# Schrödinger Wakes Up and Lilly Wires Its AI Drug Factory - AI Drug Discovery Weekly - Week of September 17, 2026 > AI Drug Discovery Weekly podcast synthesis for the week of September 17, 2026. Schrödinger's stock jumped roughly 61% on the delayed Tectora launch and a likely short squeeze, Eli Lilly wired its TuneLab AI platform to Twist Bioscience and Ginkgo Bioworks, and a run of thoughtful podcasts mapped where AI genuinely helps in drug discovery and where biology still says not yet. ## AI Drug Discovery Weekly ### Week of September 17, 2026: Schrödinger Wakes Up and Lilly Wires Its AI Drug Factory --- *Issue #12, Week of September 10–17, 2026.* ## TL;DR - *Schrödinger (SDGR) went vertical: up roughly 61% in a week and about 26% on Friday alone, finishing near $30.24, a fresh 52-week high.* The trigger was real but a week old: the September 9 launch of a new company, Tectora Therapeutics, that Schrödinger helped create and kept a stake in. Friday's giant jump itself came with no new headline, it looks like momentum and short covering piling onto that story. Worth watching for a cooler head. - *Eli Lilly's AI platform, "TuneLab," signed its first two concrete outside partnerships this week, with Twist Bioscience and Ginkgo Bioworks.* After weeks of the platform being talked about but nothing tangible landing, Lilly is now visibly building the plumbing to feed real lab data into its AI models. This is the most down-to-earth AI-drug-discovery development among the public names right now. - *A separate podcast revealed the scale some of these AI-protein deals have reached:* ProFluent, a startup out of Salesforce's old protein-AI research, has closed "multi-billion-dollar contracts with Eli Lilly" for AI-designed proteins its founder says beat CRISPR at precision gene editing. - *The big-picture debate matured in a useful way.* Last month experts were stuck on "AI doesn't understand biology." This week the smartest voices landed somewhere more constructive: AI is genuinely good at *designing molecules and proteins* (the part that can be tested quickly in a lab), but it *cannot* speed up the years of testing drugs in human bodies. So the near-term money is in the boring-but-real operational wins, running trials, sorting patients, grinding through paperwork, not a magic cure machine. - *Recursion (RXRX) stayed quiet on news for a seventh straight week*, though the stock bounced about 12%. Its only appearance was a passing mention that founding CEO Chris Gibson "recently stepped aside." ## What's New ### 1. Schrödinger's Stock Exploded: Here's What Actually Happened The single biggest move in our coverage universe this week was *Schrödinger (SDGR)*, a company that sells physics-based software for designing drug molecules and also runs its own drug pipeline. The stock closed around *$30.24 on Friday, September 17, up about 26% on the day and roughly 61% over the week* (from $18.80 the prior Friday). That put it right at a new 52-week high; its previous high was $30.41. A move that size demands an explanation, and the honest one is a little unsatisfying. The real catalyst is a deal announced *a week earlier, on September 9*: Schrödinger teamed up with two well-known life-science investors, *New Enterprise Associates (NEA)* and *RA Capital*, to launch a new drug company called *Tectora Therapeutics*, focused on immune-system and inflammation diseases. Tectora simultaneously raised a *$55 million Series A*. Schrödinger didn't put in cash, it contributed *two early-stage small-molecule drug programs* in exchange for an equity stake plus the right to future milestone payments and royalties. This is a model Schrödinger has used before: help