# AI Wins at Drug Design and Stalls Where Drugs Actually Die - AI Drug Discovery Weekly - Week of August 27, 2026 > AI Drug Discovery Weekly for the week of August 20 to 27, 2026. Podcast synthesis on a former NOETIK scientist explaining why biology's feedback loops are roughly as slow as five years ago, a quantum physicist's one-question test for hype, Sam Altman calling AI for science hugely important and not very good today, and Eli Lilly falling 5.6 percent despite a UK pill launch and an Alzheimer's blood-test clearance. ## AI Drug Discovery Weekly ### Week of August 27, 2026: AI Wins at Drug Design and Stalls Where Drugs Actually Die --- *The week the skeptics showed up. An ex-NOETIK scientist explains why AI still can't crack the hardest part of biology, and a quantum physicist offers a one-question test for the hype: "How many Nobel Prizes do you need to make this work?"* ## TL;DR * This was a loud week for *honest skeptics*, and their arguments are worth more than another round of hype. * On [Core Memory](http://url7324.matterfact.com/ls/click?upn=u001.idHmPrr2Geh7KYLAsTy7NkrIVb-2FgA4pmf2rMXQwGcOgxGCzKHtFlztHrUIWcHTL-2Bu9CCSQ2Xjhl-2BpljRyo8DJJyJC-2BFHnR1mCN-2B4YRB2A8ceBsv8lp1-2FQrSQls4E6FUReZhNBezGVzEX7yc8ZjZt2w-3D-3DTkS3_7mLGwmUci-2BLaXswv9WX1yTgqn3Wad-2FotHhzHgSNAZbWzrmRvyIis1vlKQMAtPDEybJRPxCHA5ZfFPkVoOwRvsbPKqTMxwG3Pdi3gvyTtYSiSqqYkVQ8DOFycp8BH3LmO7TwbugTf-2BLSAXuukFLlfv996z0yY-2BJPKDN3IqWt41ToiO2Rdpx6mdHZjhNZdtjxCoL7o0EXbBQvoqBxpCC7saA-3D-3D), scientist *Abhi Mahajan* (who worked at gene-therapy company Dyno Therapeutics and then at AI-biology startup *NOETIK*) laid out, in unusual detail, both what NOETIK is actually trying to do and why he thinks AI-for-biology is fundamentally harder than the headlines suggest. His core point: the parts of biology where AI is winning (protein and small-molecule design) have a clear scoreboard; the part that actually kills most drugs, does it work in a living body, does not. "The hill to climb for in-silico models is not well defined." * On [Heavybit Podcasts](http://url7324.matterfact.com/ls/click?upn=u001.idHmPrr2Geh7KYLAsTy7NkrIVb-2FgA4pmf2rMXQwGcOhdOXDMPn-2FrsecVfBne1n4CP7sPG6rmPSFvphoDe1E8y-2Fot1Aqcb8WE8Y4nKCvBXK5JpKcB35eV0Qyum6khY-2FGK5j41vIdRsROHGwgjSCUVYg-3D-3D88rg_7mLGwmUci-2BLaXswv9WX1yTgqn3Wad-2FotHhzHgSNAZbWzrmRvyIis1vlKQMAtPDEybJRPxCHA5ZfFPkVoOwRvsZnxZEOkViXKdKWeBKpHe-2BG7w-2F5oAHNoVcq7caXnxe6WrCAAPtvKYmYcDLTnJs3EaVyXHyvm-2Be7zoZWDiUYWIBuGQyfxLvY4ukdkUU3znby8njovq1RK7ZS2oXi8xeqSPA-3D-3D), quantum physicist and founder *Anastasia Marchenkova* offered a blunt filter for the wave of "quantum computing will transform drug discovery" claims: test every claim against the *best* classical computer, not against a strawman, and ask "how many Nobel Prizes do you need to make this work?" She also noted a striking statistic: *about 60% of the compute at US national-lab supercomputers goes to quantum-chemistry calculations*, the exact molecular-simulation problem the whole field is racing to speed up. * *Sam Altman*, on [David Senra](http://url7324.matterfact.com/ls/click?upn=u001.idHmPrr2Geh7KYLAsTy7NkrIVb-2FgA4pmf2rMXQwGcOgGzytAu17kbJDO1xI1T8O0ZV85WH-2F5Tt7wxu-2BwTsnqBkrV04P16Hp87c3lb6FRRXuEATEoxOqEyN0f2hoK1dhkX45q-2FatVYGDfN6XPOMoF6w-3D-3DcpeZ_7mLGwmUci-2BLaXswv9WX1yTgqn3Wad-2FotHhzHgSNAZbWzrmRvyIis1vlKQMAtPDEybJRPxCHA5ZfFPkVoOwRvse7lIN-2FndpVJFzUReky6-2Bx2vz1OHPWxkeo16kNY0g3hokO4GDoGpEr8o-2FxsXTXEVKtfi0mFL32b1uuhsst-2FUvvwxa7faeBQaHVMnhvNVNp-2BADSM3liPUgWz7PIP-2FRYI-2Bhg-3D-3D) (Aug 23), called AI for scientific discovery, "discover new physics and cure diseases," likely "one of the most important areas, even more important than automation." His own verdict on where it stands today: "not very good." * *Eli Lilly (LLY)* had a busy news week. It launched its *orforglipron pill (brand name Foundayo)* in the UK, put out a real-world study showing its *Zepbound* shot cuts total healthcare costs in older adults, and won FDA clearance (with Roche) for an *Alzheimer's blood test*, but the stock still fell about 5.6% on