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