start a company, hand it some assets, keep a slice, and let others fund the expensive clinical work. On the announcement, Schrödinger's head of therapeutics, Karen Akinsanya, noted its earlier "launch-and-keep-a-stake" companies, Nimbus, Morphic, Structure, and Ajax, have together generated *over $750 million in proceeds* for Schrödinger (TheFly and MT Newswires, Sep 9). So why did the stock rocket a full week *after* that news? There was *no fresh company announcement on Friday*: a careful search turned up only routine paperwork (a small grant of stock to three new hires; TheFly, Sep 17) and no analyst upgrades, no clinical data, no takeover bid. On the same day, the broad healthcare sector was up only about 0.3%, so this was a Schrödinger-specific move, not the whole group rising. The most likely explanation is a *delayed momentum surge and short squeeze*, traders who had bet against the stock scrambling to buy it back, layered on top of the Tectora story finally catching investors' imagination. That matters because momentum-driven spikes with no new fundamental news often give some of the gain back. This is a "know why you own it" moment, not a "the business just changed" moment. ### 2. Eli Lilly Quietly Assembled the Most Concrete AI-Drug-Discovery Machine Among the Public Names For months, *Eli Lilly's (LLY)* in-house AI platform, called *TuneLab*, has been more talked-about than seen. TuneLab is Lilly's collaborative artificial-intelligence system, with models trained on decades of Lilly's own proprietary drug data, that the company lets outside biotechs tap into. This week it stopped being an abstraction. Lilly signed *two separate data partnerships in two days*: - *Twist Bioscience (TWST)* will supply antibody-characterization data services to TuneLab, including for a Lilly model called *AbLab* that predicts whether an antibody will actually be manufacturable and stable ("developability"). In plain terms: TuneLab users can order real wet-lab antibody work from Twist using Lilly-preferred protocols, then feed those results back to sharpen the AI (TheFly, Sep 16). - *Ginkgo Bioworks (DNA)*, through its *Ginkgo Datapoints* arm, will generate discovery data, testing small molecules and antibodies for developability, for companies using TuneLab. Ginkgo's John Androsavich framed it as "closing the loop between digital design and biological reality" (TheFly, Sep 16). Why this is the week's most important AI-discovery news even though it made no splash: the whole bottleneck in AI drug discovery is *good data*. A prediction model is only as good as the real lab measurements it learns from. By wiring in Twist (for making and testing antibodies) and Ginkgo (for generating experimental data at scale), Lilly is building a *data flywheel*: design in software, test in the lab, feed the results back, design better. Among the public names we cover, this is the most tangible version of that loop anyone has shown this week. Lilly had a busy week beyond TuneLab. It *closed its acquisition of AtaiBeckley (ATAI)*, a company developing fast-acting brain therapies for mental health, whose lead program *BPL-003* targets treatment-resistant depression (press release via TheFly, Sep 11). And Wall Street warmed up: *Berenberg upgraded Lilly to Buy from Hold and raised its price target to $1,400 from $1,220* (MT Newswires, Sep 15), while *Goldman Sachs nudged its target to $1,371 from $1,367, keeping a Buy* (MT Newswires, Sep 17). The average analyst target now sits around $1,359. The stock finished at *$1,152.13, up about 2.6% on the week.