MEDICINE
FOUR OUR OF 10—THAT’S HOW many Americans the National Cancer Institute estimates will be diagnosed with cancer at some point. While 33 percent of those patients won’t live longer than five years, giving them precious little time to find effective treatments, it takes over a decade to bring new cancer drugs to market. The process involves animal testing, human trials and regulatory review—a gantlet through which less than 7 percent of experimental medicines successfully pass. Is it any wonder, then, that there are less than 2,000 Food and Drug Administration- approved pharmaceuticals on the market? Not 2,000 cancer treatments— 2,000 drugs for all diseases.
Insilico Medicine, a Baltimorebased biotech research company, hopes to revolutionize drug development by slashing the time necessary for research with the help of artificial intelligence (AI). In a study published in the medical journal Oncotarget, a team led by Insilico Medicine details their approach. Essentially, researchers built two computer networks (together known as generative adversarial networks, or GANs). One suggests new molecules that may have cancer-fighting properties; the other eliminates those suggestions based on known treatments. “It’s better to explain with an analogy from art,” says Polina Mamoshina, a research scientist at Insilico Medicine. If cancer drugs were works of art, she says, the first network would be an art student attempting to copy them, and the second network would be an art expert flagging forgeries. Each time the student’s work gets called out as a forgery, the student must get better at copying the original; each time the student’s work gets better, the expert must work harder at spotting forgeries.