Mendelspod Podcast

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Those of us in biology tend to think that chemistry is rather straight forward. But today’s guest says there’s quite an art to synthesizing a molecule.
A few years ago, Lee Cronin, a chemistry professor at the University of Glasgow joined Mendelspod for a sprawling conversation about the origin of life, alien biology, assembly theory, and his conviction that chemistry could become programmable. Now Cronin is back, and that last idea has become a company that now has a “chemifarm.”
First let’s look at the problem. It is easy to assume that once a drug company has designed a promising molecule, the chemists more or less know how to make it. Not so. Chemistry remains surprisingly artisanal. A molecule that looks perfectly plausible on a computer may require too many steps to synthesize, depend on unstable intermediates, or require reactions that simply aren’t known. And chemists may not discover that until they’ve spent considerable time trying.
This becomes an even bigger problem in the age of AI. Algorithms can propose vast numbers of new molecules, but which ones can actually be made? And if one can’t, is there another molecule nearby in chemical space that could perform the same function but be dramatically easier to synthesize?
That’s the problem Chemify is trying to solve.
“You cannot discover what you cannot make,” Cronin says in today’s interview.
He spent 15 years developing χDL, a programming language that reduces the work of chemistry to a set of basic operations that machines can execute. Chemify’s software can then work backward from a desired molecule to determine a possible route for making it. Its robotic systems execute those instructions in the physical world, including chemistry requiring unusual temperatures and conditions. And its Chemifarms bring large numbers of these systems together so the process can be repeated at scale.
The result is something Cronin calls a chemistry “hyperscaler.” A pharmaceutical company might bring Chemify a molecule proposed by its own AI system. Chemify can ask whether that molecule is realistically makeable, develop a route to it, physically attempt the synthesis, and feed what happened back into the system. If the molecule isn’t practical, the platform can suggest alternatives. Every success—and importantly, every failure—adds information about what parts of chemical space are actually accessible.
Cronin’s ambition is enormous. He wants Chemify to become a sort of utility, the “AWS for all drug discovery companies.” Not another company competing to discover the next drug, but infrastructure that allows pharmaceutical and AI companies to turn digital ideas into physical matter.

There are very few genomics projects with the level of ambition of the NIH All of Us Research Program. The latest release includes data from more than 747,000 participants, 535,000 whole genomes, 480,000 electronic health records, and—for the first time—long-read sequencing, proteomics, transcriptomics, and a large collection of information extracted from clinical notes. More than 24,000 researchers are now using the resource. But the program is also arriving at an important transition: roughly 80 percent of its original ten-year funding runs out this year.
So what happens when a massive national research experiment begins to come of age?
Josh Denny, CEO of All of Us, joins us to talk about what the program has accomplished, what researchers are beginning to learn from the data, and what comes next. We discuss the extraordinary scale and diversity of the resource and the growing use of genetics alongside electronic health records and other forms of health data. All of Us is beginning to move from building infrastructure toward producing discoveries that could affect patient care.
Denny explains why the program’s diversity is not simply a matter of representation but a scientific necessity. The project has already identified roughly 1.3 billion genetic variants, including more than 275 million that had not previously been observed. That diversity becomes even more important, Denny argues, as medicine moves toward increasingly personalized predictions and treatments.
“We are capturing such a richer population and so much more kinds of data that we can’t actually reason through it as humans,” he says. “If the data underneath it are highly biased and not representative, then we’re going to make the wrong conclusions.”
We also discuss All of Us as a platform for a much broader picture of human health—from electronic health records and wearables to nutrition, the microbiome, multiomics, environmental exposures, and eventually pediatric data. Denny shares examples of participants whose lives have already been changed by medically actionable genetic results and describes how researchers can build new studies on top of the All of Us population.
What is the next phase of the program and that of precision medicine?
“We’re going to have to redefine our definition of disease,” Denny says. Rather than treating something like type 2 diabetes as a single condition, he imagines increasingly precise descriptions of an individual’s biology, exposures, risk, and response.
After years spent building one of the largest health datasets in the world, All of Us is beginning to show what we might actually do with it.


