> In 2019, Deep Genomics announced it had discovered a treatment for a rare condition called Wilson disease, which is fatal if not addressed. For the afflicted, copper accumulates in their bodies, particularly in the liver, brain and cornea, where the metal can appear as a brownish ring. The Toronto-based company said in a news release that its molecule was the “first ever AI-discovered therapeutic candidate.” It wasn’t the only one Deep Genomics would divine through artificial intelligence.
> Founded in 2015, the company’s AI models would go on to test more than 200 million molecules for their ability to treat disease. By 2021, Deep Genomics had zeroed in on 10 drug candidates for preclinical study and aimed to have four undergoing human trials within a couple of years.
> Today, Deep Genomics has zero drugs in clinical trials and many of its plans have blown up. The company halted its Wilson disease program, ditched dozens of its machine learning models, appointed a new chief executive and is pursuing a different approach to using AI. It’s also open to a sale.
> “AI has really let us all down in the last decade when it comes to drug discovery,” said Deep Genomics founder Brendan Frey. “We’ve just seen failure after failure.”
> Dion Madsen, co-founder of Montreal-based Amplitude Ventures, was an early believer in the power of AI to improve drug discovery, and the early stage life sciences financing firm first invested in Deep Genomics in 2020. “We haven’t made as much progress as we had planned,” he acknowledged.
> The company didn’t have experienced drug developers on staff to validate predictions in the lab and provide feedback on the problems the AI models should be directed toward, and how to improve them. “Our hiccups really came not from the technology, ##but from not having the right quality of people on the validating side##,” Mr. Madsen said, adding that the situation is now improved.
There has been tremendous hype about AI in biopharma, even vendors that have nothing to do with drug development are giving us AI pitches.
Well you have to put it in perspective when the most brilliant medicinal chemists of all time have such an interesting track record across-the-board.
With all that talent and outstanding resources at their fingertips, there is a virtual guarantee that any one top chemist will achieve a certain number of blockbuster breakthroughs, even though it may take them a lifetime.
[1] https://www.manning.com/books/machine-learning-for-drug-disc...
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