Atomwise, a startup using AI to accelerate drug discovery, today secured $123 million in funding. A spokesperson said the funds will enable the startu

Atomwise raises $123 million to accelerate drug discovery with AI

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2020-08-11 13:35:05

Atomwise, a startup using AI to accelerate drug discovery, today secured $123 million in funding. A spokesperson said the funds will enable the startup to scale its technology and team as it expands its portfolio of joint ventures with researchers at the University of Toronto, Duke University School of Medicine, Charles River, Bayer, Eli Lilly, Merck, and others.

Fewer than 12% of all drugs entering clinical trials end up in pharmacies, and it takes at least 10 years for medicines to complete the journey from discovery to the marketplace. Clinical trials alone take six to seven years, on average, putting the cost of R&D at roughly $2.6 billion, according to the Pharmaceutical Research and Manufacturers of America.

Atomwise claims its AtomNet platform can screen 16 billion chemical compounds for potential hits in under two days, expediting a process that would normally take months or years. (Hit identification is often the first step in drug discovery, where the right molecules — hits — that bind to a target protein and modify its function are identified.) AtomNet’s AI algorithms screen for things like potency and the degree to which a drug acts on a given site while guarding against toxicity, and they work on targets in hard-to-reach parts of the body — like the central nervous system — with a hit rate the company claims is 10,000 times better than wet-lab experiments.

In one instance, Atomwise claims it was able to inhibit a protein-protein interaction in the nervous system for a multiple sclerosis project, exploring 8.2 million molecules that were effective in animal trials and have been licensed to a pharmaceutical company in the U.K. In another, the company discovered a drug candidate with no previous antiviral application that blocked Ebola infectivity across virus strains from multiple epidemics.

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