the week amid GLP-1 pricing and coverage worries. ## What's new ### The reality check nobody wanted, from someone who did the work The most valuable episode this week wasn't a founder selling a vision. It was a working scientist explaining, patiently and without a pitch, where AI actually helps in drug discovery and where it doesn't. On [Core Memory](http://url7324.matterfact.com/ls/click?upn=u001.idHmPrr2Geh7KYLAsTy7NkrIVb-2FgA4pmf2rMXQwGcOgxGCzKHtFlztHrUIWcHTL-2Bu9CCSQ2Xjhl-2BpljRyo8DJJyJC-2BFHnR1mCN-2B4YRB2A8ceBsv8lp1-2FQrSQls4E6FUReZhNBezGVzEX7yc8ZjZt2w-3D-3D_1gD_7mLGwmUci-2BLaXswv9WX1yTgqn3Wad-2FotHhzHgSNAZbWzrmRvyIis1vlKQMAtPDEybJRPxCHA5ZfFPkVoOwRvsf4CeEac5qE3pYdYffxsK0fMukk-2BEFPOmbWSuXWGfCsd2Zm-2BHXwCXlFeRD-2BTEoPwYNbe-2FWFXqBNbUXxkA4INJf7pq8AFZAt0NdUy-2FiTCuu8Qs4PRIq0WePgRTby3i2eJ5A-3D-3D) (Aug 20), the guest was *Abhi Mahajan*, who worked first at *Dyno Therapeutics* (a company using machine learning to design better gene-therapy delivery viruses) and then at *NOETIK*, the "biology foundation model" startup. Because he actually built these systems, his description of NOETIK is both clearer and more sober than the usual founder framing. Here is NOETIK's idea in plain terms. You take *biopsy samples from real cancer patients*, actual tumor tissue removed in surgery, and you measure them in enormous detail: where each type of cell sits (spatial data), which genes are switched on and where, and the proteins present. You feed all of that into an AI model that teaches itself to organize the samples into a kind of map, where similar tumors sit near each other. Some of the groupings match categories a doctor would recognize (one kind of carcinoma here, another there); others are patterns only the model sees. The clever part comes next. Clinical-trial results, knowing that a specific patient responded to a specific drug, or didn't, are, in Mahajan's words, "the rarest type of data that's out there." NOETIK's plan is to take a pre-trained model that already "understands" cancer, then fine-tune it on a tiny set of trial outcomes, say, "seven responders and six non-responders," and ask it to learn what made the responders special. If the model can cleanly separate the two, you can do one of two things: tell a drug company to *add that patient trait as an entry criterion* for its next trial (so more of the enrolled patients are the kind who respond, lifting the success rate), or *buy a drug that already failed* a broad trial and re-run it in exactly the sub-group where it should work. There's a second, more experimental piece: a *mouse platform used purely for finding brand-new drug targets*, where you can poke the tumor's environment in ways you can't in a person. Interestingly, Mahajan said you can run mouse data through a model trained only on human data and get a "projection of mouse data into a human landscape," the outputs come back as human-like gene readouts rather than mouse ones. His honest caveat: "the value proposition is still a little bit fuzzy," and the target-finding piece is "the least well understood" part of the whole system. Then came the part that makes this episode matter for anyone weighing the whole AI-drug-discovery thesis. Mahajan drew a sharp line between two worlds: * *The world where AI is clearly winning, protein and small-molecule design.* Here, he said, researchers "are very lucky" because they "identified interesting hills to climb." Improve a specific, measurable score (like how tightly a designed protein binds), and it tends to correlate with real-world effect. There's a scoreboard, so you can tell if you're getting better. * *The world where drugs actually die, does it work in a living body.