# The Week AI Drug Discovery Got Audited - Vertical Spotlight: Healthcare & Biotech - Week of July 28, 2026

> Startups and venture newsletter for the week of July 28, 2026. The Healthcare and Biotech vertical spotlight, where podcasts stopped celebrating AI drug discovery and started counting it, with Xaira, ARC Institute, Caris, Bayesian Health, Initiate Ventures and Yosemite in focus, and a live argument over whether a learned model can beat physics.

## Vertical Spotlight: Healthcare & Biotech

### Week of July 28, 2026: The Week AI Drug Discovery Got Audited

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*This week's rotation lands on Healthcare and Biotech. The theme across the podcasts: the field finally started counting. One researcher went looking for the wave of AI-designed drugs everyone assumed was building, and came back with a number nobody liked.*

## The Landscape

For three years the pitch has been that AI would compress drug discovery. This week, several podcasts independently stopped celebrating and started measuring, and the numbers are more interesting than the hype.

Start with the audit. On the Digital Pathology Podcast, Thibault Geoui walked through a simple exercise. The Boston Consulting Group had published a paper counting how many "AI-developed" drugs were in clinical trials. At the end of 2023, the answer was 67. Geoui worked with AI researcher Eliott Greenblatt to rerun the same methodology and ask what happened next.

> "I was really thinking, OK, you know, with all the investment that we had in AI for drug discovery and development, we must have like 300, 400 drugs. We found only six or seven more. I was like, how come? You know, I was really, really surprised."

Six or seven more, 28 months later. And no AI-designed drug has yet reached the market at all. Geoui is careful here: the one or two drugs sometimes described as AI-developed and approved are, on inspection, repurposed drugs, existing medicines pointed at a new disease. AI helped with the repurposing. That is not the same thing.

He offers reasons rather than accusations. Some of the original 67 simply failed, which is normal given the industry's roughly 95% failure rate. Many changed owners, because the AI-first biotechs take an asset partway and then hand it to a large pharma company with the scale to run late-stage trials.

And there is a genuinely strong counter-argument buried inside the bad number. Insilico Medicine, Geoui's example of the real thing, has compressed discovery and preclinical work from the usual five to six years down to about 18 months. Skeptics say Insilico still hasn't put a drug on the market. True. But as Geoui puts it:

> "They will fail after five or six years instead of failing after 18 months. I'd rather fail after 18 months."

That is the honest state of play. AI has not yet made drugs better. It has made the early part cheaper and dramatically faster, which, in a business where 95% of attempts die, is worth a great deal on its own, even before a single approval.

**The second throughline: the bottleneck has moved from algorithms to data, specifically to a kind of data that mostly doesn't exist yet.**

This came through most clearly on Latent Space, where Xaira Therapeutics' chief AI scientist Bo Wang and chief discovery officer Ci Chu explained why protein design took off and cell biology hasn't. Chu's answer is that protein design inherited 70 years of high-quality, community-curated structural data in the Protein Data Bank. That is what made AlphaFold possible. Nothing comparable exists for cells or patients.

Worse, the data that does exist is the wrong *type*. The big single-cell atlases are observational, describing healthy cells sitting there. Drug discovery needs causal answers: if I turn this gene down, what happens? Chu's explanation of why observational data can't get you there is the cleanest version of that argument anyone gave this week:

> "Let's say you observe gene A, B, C all go up and down together in your descriptive data set. You can infer that A regulate B and C... You might also say that B regulate A and C, and that would be perfectly reasonable as well... And there's n number of ways to fit a causal regulatory network into this group of data."

Which is why, he says, models trained on descriptive data "do not yet outperform linear models on causal tasks." A five-billion-parameter model losing to a straight line is an unusually blunt admission for a company that just shipped one.

**Third: the money is moving, and it is moving toward companies that own their own experiments.** On Ground Truths, ARC Institute co-founder Patrick Hsu made a point that should focus every founder's mind. The frontier AI labs are now competing directly for the pharma budget, and they have the cheapest capital in the industry.

