Better drugs through AI? Insitro CEO on what machine learning can teach
Big Pharma
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[December 03, 2024]
By MATTHEW PERRONE
WASHINGTON (AP) — Artificial intelligence is changing the way companies
do business — helping programmers write code and fielding customer
service calls with chatbots.
But the pharmaceutical industry is still waiting to see whether AI can
tackle its biggest challenge: finding faster, cheaper ways to develop
new drugs.
Despite billions poured into research, new medicines still typically
take a decade or more to develop.
Founded in 2018, Insitro is part of a growing field of AI companies
promising to accelerate drug discovery by using machine learning to
analyze huge datasets of chemical and biological markers. The South San
Francisco-based company has signed deals with drugmakers like Eli Lilly
and Bristol Myers Squibb to help develop medicines for metabolic
diseases, neurological conditions and degenerative disorders.
CEO and founder Daphne Koller spoke with the AP about what AI brings to
the challenges of drug discovery. The conversation has been edited for
length and clarity.

Q: Why is drug development so difficult?
A: I think the problem with drug discovery is that we are trying to
intervene in a system that we only slightly understand. Many of the
successes that we’ve seen in the last 15 to 20 years have been when we
arrive at a sufficient understanding of the system so we can really
design interventions to align with it.
So one of the things that we try to do at Insitro is unravel the
underlying complexity of heterogeneous diseases and identify new
intervention modes that could help, maybe not the entire population, but
perhaps just a subset of it. That way we can really identify the right
therapeutic hypothesis to intervene in a particular patient population.
And that, I think, is the real crux of the industry’s lack of success.
Q: Companies like Eli Lilly employ thousands of medical scientists
and researchers. What can your technology do that those experts can’t?
A: One of the things that has been happening in parallel to the AI
revolution is a much quieter revolution in what I call quantitative
biology, which is the ability to measure biological systems with
unprecedented fidelity. You can measure systems like proteins and cells
with increasingly better measurements and technology.
But if you give that data to a person, their eyes will just glaze over
because there’s only so many cells someone can look at and only so many
subtleties they can see in these images. People are just limited in
their ability to perceive subtle differences.
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(AP Illustration/Jenni Sohn)
 So you end up with a very
reductionist view of a very complex, multifaceted system which is
really important to unraveling the distinctions between patients and
uncovering where an intervention really can make a difference.
Q: How did you become interested in this field?
A: My PhD was in computer science. But I started to get into the
field of machine learning in the service of biomedical problems back
in 1998 or 1999.
At that time, the problems that machine learning was able to tackle
were, frankly, uninspiring. How inspired can you get about
classifying spam versus non-spam in a dataset of email messages?
I was looking for something that had more richness to it. And my
first foray into this field was not because I was particularly
interested in becoming a biologist, but because I was looking for
more technically challenging questions. And then, as I started
looking into it, I became interested in biology in its own right.
Q: Insitro employs both computer scientists and medical
researchers. Was there any culture clash in getting those two groups
to work together?
A: This is probably one of the most important things we’ve achieved
as an organization.
You can take the most sophisticated, best meaning scientists from
either side and put them in the same room together and they might as
well be speaking Thai and Swahili to each other.

When you’re an engineer, you’re looking for the strongest, most
consistent patterns that are going to allow you to make predictions
about a majority of cells or individuals. When you’re a life
scientist, oftentimes you’re actually looking for the exceptions
because those are the threads that can lead to new discoveries.
So we’ve put in place a number of cultural elements and
organizational elements to help people engage with each other
openly, constructively and with respect.
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