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Predicting selective liver X receptor β agonists using multiple machine learning methods.

Yali Li1, Ling Wang, Zhihong Liu

  • 1Research Center for Drug Discovery & Institute of Human Virology, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou 510006, China. junxu@biochemomes.com.

Molecular Biosystems
|March 4, 2015
PubMed
Summary
This summary is machine-generated.

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Researchers developed machine learning models to identify selective Liver X receptor (LXR) beta agonists for treating dyslipidemia. These models accurately predict compounds that activate LXRβ without activating LXRα, minimizing side effects like lipogenicity.

Area of Science:

  • Biochemistry and Pharmacology
  • Computational Chemistry
  • Drug Discovery

Background:

  • Liver X receptors (LXRα and LXRβ) are key regulators of cholesterol homeostasis.
  • Activating LXRs offers a therapeutic avenue for dyslipidemia, but LXRα activation can induce lipogenicity.
  • Selective LXRβ agonists are desirable to avoid LXRα-mediated side effects.

Purpose of the Study:

  • To develop and validate machine learning models for predicting selective LXRβ agonists.
  • To identify novel chemical scaffolds with potential therapeutic applications in dyslipidemia.
  • To guide the rational design of new selective LXRβ agonists.

Main Methods:

  • Compilation of a dataset of 234 selective and non-selective LXRβ agonists.
  • Application of multiple machine learning algorithms including Naïve Bayesian (NB), Recursive Partitioning (RP), Support Vector Machine (SVM), and k-Nearest Neighbors (kNN).

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  • Optimization using property descriptors and structural fingerprints, with evaluation on training, test, and external validation sets.

    • Main Results:

    • Developed over 324 machine learning models, with most achieving >80% predictive accuracy on training and test sets.
    • Top 15 models evaluated on an external set of 76 novel compounds, with 10 models exceeding 90% accuracy.
    • Naïve Bayesian models identified key molecular fragments for selective LXRβ agonism.

    Conclusions:

    • Validated machine learning models demonstrate high predictive power for identifying selective LXRβ agonists.
    • These models are suitable for virtual screening to accelerate the discovery of novel anti-dyslipidemia agents.
    • Fragment analysis provides valuable insights for the structure-based design of improved LXRβ agonists.