Nuclear receptor modulators: Catching information by machine learning

Authors

  • Cecile Valsecchi Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano Bicocca, Milan, Italy
  • Francesca Grisoni Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich
  • Viviana Consonni Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano Bicocca, Milan
  • Davide Ballabio Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano Bicocca, Milan
  • Roberto Todeschini Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano Bicocca, Milan

DOI:

https://doi.org/10.4081/bse.198

Keywords:

Nuclear Receptors, multi-task learning, machine learning, neural networks

Abstract

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.

Downloads

Download data is not yet available.

References

Downloads

Published

29-09-2021

Issue

Vol. 2 No. 1 (2021)

Section

Communications

License

PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.

How to Cite

Nuclear receptor modulators: Catching information by machine learning. (2021). Biomedical Science and Engineering, 2(1). https://doi.org/10.4081/bse.198