Computational Toxicology Methods and Protocols /

书目详细资料
企业作者: SpringerLink (Online service)
其他作者: Nicolotti, Orazio (Editor)
总结:XIV, 445 p. 3 illus.
text
语言:英语
出版: New York, NY : Springer US : Imprint: Humana, 2025.
版:2nd ed. 2025.
丛编:Methods in Molecular Biology, 2834
主题:
在线阅读:https://doi.org/10.1007/978-1-0716-4003-6
格式: 电子 电子书
书本目录:
  • 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.