SAN FRANCISCO — It took only a single, one-hour meeting in a Starbucks to convince venture capitalist Robert Nelsen that he was going to invest in the company being built by machine-learning whiz Daphne Koller. “She’s just an amazing force of nature,” said Nelsen, who invests for ARCH Venture Partners.
Koller’s company, Insitro, in a matter of months raised over $100 million from big-name investors including ARCH, Foresite Capital, Andreessen Horowitz, and the firm that manages Jeff Bezos’ personal VC investments. (Koller refers to Bezos by his first name.) Now, it’s time for the next step. On Tuesday, the giant drug maker Gilead Sciences said it would pay Insitro $15 million — and up to $1 billion more down the line if it meets certain goals — for help in developing drugs to treat a common liver disease. It is the South San Francisco-based company’s first deal with a drug maker, and offers a test of whether artificial intelligence can remake drug development.
The very nature of the financials emphasize the exciting but risky reality: The technology has great promise, but nothing is guaranteed.
I’m not sure how much of a “first” this is. Janssen made high-dimensional gene-expression data from 230,000 of their compounds using Genometry’s L1000 technology way back in 2016, specifically for machine-learning-in-drug-discovery applications. Works well.
Do I believe in 50-100 years problems in biology and medicine will be solved in silico? yes
Do I believe all the buzz around AI, machine learning is because we are at a stage where it can actually do that? Not at all
Culprit, she has been working on it for 20 years, and I doubt she has achieved anything meaningful in that area, or there is no evidence of it
There is simply not enough knowledge, consistent experimental data, and I guess, good enough algorithms to pull it off
Here are her Google Scholar citations. You’re free to define “meaningful” as you wish.
meaningful = something that has a real world general applicability, beside an academic proof of concept limited to a narrow specific case. As typical for bioinformatic research
Lot of hyperbole in here, unsurprisingly.
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