* For the immune-system cancer drugs he studied, "the hill to climb for in-silico models is not well defined." There's no clean number to optimize that reliably predicts success in a patient. And "the answer is like unknowable... it's unverifiable." His explanation for *why* AI hasn't cracked this, even with the latest models, is the sharpest framing of the "verification bottleneck" I've heard. When the hosts brought up a recent claim that a frontier model had solved several hard math theorems, possibly Fields-Medal-level work, and asked whether that kind of raw intelligence should now accelerate biology, Mahajan pushed back: *math is "an extremely verifiable domain," so the feedback loops are "extremely tight."* In math, intelligence really was the only bottleneck, so more intelligence helps enormously. In biology, he argued, "the feedback loops are roughly as slow as they were five years ago." The bottleneck isn't only intelligence, it's also "human hands to do everything." You still have to physically run the experiment, in cells, in mice, then in people, and each step takes real-world time you can't think your way past. He was even skeptical of the fashionable fix, automated "self-driving" labs. After reading up on lab automation, his takeaway was that "most experiments are not worth automating." A handful of repetitive tasks (mixing compounds, screening drugs against liver cells) scale nicely, "but they also aren't super informative. The most informative experiments are kind of just like you tinkering around and trying things," the exact fiddly, dexterous bench work robots are worst at. And he flagged the same competitive shift that keeps coming up in this beat: the "verification layer" that forces you to re-run trials is also, for now, what protects US and European drug companies from Chinese rivals, because a Chinese company can't simply take its home-market data and sell a drug in America; it has to run the American trial too. But he noted China is increasingly "running multiple Phase 1s and Phase 2s at the same time," compressing the very timeline that used to be the moat. *Why it matters:* the most credible voice this week is a builder saying the easy-to-measure parts are going great, and the part that determines whether a drug lives or dies is barely better than five years ago, because you can't shortcut a living body. That's not a bearish take on the whole field, it's a map of *where* to expect progress (design) and where to stay patient (in-body efficacy). It also quietly validates NOETIK's bet, which is aimed squarely at the hard part: predicting who responds. ### A quantum insider's hype test On [Heavybit Podcasts](http://url7324.matterfact.com/ls/click?upn=u001.idHmPrr2Geh7KYLAsTy7NkrIVb-2FgA4pmf2rMXQwGcOhdOXDMPn-2FrsecVfBne1n4CP7sPG6rmPSFvphoDe1E8y-2Fot1Aqcb8WE8Y4nKCvBXK5JpKcB35eV0Qyum6khY-2FGK5j41vIdRsROHGwgjSCUVYg-3D-3Dr_o1_7mLGwmUci-2BLaXswv9WX1yTgqn3Wad-2FotHhzHgSNAZbWzrmRvyIis1vlKQMAtPDEybJRPxCHA5ZfFPkVoOwRvsUykF3KbBENEM18F4iSzwSBaP7s8-2F4Tf3gThs612WoZyXAGJsQS0-2F0YavO6IUByJuzGeEjD323nIXaaoH-2Br4qL5M704btnmvGabOMoskywy46e1cYlVaSVO70REAoWuz3Q-3D-3D) (Aug 20), *Anastasia Marchenkova*, a quantum physicist who came up through experimental labs and now runs a company called *Markov*, described her business as, essentially, "the bullshit detector for the industry." Her test for any quantum claim is disarmingly simple: "How many Nobel Prizes do you need to make this work?" In other words, is this an engineering problem we can grind out, or does it secretly require a scientific breakthrough nobody has made yet? Her most important warning is one that applies just as much to AI as to quantum. When a company touts a benchmark, she runs the new hardware against *the best classical (ordinary-computer) algorithm*, not against a weak baseline. She used a textbook example: a famous quantum algorithm (Grover's) is often sold as a "quadratic speedup," but that's a speedup over brute-force search, not over the cleverest classical method, which may be nearly as fast. Strip out the strawman and a lot of "quantum advantage" shrinks. She extended the same skepticism to AI vendor numbers, noting "you're only getting maybe 40% of the top-line benchmark" in real production, and that the same AI job on different chips can cost "double, triple" depending on details. Two things she said land directly on this newsletter's beat: * *The scale of the prize is real.