Today, on average, it takes more than 10 years and billions of dollars to develop a new drug. The vision is to use AI to make drug discovery faster and cheaper. By predicting how potential drugs might behave in the body and discarding dead-end compounds before they leave the computer, machine-learning models can cut down on the need for painstaking lab work.
And there is always a need for new drugs, says Adityo Prakash, CEO of the California-based drug company Verseon: “There are still too many diseases we can’t treat or can only treat with three-mile-long lists of side effects.”
Now, new labs are being built around the world. Last year Exscientia opened a new research center in Vienna; in February, Insilico Medicine, a drug discovery firm based in Hong Kong, opened a large new lab in Abu Dhabi. All told, around two dozen drugs (and counting) that were developed with the assistance of AI are now in or entering clinical trials.
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We’re seeing this uptick in activity and investment because increasing automation in the pharmaceutical industry has started to produce enough chemical and biological data to train good machine-learning models, explains Sean McClain, founder and CEO of Absci, a firm based in Vancouver, Washington, that uses AI to search through billions of potential drug designs. “Now is the time,” McClain says. “We’re going to see huge transformation in this industry over the next five years.”