* (For context, last week's news was HSBC's lonely bearish "Reduce" call with a $940 target, this week the tone flipped decisively the other way.) ### 3. A Podcast Surfaced Just How Big the AI-Protein Deals with Lilly Have Gotten On *The MAD Podcast with Matt Turck* (Sep 10), AI researcher *Richard Socher*, former chief scientist at Salesforce, now running the search company You.com and the AI-research firm Recursive, gave a striking data point. Back in 2018, his Salesforce team trained one of the first large "language models for proteins," called *ProGen* (lead author Ali Madani). Madani went on to found a company, *ProFluent*, and, in Socher's words, "they've now closed multi-billion-dollar contracts with Eli Lilly... because they've created new kinds of proteins that are even better than CRISPR-Cas9 in gene editing, more specific and targeted for changing certain genes inside living people" ([The MAD Podcast with Matt Turck](http://url7324.matterfact.com/ls/click?upn=u001.idHmPrr2Geh7KYLAsTy7NkrIVb-2FgA4pmf2rMXQwGcOjEFMCYuq1uHLHg-2FdruGsLhP38QouPL3jo-2FE7MKlnicrJyleexV6Fapn7epXYl9l3MZLaLG5ayF172khR0GwQOmL7vL8xeiPQ5T9FMpShugoA-3D-3DSLGc_7mLGwmUci-2BLaXswv9WX1yTgqn3Wad-2FotHhzHgSNAZbWpxUEEW8jqMo1I3RFWPc2cZ8KlDqvbs10GC9whr6X6yk9FPAhJ1Sdy-2Bu8rlRuCJy91rX9OZGZCbek4LZyjIpor6Y9idUZnYH2bAesj8z1LgPh5ghC2MEjrmuzYrCG3gBcObZ8bLyaIm3IQydzBmLvVI5qJQr8X1MbX71xtkI0GSQ-3D-3D)). The bigger idea Socher kept returning to is that biology is shifting "from *reading* biology to *writing* it," treating proteins like programmable building blocks you can redesign. He also offered a memorable, counterintuitive point: when you want an AI to *invent* a genuinely novel protein, a model's tendency to "hallucinate" (make things up) can be a *feature, not a bug*, it's how the system explores designs no human has tried. The catch, he stressed, is data. Unlike text, math, or code, which AI can practice on endlessly through simulation, "we're just nowhere near having enough training data for biology," which is why he pointed to companies like *Tahoe Therapeutics* and *Peril Bio* that mass-produce experimental cell data. One more eye-catching aside: he described a clinical trial that "created a different drug for every different patient... a first for the FDA," and noted that the difficulty of running trials in the US and Europe is pushing many to China and Australia. *(Note: Socher's firm is called "Recursive," which is a different company from Recursion Pharmaceuticals, the RXRX we cover, easy to mix up.)* ## The Debate: Where Does AI Actually Help in Medicine? This was the richest part of the week. Several thoughtful podcasts, from different angles, converged on a more grown-up version of the argument. A month ago the sharp line was "AI speaks English, not biology." This week the experts drew a *cleaner map* of where AI works today and where it doesn't. *Point 1: AI really is good at the "design a molecule" part, because that can be checked fast.* On *AI For Pharma Growth* (episode E235, Sep 15), Dan Prattie, founder of a firm called Quadron, put it cleanly: "When it comes to finding novel proteins, I think AI will be very great, because it's an empirical test that can be run quite quickly. It bridges to the physical realm quite readily" ([AI For Pharma Growth](http://url7324.matterfact.com/ls/click?upn=u001.idHmPrr2Geh7KYLAsTy7NkrIVb-2FgA4pmf2rMXQwGcOjgD7pRzCIDByfg-2FzaAU56XSI2GADEoOQyh36e-2FCYREpDrLfys28B91E0H-2FdSdeA0y8ZeUsRHP29INQLFo3qSJvN7AAyJBPga-2BVyfPHvw1rpg-3D-3DG-Nu_7mLGwmUci-2BLaXswv9WX1yTgqn3Wad-2FotHhzHgSNAZbWpxUEEW8jqMo1I3RFWPc2cZ8KlDqvbs10GC9whr6X6ypYgvYbFLYbX75-2BegEhrjQfrD8GGi8tOtVB8l8skkHyMZ7FkZ6eyEN0IHeg0V1kJemf1KYZtQgj9qgJVu0VQ8C-2BpZC5vhb62VyiefQUfO-2FDah-2FeHLXKIbA02SlzR0XvZ3w-3D-3D)). Socher's ProFluent–Lilly example above is the real-world version of exactly this. *Point 2: but AI cannot compress the slow part, testing drugs in actual human bodies.