> "If you were to raise venture capital today in order to try to develop against a net new target... you're a money-losing proposition for the first two, three, four, five, six, seven, eight years."

Compare that with Anthropic, which Hsu pegs at roughly a $60 billion annual run rate. He described a "fight for the middle": FutureHouse spun out Edison Scientific, the Biomni team spun out Philo, Anthropic launched Claude Science, OpenAI has its own tooling, and Chai Discovery is selling antibody design models. All of them are walking into the same pharma procurement meetings.

## Companies to Know

**Xaira Therapeutics: X-Cell.** Xaira released X-Cell, a 4.9-billion-parameter "virtual cell" model that predicts what happens to a cell when you switch a gene off. The interesting part isn't the model, it's the factory behind it. Xaira built the training data itself using Perturb-seq, a pooled CRISPR technique that lets you knock out one gene per cell across thousands of cells in a single scrambled experiment, so there are no batch effects to clean up. The resulting Pisces dataset covers 25 million cells across 16 cell types, genome-wide.

The result that mattered: they trained the model only on *resting* T cells, then asked it to predict what perturbations would do in *activated* T cells it had never seen. It worked. Bo Wang described the reaction as "a wow moment from biologists that this is the first time biologists actually find the model can predict exactly how these unseen cell lines respond to different perturbations."

Wang also ranked what actually drove performance, which is unusually useful for anyone building in this space: "the quality among scale of the datasets, and then the architecture, and then the prior knowledge." He noted that switching from autoregressive training to diffusion language models gave a significant lift specifically on generalizing to unseen tasks.
*Latent Space: The AI Engineer Podcast, "Causal Models Need Causal Data: Xaira's X-Cell model for Drug Discovery" (July 21, 2026)*

**Anthropic: Coefficient Bio and Claude Science.** Patrick Hsu confirmed Anthropic acquired Coefficient Bio, a startup out of the antibody engineering group at Genentech doing zero-shot antibody design, and has launched Claude Science, a research workbench. Hsu ties the push to CEO Dario Amodei's background, a PhD in computational neuroscience from Princeton. His description of the workbench is a good picture of where this is heading: a biologist with no coding ability can say "I want to use Evo to generate a new DNA recombinase," have the system dispatch sub-agents, call AlphaFold in the browser to check whether the generated protein looks reasonable, and then order the DNA from a synthesis vendor.
*Ground Truths, "Patrick Hsu: How Is AI Catalyzing Life Science?" (July 20, 2026)*

**ARC Institute.** Four and a half years old, split between academic investigators with joint appointments at Stanford, Berkeley and UCSF, and in-house technology centers. It has published Evo and Evo2 (language models trained on DNA across all of evolution) and State and Stack (its first virtual cell models). The ambition Hsu described is running the model backwards: initialize it in a diseased cell state and ask what sequence of perturbations moves the cell back toward health. "That may not be a single magical perturbation... but it may be a combination over time in order to slowly navigate the cell state manifold." ARC is also working with OpenAI on Alzheimer's drug targets.
*Ground Truths, "Patrick Hsu: How Is AI Catalyzing Life Science?" (July 20, 2026)*

**Biomni (Stanford).** Published in *Science* and discussed at length on Ground Truths: an agent wired to 150 specialized tools and software packages plus a database of 2,500 publications. The test cases were unusually broad: rare disease diagnosis, single-cell analysis, and, notably, wearable sensor data, over 1,000 participants in a COVID study with 1.4 billion heart-rate readings, from which it validated six biomarkers. It also worked through more than 300,000 single cells to identify new pathways in embryonic skeletal development. Hsu's read on why this suddenly works: it's downstream of coding agents getting good. Because code is verifiable end to end, coding models improved fastest, and now they sit upstream of every other vertical, including biology.
*Ground Truths, "Patrick Hsu: How Is AI Catalyzing Life Science?" (July 20, 2026)*

**Cythera and Silicon Therapeutics: Woody Sherman.** Sherman spent over a decade at Schrödinger, founded Silicon Therapeutics, scaled the platform at Roivant, and now leads science at Cythera, which is building oral drugs for immunology targets that today can only be reached with injectable biologics.

