Predicting selective liver X receptor β agonists using multiple machine learning methods-Reference-Cited by-同舟云学术

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

Published:2015 Issue:5 Volume:11 Page:1241-1250
ISSN:1742-206X
Container-title:Molecular BioSystems
language:en
Short-container-title:Mol. BioSyst.

Author:

Li Yali¹²³⁴⁵,Wang Ling⁶⁷⁴⁵,Liu Zhihong¹²³⁴⁵,Li Chanjuan¹²³⁴⁵,Xu Jiake⁸⁹¹⁰¹¹¹²,Gu Qiong¹²³⁴⁵,Xu Jun¹²³⁴⁵

Affiliation:

1. Research Center for Drug Discovery & Institute of Human Virology

2. School of Pharmaceutical Sciences

3. Sun Yat-Sen University

4. Guangzhou 510006

5. China

6. School of Bioscience and Bioengineering

7. South China University of Technology

8. Centre for Orthopaedic Research

9. School of Surgery

10. The University of Western Australia

11. Perth

12. Australia

Abstract

The classification models for predicting selective LXRβ agonists were firstly established using multiple machine learning methods. The top models can predict selective LXRβ agonists with chemical structure diversity.

Publisher

Royal Society of Chemistry (RSC)

Subject

Molecular Biology,Biotechnology

Link

http://pubs.rsc.org/en/content/articlepdf/2015/MB/C4MB00718B

Reference70 articles.

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

2. Convergence of lipid homeostasis through liver X and thyroid hormone receptors

3. Liver X Receptor Agonists as Potential Therapeutic Agents for Dyslipidemia and Atherosclerosis

4. Liver X Receptors (LXR) as Therapeutic Targets in Dyslipidemia

5. LXR is crucial in lipid metabolism

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