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Related Experiment Video

Updated: Mar 17, 2026

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Probing Small-Molecule Binding to the Liver-X Receptor: A Mixed-Model QSAR Study.

Morena Spreafico1, Martin Smiesko1, Ourania Peristera1

  • 1Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel (Switzerland).

Molecular Informatics
|July 28, 2016
PubMed
Summary

A new Liver X Receptor (LXR) model in VirtualToxLab screens compounds for endocrine disruption. This validated model accurately predicts binding affinity, aiding in drug and chemical safety assessments.

Keywords:
BioinformaticsConsensus scoringDrug designFlexible dockingLiver X receptorMultidimensional QSAR

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Area of Science:

  • Toxicology
  • Computational Chemistry
  • Drug Discovery

Background:

  • Endocrine-disrupting chemicals pose risks to human health.
  • Accurate prediction of chemical-protein interactions is crucial for safety assessment.
  • VirtualToxLab offers automated screening for toxicological potential.

Purpose of the Study:

  • To integrate and validate a Liver X Receptor (LXR) model within the VirtualToxLab platform.
  • To screen natural compounds for their potential to bind to LXRs.
  • To establish the predictive capability of the LXR model for compound screening.

Main Methods:

  • Utilized molecular docking to predict ligand binding to LXR protein structures.
  • Employed multidimensional Quantitative Structure-Activity Relationship (mQSAR) for binding affinity quantification.
  • Validated the model using an external set of 17 oxysterols and performed scramble tests.

Main Results:

  • Successfully screened 161 natural compounds for LXR binding.
  • The LXR model demonstrated predictive accuracy when compared with available experimental data.
  • Model robustness was confirmed through consensus scoring and elimination of chance correlations.

Conclusions:

  • The validated LXR model in VirtualToxLab is effective for predicting compound binding affinity.
  • This automated approach facilitates efficient screening of drugs, chemicals, and natural products for endocrine-disrupting potential.
  • The model serves as a valuable tool for early-stage safety assessment in drug discovery and chemical evaluation.