The Silicon Therapeutics story is the concrete proof point: a STING agonist taken from concept to clinic in about three years, with a team of roughly 10 computational people and 10 wet lab people. STING was considered close to undruggable. The natural molecules that activate it are cyclic dinucleotides, big awkward molecules that get cleared from the body before they do anything. Sherman's team instead found a much smaller drug-like molecule and got it to pair up with itself inside the binding site through a quantum-mechanical pi-pi stacking interaction, which he believes had never been done before.

No commercial software could model that, so they built the tool themselves. "It was only needed for a few months of the project, but it was really a critical part." His summary of the discipline required is the line founders should tape to the wall:

> "Computation can be super powerful when used appropriately, but you can't make drugs on computers."

*Data in Biotech, "Beyond Language: Why Drug Discovery Needs Physical AI, Not Just Large Language Models" (July 20, 2026)*

**Caris Life Sciences: MyClarity.** The most concrete "AI is already in the clinic" example of the week. Dr. George Sledge, EVP and Chief Medical Officer, described a data asset of over half a million whole exomes, over half a million whole transcriptomes, and millions of digitized pathology slides, attached to claims data and electronic health records. For scale: an exome is 23,000 genes; a transcriptome is 61,000 transcripts per cell.

MyClarity works off the humblest object in medicine, the H&E stained slide, which Sledge calls "cutting edge 1877 technology." A doctor's pathologist sends a slide, it gets digitized, uploaded, and run through an algorithm. Within 24 hours the result comes back telling you whether an early-stage estrogen-receptor-positive breast cancer patient is at risk of *early* recurrence (in which case she may need chemotherapy) or *late* recurrence 10 to 20 years out (in which case she needs extended hormonal therapy). "This is something that we just simply couldn't do even two years ago."

He also gave a good example of what scale buys you: to study ESR1 amplification, they had to search 30,000 breast cancer patients to find roughly 120 cases.

And a striking stat on why clinicians need this at all: on the FDA's hematology and oncology site, "there's literally one new indication every week of the year for the past five or six years." Sledge's verdict: "the hardest job on the planet right now is to be a general medical oncologist."
*Healthcare NOW Radio, "News You Can Use Special Edition: AI In Action with George Sledge" (July 19, 2026)*

**Initiate Ventures and TensorBio.** Jessica Owens, formerly CDC, cancer biology at Stanford, an investing partner at Kleiner Perkins, and a co-founder of Grail, which she helped build into an $8 billion company, has launched Initiate Ventures with co-founders Yana and Randy Scott (who has taken three companies public). They co-found and back AI-native healthcare companies "at the whiteboard stage," and deliberately do *not* invest in therapeutic assets, ag tech, or implantable devices, because therapeutics "take a boatload of capital" and would break their ownership model.

Portfolio company TensorBio is building a blood test to stage liver fibrosis in MASH. Owens' market math is worth repeating because it reframes a disease most investors think of as niche: 20 million patients with active disease and 80 million with precursor disease. Her comparison: all of liquid biopsy in oncology is supported by roughly 18 million cancer survivors and about 2 million new cases a year. "Mash is massive."

The clinical need is specific. There were no approved MASH drugs until 2024, so nobody bothered screening. Now there are (Rezdiffra requires patients to be at fibrosis stage F2 or higher), so patients need to know their stage, and then need to know whether the drug is working.

Her regulatory point is the one founders should note. Everyone assumed these tests would be FDA-regulated; instead the lab-developed-test and CLIA pathway looks like it's back for the foreseeable future. That matters commercially: "Once it's FDA approved, it's locked and you have to submit back to the FDA anytime you want to change the test." Under CLIA, a diagnostic can keep learning from real-world results.

