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Artificial Intelligence Used For Faster And Systematic Drug Discovery

Update Date: Jan 08, 2017 09:58 PM EST
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Artificial Intelligence Used To Make Drug Discovery Faster And More Systematic
Scientists developed a new method for drug discovery that is faster, cheaper, and more systematic using artificial intelligence. (Photo : George Frey/Getty Images)

A husband-and-wife research team from University Of California-San Francisco(UCSF) was able to develop a new method that uses artificial intelligence for faster and systematic drug discovery. This new method drops the cost and time for searching possible new drugs to treat illnesses and diseases.

Professors of Pharmaceutical Chemistry at UCSF, Steven Atschuler and his wife, Lani Wu, have developed a way to make drug discovery faster and at a cheaper cost than that of the traditional method. The duo developed a method that involves engineering reporter cells and using a software program that uses artificial intelligence to scan and search compound libraries. This new method utilizes the cellular biology and computational analytics to find possible new drugs.

In phase one of the new method, Atschuler and Wu engineered reporter cells and digitizing and categorizing them according to its features. Along with the reporter cells, a software was developed to help scan and search compound libraries that will identify compounds that generated the desired response. These identified compounds have the potential to be used for new drugs for treatment. The new method not only speeds up the drug discovery process, it also drops the cast the fraction of the traditional one.

The second phase of the new method, as published in Nature Biotechnology, involves using the same principle for testing drugs for multiple purposes. The new method named, ORACL, which stands for Optimal Reporter cell lines for Annotating Compound Libraries, is like the Facebook of compounds where known compounds which generated the desired response are tagged by the reporter cells through the software.

With just one type of reporter cells, the team of researchers was able to screen around 11,000 drugs from multiple compound libraries for six disease pathways. These pathways are identified through a biochemical process where researchers identify drugs that influence the cellular chain of events by which a given disease advances or can be treated.

According to Phys.org, Dr. Matthew Jacobson, Chair of the Department of Pharmaceutical Chemistry in UCSF says, "To me, this just shows the power of bringing people with different types of backgrounds into biology and drug discovery."

In the future, the research team sees the ORACL method in making compound libraries indexed and searchable. Partnership with the private sectors, specifically large pharmaceutical companies, is seen as the next step to test the method in a much larger scale.

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