Open AccessNuclear receptor modulators: Catching information by machine learning
Author(s) -
Cecile Valsecchi,
Francesca Grisoni,
Viviana Consonni,
Davide Ballabio,
Roberto Todeschini
Publication year - 2021
Publication title -
biomedical science and engineering
Language(s) - English
Resource type - Journals
ISSN - 2531-9892
DOI - 10.4081/bse.2021.198
Subject(s) - computer science , nuclear receptor , machine learning , human health , artificial intelligence , risk analysis (engineering) , biology , medicine , transcription factor , environmental health , biochemistry , gene
Nuclear receptors (NRs) are involved in fundamental human health processes and are a relevant target for toxicological risk assessment. To help prioritize chemicals that can mimic natural hormones and be endocrine disruptors, computational models can be a useful tool.1,2 In this work we i) created an exhaustive collection of NR modulators and ii) applied machine learning methods to fill the data-gap and prioritize NRs modulators by building predictive models.