This is the stuff that Nvidia's Jensen Huang has been pitching* for the last half-decade or so.
It's also why we saw this in October 2024:
Nobel Prize in Chemistry 2024
From Knowable Magazine, August 18:
AI and machine learning are kicking synthetic biology up to new levels
of innovation. Researchers see huge potential for novel drugs and other
chemicals; some also see risks.
James
Field believes his automated laboratory is closing in on a powerful new
drug to kill cancer cells with unprecedented precision. Yet the drug
wasn’t created by any of the biologists working in his lab — it was
generated by artificial intelligence.
Field is a protein engineer
working in synthetic biology, a discipline that uses the latest
technology to engineer novel cells or biological components. In his
world, researchers often talk about a cycle known as DBTL — design,
build, test, learn — an iterative process where the end product, whether
it is a molecule or a new strain of bacteria, is repeatedly optimized
until it has the traits scientists seek. Field’s company, LabGenius, is
one of a group of startups that is at the forefront of a revolution in
which AI increasingly runs the DBTL cycle from beginning to end.
The
work takes place at the company’s sophisticated automated laboratory in
a former biscuit factory in south London. Such laboratories, known as
biofoundries, first emerged in the 2010s as gene editing technology,
combined with “high-throughput” machines — which can conduct hundreds of
experiments at once — accelerated the DBTL cycle by many orders of
magnitude.
Experiments using biofoundries often involve adapting a piece of DNA
that is then added to a host organism — frequently the bacterium Escherichia coli
or yeast — to get it to produce a desired molecule, which can be
anything from an antibody to an ingredient for sustainable plastic.
Algorithms
have been integral to biofoundries from the start in managing the
operation of high-throughput machines. But the industry is now
approaching a point where AI models are becoming sophisticated enough to
program DNA to produce new medicines and industrial chemicals, an
emerging field known as SynBioxAI.
Rather than
helping only to run the machinery, AI can now work iteratively, learning
from results and suggesting new avenues of experimentation, reducing
the development time for new drugs or industrial chemicals from years to
months.
According to Paul Freemont, who heads
synthetic biology at Imperial College London, we are approaching a
powerful point of “convergence of automation, data and machine learning
and AI.” He was the founding chair of the Global Biofoundries Alliance,
an international group of publicly funded biofoundries that launched in
2019 in Kobe, Japan, with 16 members. The group has now grown to over 40
members across the world, spread from China to Mexico.
Since that
launch, countries have been rapidly investing in biofoundry capacity.
The United States, for example, is spending $75 million on the
development of five new biofoundries, while China has named
biomanufacturing and synthetic biology as core strategic priorities for
its 2026-2030 five-year plan.
The objective is to engineer
biology, programming cells to produce new drugs and industrial products,
a technology that Freemont says is going to “become essential for
mankind.”
However, he believes the field hasn’t fully reached this
point of convergence. “I think there’s a massive amount of technical
work that needs to be done to achieve the vision of what people are
trying to do,” he says....
....MUCH MORE
*Some previous posts:
July 2021 - NVIDIA Opens UK's Fastest Supercomputer To Outside Researchers, Academic and Commercial
NVIDIA Claims Install of UK’s Top Supercomputer, for Research in AI and Healthcare
Announced last October, NVIDIA today launched Cambridge-1, calling it
the United Kingdom’s most powerful supercomputer. Enabling scientists
and healthcare experts to use the combination of AI and simulation to
accelerate the digital biology revolution, Cambridge-1 represents a $100
million investment by NVIDIA.....
March 2023 - "The Biorevolution: Its Implications for U.S. National Security, Economic Competitiveness, and National Power"
The author of this piece, Dr. Tara O'Toole is Senior Vice-President of the CIA's venture capital arm, In-Q-Tel.
January 2024 - Boston Consulting Group: "Synthetic Biology Is Getting Closer to Industrial Scale"
January 2024 - We Are Going To Hear More And More About Digital Biology (NVDA)
February 2024 - Taking Nvidia's Jensen Huang Seriously: Paris-Based "Bioptimus primed with $35m to unravel disease biology using AI"
Two pitches that Nvidia's CEO will be making at next month's NVDA lovefest (Nvidia GTC Conference, March 17 - 21, 300,000
attendees in-person and online) are 1) sovereign AI, every country,
even every city needs its own supercomputer powered by Nvidia chips and
its own Large Language Model trained on those supercomputers and 2)
digital biology - everything from rapid drug discovery to individually
tailored gene therapy.*
February 2024 - "17 key takeaways from the 2024 J.P. Morgan Healthcare Conference"
April 2024 - "RAND: 'Artificial Intelligence and Biotechnology: Risks and Opportunities"
October 2024 - Paris-based baCta Is Using Engineered Bacteria to Grow Natural Rubber and Slash CO2 Emissions
AI-powered synthetic biology is so hot right now.
April 2026 - "Genomics Has Revealed An Age Undreamed Of"
This piece was published at Palladium magazine in 2023, so a few years after Nvidia's Jensen Huang started talking about AI and medicine and artificial biology*, but before the possibilities really started to become apparent. Now genomics looks to be very investable.**
In many ways the vibe feels similar to the zeitgeist around chips and training AI a dozen years ago.
I don't know if it is going to work out as well as 2013's "Why Is Machine Learning (CS 229) The Most Popular Course At Stanford?"—which was followed by 2014's Deep Learning is VC Worthy—which was followed by 2015-to-date: "Saaaay, this Nvidia may be on to something."
But we shall see.