Ron C

Longevity, biodefense, and infectious disease.
University of Michigan
Founder @ superbiome

[Stop the Nonsense @kasthomas]

I find it frustrating that AI people fundamentally don't understand how science works.

Science has never been roadblocked by computation.

Let me give you a concrete example. Nucleic acids (DNA, RNA) were first discovered by the Swiss physician-chemist Friedrich Miescher in 1869. He made an extract from white blood cell nuclei. The extraction technique was one that typically extracted proteins from a cell. But in this case, Miescher noticed that his extracts contained no sulfur. (Proteins contain sulfur.) Also, strangely, the extracts contained phosphorus (something NOT found in proteins). He called his extracts "nuclein" (to distinguish them from "protein").

Miescher measured the pH of his extracts. They were acidic. We now call the macromolecules his discovered "nucleic acids."

There is no way computation would have predicted Miescher's discovery of nucleic acids nor would it have helped Miescher in any way.

For many years (1869 to 1944), proteins were thought to be the genetic material. No one believed nucleic acids were complex enough to encode phenotypes. Work by Avery and his colleagues (in 1944) showed that DNA, not proteins, must in fact be the genetic material. The Avery discovery did not hinge on computation in any way.

In 1950, Austrian biochemist Erwin Chargaff discovered that the amount of guanine in DNA equals the amount of cytosine, and also adenine equals the amount of thymine. This discovery did not hinge on computation and would not have been made easier by AI.

People who think AI will "solve biology" do not understand how scientific discovery works.

Thank you for asking the original question.

[Ron C @RonC345]

If you care about or are investing in AI X Bio, listen to me.

Most Alzheimer’s drugs hit their targets and still failed phase 3 clinical trials. You'll even have drugs that are reducing amyloid plaque production, and they still aren't conferring a benefit.

There’s a reason clinical pipelines are crowded with drugs chasing the same popular targets. With modern chemistry and AI, optimizing molecules to bind to a protein with favorable pharmacokinetics is becoming a commodity.

The real bottleneck, and one of the most overlooked hurdles in pharma, is proving the mechanism of action actually alters human disease.

You can do all the Phase 1 optimization you want, but you're going to be squeaking out single-digit millions of dollars of savings while a 20% reduction in Phase 2 failure rates could save around $435 million per successful drug launch.

That's why we're focused on reducing Phase 2 risk with in vivo modeling in humans. No reliance on organoids or mice to predict human efficacy. They can't reliably answer the question that matters most: will this mechanism actually work in humans?

[Patrick Hsu @pdhsu]

need better target ID in addition to better molecular design

[Ron C @RonC345]

Anthropic's revenue is 17 billion, by the way. Price-to-earnings ratio goes brrr

[Vineeta Agarwala @vintweeta]

Drugs are some of the largest and most important products in the world

[Ron C @RonC345]

@robziman Yes, there are no substitutions.

[Ron C @RonC345]

rude awakening incoming

some problems get solved by cranking up reasoning power and prowess namely in mathematics.

the total human interactome is estimated to contain roughly 480,000 to 2.6 million direct protein-protein interactions (and potentially millions more involving metabolites, lipids, and nucleic acids), meaning the vast majority remain unmapped and uncharacterized.

applying ai models to biology is like trying to have the smartest person on Earth guess what number I'm holding behind my back. Unless they develops X-ray vision, it doesn't matter how smart they are.

That's why we're developing the in-vivo autonomous discovery loop.

Rather than relying on static simulations outside the body, our system combines smart biological hardware with adaptive AI agents that observe, intervene, and learn directly inside living tissue.