* She cited that roughly *60% of the compute at US national-lab supercomputer centers goes to quantum-chemistry calculations*, simulating how molecules behave. That is precisely the bottleneck faster computing (quantum, or better AI, or better GPUs) would relieve, and it's why pharma and materials companies (she named Airbus and BMW on battery chemistry) tell her that "anything can help us speed up the simulation time." * *AI progress feels incremental right now.* Testing the latest model releases, her verdict echoed the week's skeptical mood: "I was fascinated for like two hours. And then I found the edges... we still have a lot to go with AI." She thinks the field is on "Gary Marcus's trajectory of incremental improvements" and will need "something quite different," new architectures, for the next real leap. At the same time, she said sentiment inside the quantum field has genuinely shifted: people "are no longer questioning if we're going to get there." *Why it matters:* it's healthy to hear a quantum insider apply a hype filter to her own industry. The signal to keep: the demand is unquestionably there (60% of national-lab compute is molecular simulation), but every "we simulated X atoms" or "we beat the benchmark" claim deserves the question, *versus the best classical method, and does it survive production?* ### Sam Altman: curing disease is more important than automation, and not very good today In a wide-ranging biographical conversation on [David Senra](http://url7324.matterfact.com/ls/click?upn=u001.idHmPrr2Geh7KYLAsTy7NkrIVb-2FgA4pmf2rMXQwGcOgGzytAu17kbJDO1xI1T8O0ZV85WH-2F5Tt7wxu-2BwTsnqBkrV04P16Hp87c3lb6FRRXuEATEoxOqEyN0f2hoK1dhkX45q-2FatVYGDfN6XPOMoF6w-3D-3DcAAu_7mLGwmUci-2BLaXswv9WX1yTgqn3Wad-2FotHhzHgSNAZbWzrmRvyIis1vlKQMAtPDEybJRPxCHA5ZfFPkVoOwRvscHkeyQfqHUaJEp86L9E-2B0fNMzMHtldu97XqG-2F2rTiP4qq24uOdZp-2FBSnX8bXK7q0oplPia-2BnZ-2FiNzlEhK-2BUhAj7WvK-2FT8R5Yp3EROQNBemdtsxhAaR-2Bu1h4DoJTkqkt7A-3D-3D) (Aug 23), OpenAI's Sam Altman said he is "extremely interested in what AI can do to advance scientific discovery," and that the prospect of AI helping "discover new physics and cure diseases" is, in his view, likely to be "one of the most important areas, even more important than automation of other tasks." He tied it back to the founders of the field, Alan Turing and Claude Shannon debating in the 1940s whether a computer could one day "cure diseases." His candid assessment of the present, though, fits this week's theme. Pressed on whether AI is actually doing this yet, he allowed it "is discovering other stuff" and doing novel math, but on the broader promise his own words were: "not very good." The optimism is about the destination; the honesty is about the distance still to travel. ## The debate *Can more raw AI intelligence, by itself, speed up drug discovery, or is the real bottleneck the physical world?* This week crystallized a genuine split that has been building for a month. * *The optimists (recapped from prior weeks):* Insilico's Alex Zhavoronkov describing a "prompt to drug" pipeline of 32 candidates; Ginkgo's GPT-5-designed experiments beating a Stanford lab; IBM and Cleveland Clinic's leap to 12,600-atom simulations. The through-line: give the models enough capability and compute, and the discovery loop collapses from years to months. * *The skeptics (this week):* Abhi Mahajan arguing that biology's feedback loops are "roughly as slow as they were five years ago" because you can't skip running the experiment in cells, mice, and people, and that the part of drug discovery AI has *not* cracked (does it work in a body) is exactly the part that kills most drugs. Anastasia Marchenkova insisting that most flashy speedup claims wilt when measured against the best classical methods in real production. Even Altman conceding today's tools are "not very good" at science. *The reconcilable version, and where we come down:* these sides are arguing about *different stages of the pipeline*, and both can