Machine learning's next frontier: Epigenetic drug discovery

ScienceDaily | 10/22/2019 | Staff
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"In order to identify the rare few drug candidates that induce desired epigenetic effects, scientists need methods to screen hundreds of thousands of potential compounds," says Alexey Terskikh, Ph.D., associate professor in Sanford Burnham Prebys' Development, Aging and Regeneration Program and senior author of the study. "Our study describes a powerful image-based approach that enables high-throughput epigenetic drug discovery."

Epigenetics refers to chemical tags on DNA that allow cellular machinery greater or less access to genes -- thus altering gene expression. Nearly all changes in a cell, including reaction to a drug and environmental stress, are reflected by its epigenetic state. Several medicines that target epigenetic alterations are approved by the U.S. Food and Drug Administration (FDA) for the treatment of cancer, and researchers are working to find additional epigenetic-based therapies. However, drug discovery has been slowed by a lack of a high-throughput screening method: Scientists currently visualize epigenetic changes using special dyes and traditional microscopy methods.

Study - Scientists - Algorithm - Set - Drugs

In the study, the scientists trained a machine-learning algorithm using a set of more than 220 drugs known to work epigenetically. The resulting method, called Microscopic Imaging of Epigenetic Landscapes...
(Excerpt) Read more at: ScienceDaily
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