NURA
NURA integrates toxicological and pharmacological bioactivity data to provide a curated dataset of 15,247 molecules annotated across 11 selected nuclear receptors for pharmacological and toxicological analyses.
Key Features:
- Data integration: Aggregates bioactivity annotations from Tox21, ChEMBL, NR-DBIND, and BindingDB.
- Coverage: Provides annotations for 15,247 molecules across 11 selected nuclear receptors.
- Enhanced data quality: Applies rigorous curation to increase the number of molecules, structural diversity, and covered atomic scaffolds relative to individual source databases.
- Bioactivity focus: Centralizes bioactivity annotations relevant to nuclear receptor modulation for both pharmacology and toxicology contexts.
- Support for data-driven methods: Supplies curated data suitable for machine learning and other predictive-modeling approaches.
Scientific Applications:
- Drug Discovery: Facilitates identification and characterization of novel compounds targeting nuclear receptors.
- Toxicological Risk Assessment: Supports evaluation of adverse effects associated with small molecules interacting with nuclear receptors.
- Research and Development: Provides a dataset for developing predictive models and conducting high-throughput screening studies linking toxicology and medicinal chemistry.
Methodology:
Integration of data from Tox21, ChEMBL, NR-DBIND, and BindingDB followed by rigorous curation and enrichment of molecular annotations, structural diversity, and atomic scaffolds to produce a curated bioactivity dataset suitable for machine learning and predictive modeling.
Topics
Details
- Tool Type:
- command-line tool
- Added:
- 1/18/2021
- Last Updated:
- 3/13/2021
Operations
Publications
Valsecchi C, Grisoni F, Motta S, Bonati L, Ballabio D. NURA: A curated dataset of nuclear receptor modulators. Toxicology and Applied Pharmacology. 2020;407:115244. doi:10.1016/j.taap.2020.115244. PMID:32961130.
PMID: 32961130
Funding: - Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung: 205321_182176