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 that target a protein linked with fibrosis (tissue scarring). They synthesized six of these molecules in the lab and then tested two in cells; the most promising one was 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, prioritizing new structures that could be synthesized in the lab. It’s similar to what a human chemist might do to seek new therapies—just much faster.
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