* On *WSJ's Take On the Week* (Sep 13), the Journal's David Wehner was blunt about the limits: yes, AI is already helping recruit patients for trials and diagnose disease, "but in the end, we still have to test drugs in human bodies, and we have to wait years and years, and AI can't accelerate that, not yet. So AI is churning up drug candidates, but... we still have to run them through these very slow clinical trials. And so we just don't know [if it's working], and it will take years to find out" ([WSJ's Take On the Week](http://url7324.matterfact.com/ls/click?upn=u001.idHmPrr2Geh7KYLAsTy7NkrIVb-2FgA4pmf2rMXQwGcOiRNGL0tJQ2Smr-2BdLG-2F8Fc0cwpYdvdNNEjhfOF9Q7sn7LS7Zi2LJK7mBjAcXBJhBC7F6ufahUeNhxkXFtGI7qe2zhgwZX7ADNEwDES21XbrZw-3D-3DXQ6n_7mLGwmUci-2BLaXswv9WX1yTgqn3Wad-2FotHhzHgSNAZbWpxUEEW8jqMo1I3RFWPc2cZ8KlDqvbs10GC9whr6X6ypGuMhS-2FsxrooUtIuQRU39BoWDix6ApoD2YIQuEHmVO5ewE8zu-2BjmlCyaBeCFPmUiPS7FXD9jCsoHp0-2FO0ZtrRO9k7PVr-2B8F8Y1m4WvTefeefa3wIAyWpAU5RrU9SSl2zA-3D-3D)). *Point 3: so the near-term, bankable value is operational, not scientific.* The most substantive voice of the week was *Andrii Buvailo*, a long-time analyst of the AI-in-pharma industry, on *Talking Precision Medicine* (episode #61, "The Kodak moment for drug discovery," Sep 17). His argument, in plain terms: don't expect AI to press a button and spit out a finished drug. The genuine 2026 momentum, he said, is in the "operational" grind, "shortening the time for the bureaucracy to go from pre-clinical to clinical," chewing through "hundreds or thousands of pages of documents," picking clinical-trial sites, sorting which patients to enroll, and spotting where a trial might fail *before* you commit to it. On the science itself, he agreed with the others: "AI cannot compress the scientific and experimental part, biology is biology." He described today's AI more as a "co-scientist" or "brainstorm partner" than an autonomous inventor ([Talking Precision Medicine](http://url7324.matterfact.com/ls/click?upn=u001.idHmPrr2Geh7KYLAsTy7NkrIVb-2FgA4pmf2rMXQwGcOj-2BThyaFnQWjWlbr-2FwbnxfeMagQ4NHVqfwIu5jC-2FR6KK3-2BAuReGSGKmg8VmVwYPfVu39sQXbyVlFB3t99O1YAYyI3uf9p-2B-2FYAZOz1scohu3-2Fg-3D-3D48G5_7mLGwmUci-2BLaXswv9WX1yTgqn3Wad-2FotHhzHgSNAZbWpxUEEW8jqMo1I3RFWPc2cZ8KlDqvbs10GC9whr6X6yteRV1HTymm9UbLuJpN8WndEmGr7cZlVzhzDGh-2FOEE5PKquh7zQKpy2GC2DIqE-2Fa-2FTQWhjLedd-2F8eFDfh-2FPq-2F6k4ku3NtA7yg4zT2WiOkS5vynRXnrUxZENLuO8B5RYBeg-3D-3D)). *Point 4: a warning for investors on how to value these companies.* Buvailo delivered the line every AI-biotech shareholder should tape to their monitor: no matter how impressive a company's AI platform is, "at least right now in the capital markets, *you're worth your lead [clinical] asset and not a dollar more.*" He also pushed back on the popular scorekeeping method of counting "AI-discovered drugs" in clinical trials, calling it "a very limited and probably skewed way to understand if AI is having an impact," because in practice humans and AI are so intertwined that you can rarely say a drug was purely AI-designed. He pointed to a widely praised report by AI-and-biology pioneer Daphne Koller ("no magic wand") as the sober take, and suggested better yardsticks: productivity over the years, pipeline growth, or, for the software sellers, simply user counts and satisfaction. He also noted that judging AI's true impact will take "a generation of these discoveries and products going through clinical development" before we have enough data points. *A related "governance" worry.