk: | engelska |
| Publicerad: |
New York, NY :
Springer US : Imprint: Humana,
2025.
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| Upplaga: | 2nd ed. 2025. |
| Serie: | Methods in Molecular Biology,
2834 |
| Ämnen: | |
| Länkar: | https://doi.org/10.1007/978-1-0716-4003-6 |
| Materialtyp: | Elektronisk Bok |
Innehållsförteckning:
- QSAR: Using the Past to Study the Present
- Molecular similarity in predictive toxicology with a focus on the q-RASAR technique
- Weight of Evidence: criteria and applications
- Integration of QSAR and NAM in the Read Across process for an effective and relevant toxicological assessment
- Automated workflows for data curation and machine learning to develop Quantitative Structure-Activity Relationships
- Applicability Domain for Trustable Predictions
- The potential of molecular docking for predictive toxicology
- Computational toxicology methods in chemical library design and high-throughput screening hit validation
- Toxicity potential of nutraceuticals
- Development, use and validation of (Q)SARs for predicting genotoxicity and carcinogenicity: experiences from Italian National Institute of Health activities
- Adverse outcome pathways mechanistically describing hepatotoxicity
- Machine learning in early prediction of metabolism of drugs
- In vitro cell-based MTT and Crystal Violet assays for drug toxicity screening
- Recent advances in nanodrug delivery systems production, efficacy, safety and toxicity
- Investigating the benefit-risk profile of drugs: from spontaneous reporting systems to real word data for pharmacovigilance
- MolPredictX – a Pioneer Mobile App Version for Online Biological Activity Predictions by Machine Learning Models
- TIRESIA and TISBE, explainable artificial intelligence based web platforms for the transparent assessment of the developmental toxicity of chemicals and drugs
- PFAS-Biomolecule Interactions: Case Study Using Asclepios Nodes and automated Workflows in KNIME for Drug Discovery and Toxicology.