She was also blunt about evidence quality in the current pitch flow: "I see far too many pitches where it's like, what was your discovery cohort? And it's like eight people plus all these EHR records."
*The BioCentury Show, "Ep. 115: Initiate's Owens on what it takes to be AI-native" (July 23, 2026)*

**Yosemite (Reed Jobs).** Reed Jobs' firm announced the first close of a second fund targeting $350 million. The model is unusual on two axes: it invests only in oncology (about 40% of biotech), and roughly a third of deployed capital goes into companies Yosemite creates itself, often with academics at Yale, Berkeley and Stanford. Alongside the fund, 2.5% of AUM goes into a donor-advised fund for no-strings-attached research grants, topped up with $1 million a year from management fees. In fund one, two of 20 companies came directly out of a grant.

On what AI is actually doing for him: "What AI is being able to do right now is accelerate a lot of grunt work that, frankly, they're not necessarily doing a better job of. But what they are doing is doing it incredibly fast and with reproducible and consistent outcomes." His example: finding a molecule to bind a target used to take a postdoc doing X-ray crystallography, cost roughly $50,000 and take weeks. "Now this is something that we can model out in minutes."

He also flagged a trial-design change that could matter more than any model. Instead of recruiting a control arm, you use a synthetic stand-in and recruit only the active arm. "That's going to half the amount of patients you need, and it's going to massively increase the speed of this." He says the FDA is leaning into it now.

Two macro numbers from him. On the funding environment: pharma is entering its largest patent cliff in history while sitting on record cash, which has driven an acquisitive spree over the last eight months or so. On the research base: last year an administration asked for a cut of up to 40% of the NIH budget, where the largest previous cut was 1% in 2009, which cost roughly 7,000 NIH scientists their jobs. Congress rejected the 40% request.

And the single best illustration of what better chemistry unlocks: Revolution Medicines, now worth around $40 billion, is drugging KRAS, the most heavily mutated oncogene, in pancreatic cancer, and has doubled survival in pancreatic adenocarcinoma from 12 to 24 months. Jobs' analogy is that a good drug target is Pac-Man, with an obvious mouth to block; KRAS "is sort of like the Death Star to your Pac-Man... this oval that just goes along and kills you." Amgen scientists stumbled on a cryptic pocket in it about a decade ago, producing Lumakras. Historically only about 15% of what your DNA encodes has been druggable at all.
*StrictlyVC Download, "Reed Jobs Is Betting Cancer Doesn't Have to Be Fatal" (July 21, 2026)*

**Bayesian Health, and the counter-reading.** Johns Hopkins announced FDA clearance for an early-warning system for sepsis, developed by Suchi Saria's lab and commercialized by Bayesian Health, described as one of the first AI-based medical tools to get clearance, detecting sepsis hours faster than clinicians and reducing deaths by nearly 20%. Saria began translating the research after losing her nephew to sepsis in 2017.

Emily M. Bender and Alex Hanna went through the underlying papers on Mystery AI Hype Theater 3000 and argued the 20% figure is far narrower than the headline. The retrospective study screened roughly 469,000 patient encounters, of which 9,805 had sepsis; the analysis then set aside the cases the system missed and looked only at true positives, then narrowed again to cases where the physician engaged with the alert and the patient wasn't already on antibiotics. False alarms, the ones that cost clinician attention, are not in the denominator. As Bender put it: "if you see a percent anywhere, you've got to ask what's the denominator."

For context on why anyone is skeptical at all: the paper's own citations note that one of the most widely deployed sepsis early-warning systems had a sensitivity of just 33% and a precision of 2.4%.