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Welcome to Mendelspod’s 16th season!
It’s hard to believe we’ve been doing this for just over 15 years, following first and foremost the people reshaping biology and medicine. But also the technologies and ideas. Our original vision was to feature the “people behind the headlines” and you’ll see that what we continue to do in this first show of the new season. A sincere thank you to everyone who has listened, watched, subscribed, sponsored, recommended guests, shared a show, told your friends, and simply kept coming back. We have a terrific season ahead, with some of the biggest names in the life sciences as well as some you may not know yet who are asking entirely new questions.
To begin, we wanted to go big picture.
Rob Phillips, professor of biophysics, biology, and physics at Caltech and co-author of Physical Biology of the Cell, thinks this could be the “Newtonian moment” for biology. We have extraordinary amounts of data. What biology needs now, he argues, are concepts that can organize those observations into a more predictive understanding of life.
“The data in biology is getting to the point where we have no excuse,” Rob says.
The conversation ranges from the limits of molecular reductionism to the meaning of prediction, the role of AI, and why better measurement still matters enormously. Phillips makes the case for explanations at many different scales. Understanding a wildebeest migration through molecules, he says, would be like explaining the Golden Gate Bridge through its iron atoms.
Rob is nothing if not provocative—in the best sense. His style is to pile up ideas and questions one on the other to propel his listeners into that creative space of wonder.
“We need more opinions. We need more authenticity,” Phillips says. “I want to hear how people think about things differently.”


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On Mendelspod, we’re always searching for scientists who are looking at biology in new ways. Sometimes that means a new technology. Sometimes it means asking entirely different questions. Erdinc Sezgin is bringing the tools of physics to biology.
Sezgin is a group leader at the Max Planck Institute of Molecular Cell Biology and Genetics, where he heads the Membrane Biophysics Group. His research focuses on one of the most familiar yet least understood structures in biology: the cell membrane.
In this conversation from April, Sezgin explains why the membrane isn’t a smooth sea of lipids, but a dynamic landscape of tiny molecular neighborhoods that constantly assemble, disappear, and reorganize. We discuss how he is using the tools of physics to better understand the membrane’s inner and outer lipid layers, each with distinct electrical properties.
Sezgin also talks about his collaboration with Pixelgen Technologies, where Molecular Pixelation was used to study how changes in membrane charge reshape the cell surface. By knocking out a lipid-regulating complex, Sezgin and his colleagues showed that living cells can adopt surface features that alter immune recognition and may help explain how cancer cells evade destruction.
It’s a reminder that major biological insights often arrive alongside new tools that make previously hidden phenomena measurable. Sezgin’s work is also a broader comment on scientific boundaries. Biology is not separate from physics or chemistry, but an expression of them in living systems.
“Cells don’t have physics, chemistry, biology... It is life,” he says.


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One of the biggest technology shifts we saw in biology over the past year came from the young company, Cellanome. Instead of relying only on static single-cell snapshots, the Cellanome platform enables longitudinal observation of live cells.
In this conversation, Pier Federico Gherardini, VP of Computational Biology at Cellanome, joins Matthew Spitzer of UCSF, whose lab is putting the Cellanome platform to work in cancer immunology.
The new CellCage technology allows researchers to follow individual cells and their interactions over time, then pair that behavior with transcriptomic and other molecular readouts. As Gherardini explains, this creates “a new data type” that connects functional behavior directly to molecular biology.
For Spitzer, the breakthrough is linking phenotype and function in the same individual cell. His lab can watch dendritic cells activate T cells, or T cells interact with tumor cells, and then ask what was molecularly different about the cells that actually performed the function.
“Now we have measured the function of the cell and the phenotype for the same exact individual cell,” Spitzer says.
The result is a new way to study cell biology that could have implications for cancer immunology, cell therapy, target discovery, and functional screening.


This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.mendelspod.com/subscribe