* The AI For Pharma Growth episode framed a quieter risk: as AI moves "from the edge of pharma operations to the center of it," drafting regulatory submissions, screening candidates, summarizing trial data, a "trust crisis" is forming around whether executives, regulators, and patients can rely on these systems. Prattie flagged a real tension for drug companies: an AI trained only on a company's internal data is compliant with regulations but weaker; a powerful public model is better but not compliant, so a human expert "curating across them" becomes essential. His blunt reality check: banning unsanctioned AI tools won't work ("shadow AI... is here to stay"); the job is to make their use safe and rewarded. The through-line: this is a *more investable framing* than the doom-or-hype extremes. It says AI's payoff in drug discovery will show up first in efficiency and success rates, gradually, and mostly inside big companies with lots of proprietary data and their own trials, which, notably, describes Eli Lilly and its TuneLab flywheel far better than it describes a small platform company living or dying on one clinical readout. ## Stocks in Play | Ticker | Company | Fri close (Sep 17) | Day change | Week-over-week | 52-week range | Market cap | This week's driver | |---|---|---|---|---|---|---|---| | SDGR | Schrödinger | $30.24 | +26.4% | +60.9% | $10.95 – $30.41 | ~$2.3B | Tectora launch (Sep 9) catching fire; momentum/short squeeze, at a 52-wk high | | LLY | Eli Lilly | $1,152.13 | +1.3% | +2.6% | $712.05 – $1,292.65 | ~$1,085B | TuneLab signs Twist + Ginkgo; AtaiBeckley deal closes; two analyst upgrades | | RXRX | Recursion | $3.54 | +11.0% | +12.0% | $2.77 – $7.18 | ~$1.9B | No company news (7th quiet week); a technical/sympathy bounce off near-lows | *Schrödinger (SDGR).* The move is dramatic but the fundamentals underneath it are a week old. The company's "launch a biotech, keep a stake" playbook is real and has paid off before (>$750M from past ventures), and Tectora adds another shot on goal. But Friday's 26% spike had no new news behind it, the healthcare sector was roughly flat, and a stock sitting exactly at a 52-week high after a squeeze is priced for good news, not disappointment. Respect the momentum, but don't confuse it with a step-change in the business. *Eli Lilly (LLY).* The steadiest and, this week, the most *substantively* interesting name for our purposes. The TuneLab partnerships with Twist and Ginkgo are the clearest sign yet that Lilly is turning its AI ambitions into an actual, data-fed operating system rather than a slide deck. Add the closed AtaiBeckley deal and back-to-back analyst upgrades (Berenberg to Buy, $1,400; Goldman to $1,371), and sentiment has clearly turned since last week's bearish HSBC note. There's no direct read on how much TuneLab moves Lilly's ~$1.1 trillion market value yet, but strategically, it fits the "big company with proprietary data wins the AI race" thesis the podcasts kept describing. *Recursion (RXRX).* Seven weeks with no company news. The stock's ~12% pop looks like a bounce off depressed levels rather than anything fundamental, it's still roughly half its 52-week high. The only mention anywhere this week was Buvailo noting that founding CEO *Chris Gibson "recently stepped aside,"* and recalling that Recursion, under Gibson, used a "fail fast" yardstick, how many drug candidates could its AI *kill* quickly, as a measure of whether the platform was working (Talking Precision Medicine, Sep 17; leadership change mentioned in passing, not independently confirmed here). The catalysts still on the calendar: November Phase 2 data in a form of colon polyps (the TUPELO study) and a possible milestone from its Roche/Genentech neuroscience partnership. ## Read-throughs *Big Pharma's AI edge may come from owning the data and the trials, not from the flashiest algorithm.