The same episode surfaced something founders selling into public agencies should watch: Seattle Fire Department has been running a relationship with Corti since 2019, using AI on 911 calls to help route callers, put in place without public review and without notifying callers. Washington is a two-party consent state with a carve-out for 911.
*Mystery AI Hype Theater 3000, "State of Emergency, 2026.06.29" (July 23, 2026)*

**Paradigm and Massive Bio: the FDA's AI pilot.** The FDA has an open request for information on a pilot for AI-enabled optimization of early-phase clinical trials, and the hosts of Note to File read through the public comments. Massive Bio's central recommendation: the FDA should design this as "a regulatory grade human governed AI enabled trial orchestration program, not a narrow model evaluation or a sponsor only operational experiment." The hosts' response, "it seems like a sponsor only operational experiment. I mean, 100%," captures the mood of practitioners watching this.

Their sharper question was about design choice. The pilot is starting in oncology, which they see as either shrewd or naive depending on the goal: oncology trials are messy and hard to enroll, but because oncology trial participation often *is* the clinical care, the data sits in the electronic health record, which makes it the right proving ground if what you're really demonstrating is EHR integration, as Paradigm's approach implies.

Separately, the same show covered the FDA's real-time clinical trials initiative, with two proof-of-concept trials streaming endpoints to the agency as they happen, and pushed back on the claimed 45% reduction in "dead time," arguing that streaming unclean, unadjudicated data doesn't fix the actual bottleneck, which is patient enrollment.
*Note to File: A Clinical Research Podcast, "WHO's New GCP Course, China Data Ban & FDA's AI Pilot" (July 22, 2026); "Real-Time Clinical Trials: FDA's Big Bet or Big Mess?" (July 20, 2026)*

## The Money and the Market, Briefly

Two things worth knowing if you're funding in this vertical.

**The IPO window is opening, and reverse mergers have quietly become a real alternative.** On Biotech Hangout, the hosts noted that Mentari's reverse merger into NMED came with a nearly $300 million PIPE, for a bispecific PACAP antibody for migraine, a mechanism Lundbeck recently validated with an intravenous approach. That is now the sixth biotech reverse merger accompanied by a $200 million-plus financing. Last year there were about three; the year before, one; before that, almost none. As one host put it, reverse mergers have "finally figured out how to approximate an IPO with the benefits of the IPO and the capital raise of an IPO." The tradeoff is thinner sell-side coverage, partly offset by lower fees, though most of these PIPEs come with bank sponsorship and some analyst following baked in.

They also contrasted Scribe Therapeutics' IPO, raising roughly $100–120 million, still early-stage, with Australian TGA approval for a PCSK9 gene-silencing therapy, with Sana Biotechnology's 2021 preclinical IPO, which raised over $600 million at a $4.5 billion pre-money valuation. Different eras, and the hosts were candid that many companies don't choose to IPO so much as run out of private capital: "many companies are at the end of the line for raising funds from private investors... if they want to continue, they IPO."

**Contract research organizations are not being disrupted, they're being handed a margin lever.** The Clinical Trials Guru's read is that demand is healthy: IQVIA reported 2025 revenue of $16.3 billion against a $32 billion R&D backlog, and ICON $8.25 billion. Sponsors, he argues, will no longer pay for project management or admin, "that's been commoditized," but will pay for "speed, enrollment certainty, regulatory confidence, clean data, and operational transparency."

His counter to the fear that pharma will use AI agents to bring clinical operations back in-house: "The AI does not have relationship with sites. The AI doesn't know outside of a theoretical algorithm, which sites are going to perform well." He also expects an inversion in hiring, senior people back in demand and junior roles squeezed, precisely *because* the FDA's draft AI guidance keeps a human accountable in the loop. If your tools surface more problems, you need more experienced people to act on them.

One structural number for anyone selling into this market: a 2025 report found 35% of drug developers increased their use of functional service providers over two years, versus 29% increasing full-service outsourcing, with flexible and hybrid models now roughly two-thirds of clinical development outsourcing. Sponsors want à la carte.
*Biotech Hangout, "Episode 190" (July 24, 2026); Random Musings From The Clinical Trials Guru, "The Worst Is Over for CROs and Jobs" (July 20, 2026)*

## One Debate: Can you trust an AI model that says it beat physics?

Here is the unresolved question of the week, and it has real money riding on it.

