E-grāmata: Computational Toxicology: Methods and Protocols

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.