How Big Pharma Is Using Artificial Intelligence To Make More Effective Drugs

As in so many other industries, the ability to analyze vast amounts of data, then have the model determine which are the most likely to be effective will, eventually, have dramatic effects on both efficiency and effectiveness. JL

Sy Mukherjee reports in Fortune:

Artificial intelligence (can) improve drug discovery at the earliest stages (when the risk of failure is also the highest). [A.I.] can help analyze large data sets from clinical trials, health records, genetic profiles, and preclinical studies; within this data, it can recognize patterns and trends and develop hypotheses at a much faster rate than researchers alone. "You don’t start out with a predetermined hypothesis—you feed the system all this data and allow the data to generate the hypotheses.”
Creating innovative, lifesaving medicines, say pharmaceutical company bosses, requires a sufficient return on investment. But lately, that ROI stinks. In 2017, according to Deloitte, the 12 largest bio- pharma companies got a mere 3.2% return out of their drug-research arms. In 2010, that number was 10.1%.
How can pharma break out of this rut? One avenue might be the use of artificial intelligence to improve drug discovery at the earliest stages (when the risk of failure is also the highest). “[A.I.] can help analyze large data sets from sources such as clinical trials, health records, genetic profiles, and preclinical studies; within this data, it can recognize patterns and trends and develop hypotheses at a much faster rate than researchers alone,” says Deloitte.
And Big Pharma names like Merck, Sanofi, and Astra- Zeneca, are already taking it to the lab. In 2017, AstraZeneca struck a partnership with BERG, a Massachusetts startup, to use the latter’s A.I. platform to home in on promising biological targets and possible agents against neurological diseases such as Parkinson’s.
So how does it work? For starters, says BERG CEO Niven R. Narain, by going “back to biology.” Tissue samples are taken from both healthy and sick patients, analyzed on multiple molecular levels, combined with clinical data, and then fed through BERG’s A.I. platform to suss out targets.
For analyzing that data, BERG eschews “the publicly available databases,” says Narain. “We use a Bayesian approach rather than a neural network,” he says. “It’s not just taking a bunch of data, putting it through a model, and coming up with some correlation. You don’t start out with a predetermined hypothesis—you feed the system all this data and allow the data to generate the hypotheses.”
So, in short, A.I. sounds like good old-fashioned science. Go figure.