* Put the week's threads together, Lilly's TuneLab flywheel, ProFluent's multi-billion-dollar Lilly protein contracts, and Buvailo's "you're worth your lead asset" warning, and a pattern emerges. The companies best positioned to profit from AI in drug discovery may be the large drugmakers with decades of proprietary data and the machinery to run their own trials, using nimble AI shops as suppliers. That's a more favorable setup for an Eli Lilly than for a small, single-platform stock. *Personalized cancer vaccines keep pulling AI into the spotlight.* On *Motley Fool Hidden Gems Investing* (Sep 14), the hosts highlighted *Moderna's "Maestro" system*, which helps orchestrate personalized cancer vaccines (developed with Merck) by reading a patient's tumor DNA and predicting the best immune response, and *Crystal Biotech's* use of AI for real-time quality control on its gene-therapy production lines ([Motley Fool Hidden Gems Investing](http://url7324.matterfact.com/ls/click?upn=u001.idHmPrr2Geh7KYLAsTy7NkrIVb-2FgA4pmf2rMXQwGcOiQPR6oTEr3tnYCIl31E7tZMSztv17gWgb8xuR7r30xcJYLsKs6v0G4q1aJDs9Aa28LzZxfeKH5O7rff3DwNc4FTerRzitk9Cxuj1J9Wj-2BS-2Bw-3D-3DX0Bi_7mLGwmUci-2BLaXswv9WX1yTgqn3Wad-2FotHhzHgSNAZbWpxUEEW8jqMo1I3RFWPc2cZ8KlDqvbs10GC9whr6X6yi5nF7IXllMVrzPmsKK2I7KZQIA-2BNSF41OaKKrQjZ8SR7mFl94gSAhkETqtsc0ZkDGr914TYBBZMSIDjxm1WVD-2BIz8YrWDucbxQt8ndS-2BwN-2B4MgEpYu5nTBkQkyQn-2F2KLQ-3D-3D)). WSJ's Take On the Week made the same connection to the broader biotech rally driven by cancer-vaccine excitement, while cautioning that those breakthroughs "didn't really have a lot to do with AI." *Will AI kill the "outsourced trial" business, or make it boom?* The same Motley Fool episode weighed a live investor debate about contract research organizations (CROs), the firms like *MedPace* and *IQVIA* that run trials for biotechs. One fear: AI automates away the paperwork-heavy tasks CROs bill for. The counter, which the hosts leaned toward: if AI produces *more* drug candidates, there will be *more* trials to run, and the "tech-fluent" CROs will thrive while old-school ones fade. A useful reminder that AI's ripples hit the drug-industry supply chain, not just the drugmakers. *The "self-driving science lab" money keeps getting bigger.* On *Tech Brew Ride Home* (Sep 14), it was reported that *Jeff Dean*, the legendary engineer who recently resigned as Google's chief scientist, is raising money for his new startup *Discovery Loop* at a valuation "around $50 billion," up from about $10 billion just weeks earlier. Discovery Loop describes itself as "automating discovery to accelerate science and engineering" through the "parallel execution of thousands of experiments" ([Tech Brew Ride Home](http://url7324.matterfact.com/ls/click?upn=u001.idHmPrr2Geh7KYLAsTy7NkrIVb-2FgA4pmf2rMXQwGcOj9x7FeWpn5FR8uyrIpJ7zVBdKiJJ-2F0azYa-2F3LLWkuE54EqIdQgf89Y1hHG4uleZ73rLESa8fNfP-2FiyQQwpbo48aBbNf8xoXfHqIR7HTd6uiw-3D-3DrBkv_7mLGwmUci-2BLaXswv9WX1yTgqn3Wad-2FotHhzHgSNAZbWpxUEEW8jqMo1I3RFWPc2cZ8KlDqvbs10GC9whr6X6yicJ-2F2Oaccv6LMUhfWhTwRQ4202-2F7Jyu5jWQkhXsEU35uLCN4yXFyJXM3fouFO2X0lpv-2F8ao4b92TIbAs9ggDtpgtmXl18hSIa9AW0AYUXETsMk9-2B2NkE5EHlsLs2gXVXw-3D-3D)). It's not a drug company per se, but the eye-watering valuation shows how much capital is flooding toward "AI that runs the experiments," a theme adjacent to everything we cover. *China's speed advantage is now a pharma story, not just an AI story.