Last year, Longevity Vision Fund announced its investment into Insilico Medicine, a pioneer in next-generation artificial intelligence technology for drug discovery led by Alex Zhavoronkov.
Artificial intelligence (AI) and its role in the future of healthcare is one of the key investment interests for the Longevity Vision Fund. AI is speeding up the process of designing new drugs from years to just days. AI-powered drug discovery technology has unparalleled disruptive potential – drastically cutting drug development costs, by shortening the drug development timeframe and eliminating many false starts, which can occur at any stage of the development process.
In March 2020 Fierce Biotech, one of the leading resources in biotech news published a yearly Fierce15 list highlighting the most exciting biotech and MedTech companies in the world. Insilico Medicine was featured this year as one of the 15 most exciting MedTech companies to watch. Here is Fierce Biotech profile of the company:
The scope: We’ve been told for years that artificial intelligence could one day be the key to faster drug discovery, development, and testing—an essential tool to shorten the time between an idea and a potential cure. Last fall, we started to see some proof.
In fewer than 50 days, Insilico Medicine demonstrated it could use AI to create thousands of druglike molecular structures and winnow them down to a promising few, before successfully testing the lead candidate in live mouse models.
The company set its AI’s sights on drugging a receptor tyrosine kinase known as DDR1—found on epithelial cells in the skin, kidney, liver, and lungs—that is linked to diseases and disorders tied to fibrosis.
In three weeks, Insilico had developed a slate of 30,000 novel, computer-generated small molecules. In 25 days more, it selected and synthesized the most promising six and ran them through in vitro testing for selectivity and metabolic stability before an in vivo experiment.
The paper was published in Nature Biotechnology last September and has since become one of the journal’s most popular papers. Additionally, the code used for the project—dubbed GENTRL, for generative tensorial reinforcement learning—was made open source and available to the public.
