Lazar (Lazy Structure-Activity Relationships ) takes a chemical structure as input and provides predictions for a variety of toxic properties. Lazar uses an automated and reproducible read across procedure to calculate predictions. Rationales for predictions, applicability domain estimations and validation results are presented in a clear graphical interface for the critical examination by toxicological experts.OpenRiskNet provides resources to enable users to install their own virtual infrastructures populated with the applications and middleware making up the virtual research environment on public or private cloud resources, as well as in-house server/workstations. The reference environment (https://home.prod.openrisknet.org/) is provided as a quick entry point to test the OpenRiskNet features.Case studies have been defined to test and evaluate the solutions provided by OpenRiskNet to the predictive toxicology and risk assessment community especially regarding the usability of the developed APIs and the interoperability layer. These demonstrate the capabilities to satisfy the requirements of the different stakeholder groups including researchers, risk assessors and regulators and present real-world applications like systems biology approaches for grouping compounds, read-across applications using chemical and biological similarity, and identifying areas of concern based on in vitro and in silico approaches for compounds lacking any previous knowledge from animal experiments: 1) Data curation and creation of pre-reasoned datasets and searching, 2) Modelling for Prediction or Read Across, 3) A systems biology approach for grouping compounds, 4) Metabolism Prediction, 5) Identification and Linking of Data related to AOPWiki, 6) Toxicogenomics-based prediction and mechanism identification, 6) Reverse dosimetry and PBPK prediction.
Engineering & Technology
- Chemical Engineering
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