[Bajan @Bajanpunkw]

This is f*cking insane. Anthropic CEO Dario Amodei, live with JPMorgan CEO Jamie Dimon days before Anthropic goes public:

"In something like 2023, models couldn't even do high school math… In 2025, they started solving unsolved problems in mathematics, minor unsolved problems

And then you start to see major open problems being solved… things where you could win the Fields Medal for it"

In 2 minutes, he explains why biology is next, and why the skeptics in biology sound exactly like the skeptics in math did

High school math → Minor open problems → Fields Medal level → Biology

In biology, the skeptics say the blocker is the trials. Then the manufacturing. Then the FDA

His answer: "little by little, and then… all at once within a few years, each of these steps are being addressed by AI"

My guess: the first big "AI cracked a disease" headline will get the same reaction the math results got, "it was in the training data", right up until it isn't a headline anymore

[Ron C @RonC345]

@avramidou What do you think about a consumer CRO for omics where everyday people get paid to do at home tests

[Ron C @RonC345]

@chrissyfarr Yea… true

[Ron C @RonC345]

@jadenswang @uncorkcap @EniacVC @Standard_Cap Background agents change the inference mix. A health agent checking labs, sleep and orders all day is mostly small, latency-tolerant calls, and cost per task decides if it works on a consumer budget. Superbiome needs exactly this kind of routing. Would love to chat.

[Ron C @RonC345]

@0xsamgreen Merchants will keep saying no until an agent can prove who it acts for and what it can spend. Health will hit the same wall with pharmacies and clinics. Superbiome buys health products on a capped virtual card, so we need that guardrail layer. Would love to chat.

[Ron C @RonC345]

Just missing the pathophysiology of the disease part

[Mgoes (bio/acc 🤖💉) @m_goes_distance]

i'm convinced we will solve biology in the next 3-5 years

google is vertically stacking it layer by layer:

layer 1- alphafold: what proteins look like
layer 2- alphagenome: what dna does
layer 3- alphagenome 2: which cell it happens in
layer 4- isomorphic: designing the drug

layer 5 is getting the drug into the right cell, not cracked yet

time to accelerate that, bio/acc

[Ron C @RonC345]

@devcayer That would actually be interesting. If was an investor I would make bets like that I guess.

[Ron C @RonC345]

A new CRO run by three 19 year olds for "AI run experiments" Jesus christ...

[Ran Blekhman @blekhman]

Why does every AI Bio company sounds exactly like Theranos?

"a CRO but really fast" (with no tech innovation, no experience in biology) sounds a lot like "a blood test but from a really small amount"

[Ron C @RonC345]

@zhanelnugmanova Doing something tangential. Would be great to chat.

[Ron C @RonC345]

Goodbye https://t.co/oWLVhhtg5n. At this point if you create software you best hope that you are creating a data moat that can at least be purchased.

[Claude @claudeai]

Introducing Claude Meetings: add Claude to your team’s video call and it builds docs, whiteboards, and other artifacts with you while you’re still talking.

Starting to roll out in beta on Team and Enterprise plans.

[Ron C @RonC345]

Biotech CEO "Ya know in this presentation I want to caveat the following risk factors that bring uncertainty to our middle of the path scenario..."

AI CEO "We're curing death"

[NIK @ns123abc]

Anthropic CEO predicts cancer, Alzheimer's, heart disease, mental illness will be cured in less than 10 years

ACCELERATE

[Ron C @RonC345]

Clearly there is a massive gap for health.

[Olivia Moore @omooretweets]

New @a16z market map alert! 🚨

Consumer agents have emerged as the hot category...and as a can't-miss distribution channel

We've seen products reach hundreds of thousands of users faster than ever before (with best-in-class retention!)

But, we're still SO early 👇

[Ron C @RonC345]

The last thing humans would be harvested for in the matrix is energy and the first this is compute. The brain is an extremely energy efficient super processor. It also comes pre-trained with years of information.

[Ron C @RonC345]

The amount of resources spent to work on shitty shortcuts to high quality human testing is absurd.

[owl @owl_posting]

Why haven't organoids solved all of drug discovery?
(5.8k words, 26 minutes)

here, i try to answer a question that i imagine has been on peoples minds for awhile. first essay of a three part series over organoids, because this subject is complicated

https://t.co/b25zgVlt2M

[Ron C @RonC345]

People really think better reasoning will solve disease. It’s like asking the smartest person on earth to guess how many fingers you’re holding behind your back.

[Rich Law @drrichjlaw]

Stop it @DarioAmodei. The data exists for <1% of biology. Likely AI will help get a few better drugs slightly faster & more efficiently in that time frame but honestly, especially with 5-8 years needed in the clinic, what you are saying is hyperbolic BS!