If you are using NR-Toxpred for your research Please cite "Predicting the binding of small molecules to nuclear receptors using machine learning Brief Bioinform . 2022 May 13;23(3):bbac114. doi: 10.1093/bib/bbac114" link

NR Tox Pred - A Web Server for Predicting the Activity of Small Molecules on Nuclear Receptors

Nuclear receptors are important targets for the toxic effects of many xenobiotic chemicals which can act as agonists or antagonists to the receptor in question. NR Pred is a SVM based webserver for the prediction of binding of chemical molecules to multiple different nuclear receptors. The user can input the small molecule in SMILES, as a CAS number or sketch the molecule in the space provided and predict the compounds activity against the androgen receptor (AR), estrogen receptor – α (ER-α), estrogen receptor - β (ER-β), farnesoid X receptor (FXR), glucocorticoid receptor (GR), progesterone receptor (PR), peroxisome proliferator-activated receptor delta (PPARD), peroxisome proliferator-activated receptor gamma (PPARG) and retinoid X receptor (RXR) and calculate the binding mode with each.

Predict

Acknowledgement

Production of this website was supported by contracts from the California Office of Environmental Health Hazard Assessment and NIH grants P42ES004705 and S10OD034382

Disclaimer

This website is for personal use and research purposes only. The resource includes information sourced from other sites, databases and sources. The user must apply judgment in use of information and the products of analyses performed on the site. Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the developers or the University of California. The views and opinions of the developers of the site expressed herein are solely theirs and shall not be used for advertising or product endorsement purposes With respect to analyses provided by this server, neither the University of California nor any of their employees, makes any warranty, express or implied, including the warranties of merchantability and fitness for a particular purpose, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights.