NURA: A curated dataset of nuclear receptor modulators

Cecile Valsecchi, Francesca Grisoni, Stefano Motta, Laura Bonati, Davide Ballabio

Research output: Contribution to journalArticleAcademicpeer-review


Nuclear receptors (NRs) are key regulators of human health and constitute a relevant target for medicinal chemistry applications as well as for toxicological risk assessment. Several open databases dedicated to small molecules that modulate NRs exist; however, depending on their final aim (i.e., adverse effect assessment or drug design), these databases contain a different amount and type of annotated molecules, along with a different distribution of experimental bioactivity values. Stemming from these considerations, in this work we aim to provide a unified dataset, NURA (NUclear Receptor Activity) dataset, collecting curated information on small molecules that modulate NRs, to be intended for both pharmacological and toxicological applications. NURA contains bioactivity annotations for 15,247 molecules and 11 selected NRs, and it was obtained by integrating and curating data from toxicological and pharmacological databases (i.e., Tox21, ChEMBL, NR-DBIND and BindingDB). Our results show that NURA dataset is a useful tool to bridge the gap between toxicology- and medicinal-chemistry-related databases, as it is enriched in terms of number of molecules, structural diversity and covered atomic scaffolds compared to the single sources. To the best of our knowledge, NURA dataset is the most exhaustive collection of small molecules annotated for their modulation of the chosen nuclear receptors. NURA dataset is intended to support decision-making in pharmacology and toxicology, as well as to contribute to data-driven applications, such as machine learning. The dataset and the data curation pipeline can be downloaded free of charge on Zenodo at the following DOI:

Original languageEnglish
Article number115244
Number of pages9
JournalToxicology and Applied Pharmacology
Publication statusPublished - 15 Nov 2020
Externally publishedYes

Bibliographical note

Copyright © 2020 Elsevier Inc. All rights reserved.


  • Chemistry, Pharmaceutical/methods
  • Computer Simulation
  • Data Collection
  • Data Interpretation, Statistical
  • Databases, Factual
  • Drug Evaluation, Preclinical
  • Humans
  • In Vitro Techniques
  • Models, Molecular
  • Receptors, Cytoplasmic and Nuclear/drug effects
  • Small Molecule Libraries
  • Software
  • Toxicology/methods

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