How artificial intelligence techniques are aiding the hunt for new drugs

A better cure for cancer – and other illnesses – could already be in existence, hidden right under our noses. 

The problem is that possible new lifesaving drugs are created much faster than scientists can study them. Millions of untested compounds wait, jumbled together in no particular order in vast repositories called compound libraries.

A new search method that blends cellular biology and computational analytics may be the answer. A husband-and-wife research team at UCSF have developed a way to do the job much faster and at a fraction of the cost of the traditional method. The work involved designing a new kind of cell, writing some new software, and then parsing the resulting landslide of data.

Envision a compound library as a massive box containing thousands of unorganized, unlabeled photographs. The current method is akin to each researcher pulling out a handful of photos and looking through each handful to find pictures of one particular person. They may be able to identify a few, but every future project has to start from scratch.

By contrast, imagine a method that digitizes the photos and then screens them with a program akin to facial recognition software. This is the first step in Altschuler and Wu's new method, which categorizes reporter cells in much the same way that Facebook tags photos of your friends: by digitally identifying their features.

Now, when a compound library with unknown properties is screened with reporter cells, the software can identify which of those compounds is generating a desired response. The cost for each test drops from hundreds of dollars to a dollar or less.