About six months ago, Recursion and MIT released Boltz under an open license, with a headline claim of near-FEP-quality binding affinity predictions at roughly 1,000 times the speed. (Free energy perturbation, or FEP, is the slow, physics-based simulation that drug companies currently trust to estimate how tightly a molecule will stick to its target. It is expensive and it is the gold standard.) If a neural network can match it a thousand times faster, you no longer need the physics in the loop, and a large chunk of the computational chemistry industry gets re-priced overnight.

Woody Sherman's answer was unusually direct:

> "I can first of all say that that proposal doesn't hold. And that's what the field has been learning over the course of the last six months. It was exciting news. It was a great press release, but it's just not the reality."

His argument is precise, not dismissive. The learned models work well "in cases where you already have enough data around the target and the drugs that are of interest." But the whole point of an interesting drug program is that you're going somewhere new, where that data doesn't exist. "These models at this point, the co-folding models in their ability to predict affinity are nowhere close to the physics based free energy method. They just don't know anything about the target of interest."

The deeper problem is benchmarks. Sherman thinks people are genuinely trying to be honest, and the leakage happens anyway. He cited an article Pat Walters published about a week earlier showing that even a clean-looking date cutoff, train on everything before 2025 and test on everything after, leaks, because something "published" after the cutoff was often first published a decade earlier under slightly different conditions. "It's still a lookup table of something that's been done before."

He is not arguing physics always wins. He concedes that as you accumulate data, "the models get to be more accurate than the physics based simulations because you're training directly on the endpoints that matter," and that the physics itself is full of approximations. The Newtonian force fields used in molecular dynamics are approximations of quantum mechanics, which is exactly why his own STING program had to drop down to real quantum mechanical calculations to see the pi-pi stacking interaction that made the drug work.

So the debate isn't "AI versus physics." It's narrower and more useful: **does a learned model generalize to a target it has never seen, or is it interpolating inside a region it has already memorized?** Nobody in these conversations claimed to have a benchmark that reliably answers that question.

Set Sherman's caution against Reed Jobs' optimism and you get the honest range. Jobs isn't claiming the models discover new biology; he's claiming they do the grunt work fast, cheaply and reproducibly, and that this alone changes what a small team can attempt. Sherman agrees with all of that. Where they'd differ is on the next step: whether the acceleration keeps compounding into genuine novelty, or plateaus at the edge of the training data.

Geoui frames the same fork as the difference between optimization and discovery. Optimization, finding the right growth medium for a cell line or sampling a parameter space intelligently, "is really the type of thing where you don't need a lot of creativity," and he argues it should be fully autonomous, partly because humans are the source of irreproducibility. He cited an Amgen paper finding roughly 60% of oncology experiments couldn't be reproduced, and a later Nature survey putting it at 60–70%. Discovery, where "even expressing the question is difficult," is still where humans matter.

That distinction is probably the most useful thing a founder can take from this week. If you are automating optimization, the case is strong today and the ROI is measurable. If you are selling discovery, the burden of proof just got heavier, because someone is now counting.

## A Note on Scale

One last number, because it explains why everyone keeps trying despite the audit. Geoui laid out the search space for small-molecule drugs: there are roughly 10^60 drug-like molecules. To calibrate that: every grain of sand on Earth is about 10^25, and every atom in the universe is about 10^80.

You cannot synthesize them. You cannot even represent them all in a computer. But the number you can design computationally is "several orders of magnitude greater than what you can actually make physically."

For comparison, he noted it took roughly 50 years of experimental work to determine 170,000 protein structures. Since AlphaFold, the count is in the hundreds of millions.

AlphaFold didn't solve drug discovery. Nobody serious claims it did. But it took a step that used to consume four years of a structural biologist's PhD and made it a few minutes. That is the shape of every real win in this vertical so far: not a cure, but a step that used to be a career.

*Next week: Sales & Customer Service.*

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