be right. AI is genuinely, measurably winning at *design*, proposing molecules and proteins, where there's a clear score to optimize (Mahajan's "interesting hills to climb"). The unsolved problem is *translation*, predicting whether a designed molecule will actually be safe and effective in a living human, where there is no clean scoreboard and where the clock is set by biology, not by GPUs. The most interesting companies (NOETIK is a clean example) are the ones aiming AI directly at that translation gap, using patient data to predict *who* responds, rather than claiming to have removed the wet-lab, in-body step entirely. The tell to watch for going forward: does a company claim AI is speeding up *molecule design* (believable), or that it has compressed *clinical validation* itself (extraordinary, and requiring extraordinary proof)? ## Stocks in play *Eli Lilly (LLY), $1,175.49*, down about 1.2% on the day (previous close $1,189.41), and down roughly *5.6% on the week* from $1,245.69 a week ago. 52-week range $712.05–$1,292.65; market cap about $1.1 trillion. Despite a stack of positive headlines, the stock pulled back, a reminder that in obesity drugs, the market is trading pricing and coverage, not press releases. The week's news: * *Orforglipron pill (Foundayo) launched in the UK* (Aug 24). Britain is the first European country to get Lilly's once-daily weight-loss *pill* (as opposed to the injectable shots). It's initially sold by private prescription for weight management and type-2 diabetes, priced at roughly £100–£120 per month, while it's reviewed for coverage by the UK's national health service. The UK regulator authorized it about two weeks earlier. A pill is strategically important because it's far easier to manufacture and distribute at scale than injectables. * *Zepbound cuts total healthcare costs in older adults* (Aug 26). A real-world study of adults over 55 found that staying on Zepbound (Lilly's obesity shot) was linked to *lower overall healthcare costs* versus similar untreated adults, driven by fewer hospital admissions and ER visits. At six months, costs were up to 15% lower (up to about $181 per patient per month); by 12 months, up to 38% lower (up to about $607 per patient per month). Notably, starting at 12 months the estimated savings *exceeded* the $195/month cost of treatment under Medicare's GLP-1 Bridge program. This is Lilly building the economic case that insurers should *pay* for these drugs because they save money later. The stock still fell about 3.3% that day. * *FDA clears the Roche/Lilly Alzheimer's blood test* (Aug 24). The FDA cleared the Elecsys pTau217 blood test, developed with Roche, to help identify Alzheimer's-related amyloid buildup in people 55+ showing signs of cognitive decline, potentially avoiding more invasive PET scans or spinal-fluid tests. A useful reminder that Lilly's franchise isn't only obesity; its Alzheimer's diagnostics-and-drug effort is advancing too. * *Coverage and policy crosscurrents.* PepsiCo told some employees that weight-loss drugs (both Lilly's Zepbound and Novo's Wegovy) will no longer be covered by company insurance starting in October, calling them "one of the fastest-growing costs" (Aug 25), a live example of the coverage risk that has been dogging the whole category. Separately, HHS is reportedly planning *two new FDA deputy-commissioner roles, one focused on technology and the intersection of health and AI* (Aug 25), worth watching as a signal of how the FDA intends to regulate AI in drug development and diagnostics. *Schrödinger (SDGR), $20.41*, up 4.83% on the day and roughly *+4.2% on the week* from $19.58. 52-week range $10.95–$23.02; market cap about $1.5B. The move looks like drift rather than a specific catalyst. *Recursion Pharmaceuticals (RXRX), $3.44*, down 1.43% on the day but roughly *+3.0% on the week* from $3.34. 52-week range $2.77–$7.18; market cap about $1.8B. ## Read-throughs * *The verification bottleneck is the sector's real gating factor, and it's a feature, not a bug.