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

作者：Mueller Ralf; Dawson Eric S; Niswender Colleen M; Butkiewicz Mariusz; Hopkins Corey R; Weaver C David; Lindsley Craig W; Conn P Jeffrey; Meiler Jens^*

来源：Journal of Molecular Modeling, 2012, 18(9): 4437-4446.

DOI：10.1007/s00894-012-1441-0

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

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 mGlu(4) 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.

• 出版日期2012-9