The approach is based on two popular AI techniques: generative adversarial networks, and reinforcement learning.

The news: A team from AI pharma startup Insilico Medicine, working with researchers at the University of Toronto, took 21 days to create 30,000 designs for molecules which target a protein linked with fibrosis (tissue scarring.) They synthesized six of these molecules in the lab, then tested two in cells, with the most promising one tested in mice. The researchers concluded it was potent against the protein and showed “drug-like” qualities. All in all, the process took just 46 days. The research was published in Nature Biotechnology this week.

The method: The system examines previous research and patents for molecules known to work against the drug target, then prioritizing new structures that could be synthesized in the lab. It’s similar to what a human chemist might do as they seek new therapies—just much faster.

Context: Getting a new drug to market is hugely costly and time-consuming: it can take 10 years and cost as much as $2.6 billion, with the vast majority failing at the testing stage, according to the Tufts Center for the Study of Drug Development. No wonder then, that there’s so much work underway on using AI to expedite the process. DeepMind is among the companies exploring pharmaceutical research as a potential future avenue for its algorithms.

A word of caution: Although the research looks promising, it’s still very much a proof-of-concept. We’re a long way from AI-designed drugs being created, let alone sold to patients. We explored the issue in this article from our TR10 issue earlier this year.

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