* The single most useful idea this week is Mahajan's: AI accelerates the stages with a clear scoreboard (design), and stalls at the stage without one (does-it-work-in-a-body). For anyone assessing an AI-drug company, that's a diagnostic. A company promising faster *molecule generation* is making a believable claim. A company implying it has compressed *clinical validation* is claiming to have solved the thing the whole industry hasn't, and the burden of proof should be correspondingly high. * *"Versus the best classical method, in production" is the right filter for every speedup claim, quantum or AI.* Marchenkova's discipline is a good habit to import. When the next "we simulated X atoms" or "we beat benchmark Y" headline lands (and given that ~60% of national-lab compute is molecular simulation, there's real money chasing these), the questions are: compared to the best ordinary computer, not a strawman? And does it hold up in a real workload, not just a demo? * *China's parallel-trials strategy keeps surfacing as the competitive wildcard.* For a third time in recent weeks, a guest flagged that the thing protecting Western drug developers, the requirement to independently verify a drug, is exactly what China is attacking by running many early trials at once. The moat is real today; the erosion is worth tracking. * *The obesity trade is about price and coverage, not headlines.* Lilly shipped a pill launch, a cost-savings study, and an FDA clearance in one week and the stock fell. The PepsiCo coverage cut is the tell: the market's worry is who pays and at what price, and every employer that drops coverage feeds it. Lilly's cost-savings study is the counter-argument it's building for insurers. Watch which side accumulates more evidence. * *The field's own leaders are setting expectations low for now.* Sam Altman calling AI-for-science "one of the most important areas" but "not very good" today is a useful anchor. The prize is enormous and widely believed in; the honest near-term read, even from the most bullish corner of AI, is patience. ## What changed vs last week * *Center of gravity flipped from optimism to a reality check.* Last week's issue was carried by the "prediction to generation" optimists (Insilico's deep pipeline, Ginkgo's GPT-5 lab results). This week the strongest, most credible voices were *skeptics who have actually built these systems*: Mahajan on the limits of AI-in-biology, Marchenkova on hype-testing quantum and AI claims. The debate matured from "look what's possible" to "here's what's actually hard, and why." * *NOETIK got a second, independent, and more skeptical source.* We introduced NOETIK last week via its founder (Ron Alfa, on Once a Scientist). This week a former NOETIK scientist described the same approach from the inside, with more technical detail and franker caveats ("the value proposition is still a little bit fuzzy"). Two independent sources in two weeks is a real signal that this predict-who-responds approach is a live idea in the field. * *The quantum thread got its counterweight.* Last week: IBM and Cleveland Clinic bullishness (12,600-atom simulation). This week: an inside-the-industry skeptic with a "how many Nobel Prizes?" test. The thread is now two-sided. * *On the watchlist:* Anthropic's rumored hire of John Jumper and any named preclinical program; the still-unverified "AI-designed drug in Phase 3 regulatory review" claim (note Insilico's separate, own claim of one drug in Phase 3); Schrödinger's Bunsen/BMS at-scale deployment; Recursion's November Phase 2 data (TUPELO/FAP) and any Roche/Genentech milestone; Chai's $400M round details; whether NOETIK's "reverse drug discovery" generalizes; and Ginkgo's OpenAI autonomous-lab collaboration. --- ## Get the full transcripts on Matterfact This briefing was produced with Matterfact, which indexes the complete, full-text transcripts of 120M+ podcast episodes alongside SEC filings, earnings calls, and financial datasets. 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