* WSJ's Take On the Week described what it called a "DeepSeek moment" for the drug industry: a Chinese-partnered biotech produced data suggesting a better-targeted version of Merck's Keytruda, the world's best-selling drug at over $30 billion a year. The panel argued China's faster, cheaper trials and "fast follower" copies are a genuine competitive threat to early-stage US biotech. It reinforces Socher's separate point about trials migrating overseas. *Lighter mentions this week (for the record):* *MIT Technology Review Narrated* (Sep 16) covered startup Generation Lab's claim of a two-drug combo, "One Generation," meant to rejuvenate aging blood, screened using a microfluidic device rather than AI ([MIT Technology Review Narrated](http://url7324.matterfact.com/ls/click?upn=u001.idHmPrr2Geh7KYLAsTy7NkrIVb-2FgA4pmf2rMXQwGcOhf1eGza4z6nZvHRWnu2ZHYH8UqCSugIfD4oaCMpA9v26Vd649AOKhxF-2Byo6kVLahM6x5aEnbCBFKHDCGxKdU7iiWe7ColT9uld5rtikbfDoA-3D-3DfavH_7mLGwmUci-2BLaXswv9WX1yTgqn3Wad-2FotHhzHgSNAZbWpxUEEW8jqMo1I3RFWPc2cZ8KlDqvbs10GC9whr6X6ynPrUp3-2FM-2FiGKLvgdWzo311L91N-2BVH-2FDRm2LBULB2dN8LxtC4gBFT79AT3MOfKATB99MdKcvPQPx4mdmrtrepCDoE7xeI2JfyWLfOwUCnzbB7eJBagY4KFSm-2ByLWqiseGg-3D-3D)). *Smart Biotech Scientist* (#288, Sep 17) touched on using AlphaFold to spot protein weaknesses during manufacturing ([Smart Biotech Scientist](http://url7324.matterfact.com/ls/click?upn=u001.idHmPrr2Geh7KYLAsTy7NkrIVb-2FgA4pmf2rMXQwGcOjMhrjx45pLNCsSErdZZF1JQ-2Bqa-2Ffr0L2O-2Bg4Wgf2pemV-2FkeT3rM9wzxuFBbQzBKI3MfW6nbHIt9FrLnDmPsMRz1s7NfLDGuOVMzBrS5KJwqw-3D-3DVMTs_7mLGwmUci-2BLaXswv9WX1yTgqn3Wad-2FotHhzHgSNAZbWpxUEEW8jqMo1I3RFWPc2cZ8KlDqvbs10GC9whr6X6yjiKpl7efe9qevIaVHxOTXuRpATCefEwVowQcfYxcapEZR4aXL2G0HHvRy7rhctRtmXVhMldA3stmK4x-2Fu-2B65cWzf05FjXPW1sAeQ1ERRlrdEMiyRQAAV965CDTnkI7V1g-3D-3D)). *Nature Podcast* (Sep 11) and *Big Take Asia* (Sep 15) revisited DeepMind's AlphaGenome map and China's biotech surge, respectively, both themes carried over from prior weeks. ## What Changed vs Last Week - *Schrödinger did a complete 180.* Last week SDGR was the *worst* of our three names, down about 9% and, oddly, falling right after the Tectora deal was announced. This week it was the best by a mile, up nearly 61% to a 52-week high as that same deal finally ignited a momentum surge. Same news, opposite reaction, one week apart, a good case study in how the market's mood, not just the facts, drives short-term prices. - *Lilly's AI story went from vague to concrete.* Last week LLY had no AI news at all and a lonely bearish analyst call (HSBC, Reduce, $940). This week TuneLab landed its first two real ecosystem partners (Twist, Ginkgo), the AtaiBeckley deal closed, and two firms upgraded their targets ($1,400 and $1,371). The narrative around Lilly-as-AI-platform got its first hard evidence. - *The debate grew up.* Last week's fault line was philosophical and a bit gloomy, Lilly's own CEO saying "AI models speak English; they don't speak biology." This week the conversation became more practical and, frankly, more useful: AI *is* good at designing molecules (the fast-to-test part), it *can't* speed human trials (biology is biology), so the real near-term value is operational, and, new this week, you should stop scoring the field by counting "AI-discovered drugs." - *Recursion stayed in the penalty box on news* (seventh quiet week) but the stock stopped falling and bounced ~12%. The only new color was the note that its founding CEO has stepped aside. - *Last week's landmark science stories cooled off.* The AI-designed bacteriophage genomes (Evo/Arc) and Anthropic's protein-binder claims that dominated last week did not generate genuinely new on-theme episodes this week; the center of gravity shifted back to the public companies and the practical debate. --- ## 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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