Iterative experimental and virtual high-throughput screening identifies metabotropic glutamate receptor subtype 4 positive allosteric modulators

Ralf Mueller, Eric S. Dawson, Colleen M. Niswender, Mariusz Butkiewicz, Corey R. Hopkins, C. David Weaver, Craig W. Lindsley, P. Jeffrey Conn, Jens Meiler

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

Activation of metabotropic glutamate receptor subtype 4 has been shown to be efficacious in rodent models of Parkinson's disease. Artificial neural networks were trained based on a recently reported high throughput screen which identified 434 positive allosteric modulators of metabotropic glutamate receptor subtype 4 out of a set of approximately 155,000 compounds. A jury system containing three artificial neural networks achieved a theoretical enrichment of 15.4 when selecting the top 2 % compounds of an independent test dataset. The model was used to screen an external commercial database of approximately 450,000 drug-like compounds. 1,100 predicted active small molecules were tested experimentally using two distinct assays of mGlu4 activity. This experiment yielded 67 positive allosteric modulators of metabotropic glutamate receptor subtype 4 that confirmed in both experimental systems. Compared to the 0.3 % active compounds in the primary screen, this constituted an enrichment of 22 fold.

Original languageEnglish (US)
Pages (from-to)4437-4446
Number of pages10
JournalJournal of Molecular Modeling
Volume18
Issue number9
DOIs
StatePublished - Sep 2012

Keywords

  • Enrichment
  • Machine learning
  • Metabotropic glutamate receptor subtype 4
  • Quantitative structure-activity relationship
  • Virtual high-throughput screening

ASJC Scopus subject areas

  • Catalysis
  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Computational Theory and Mathematics
  • Inorganic Chemistry

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