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Deep dives | Catalysts | Asymmetry. Everything posted is NFA.

Joined August 2026
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Welcome to SM Research Labs. I’m in my early 20s and have been passionate about markets since I started trading equities at 14. I eventually moved into crypto, where I was fortunate enough to make some good money. I now focus primarily on equities, with an emphasis on fundamental research, valuation and building proper investment theses. I started this account to document that process in a more formal way, learning as I go, improving with every thesis, and building a portfolio of my work that I can look back on years from now. Appreciate everyone who comes along for the journey.
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Proper alpha from the main account. $IAM's GPCR team is led by a Lefkowitz-trained head who runs Iambic's non-oncology programs. That points to a possible second pipeline that isn't priced in. Migraine? Weight loss? Hormones? All on top of the cancer drug pipeline.
To add to this, I found some alpha in $IAM’s old job listings, since taken down from its careers page. Iambic has an in-house GPCR drug team. GPCRs are the receptors behind about 1 in 3 FDA-approved drugs, including Ozempic, Wegovy and Mounjaro. In late 2025 Iambic hired a GPCR team: a director-level lead (role now filled), plus scientists screening small molecules against GPCRs, tasked with taking programs “from hit ID through development candidate selection.” Iambic’s Head of GPCR Biology runs its non-oncology programs. His background is in metabolic, renal, liver, respiratory and cardiovascular drugs. Small molecules can be pills. GPCR pills already sell in every area Iambic is linked to: • Obesity: Lilly’s Foundayo (GLP-1 pill) • Hormones: Crinetics’ Palsonify • Migraine: Nurtec, Ubrelvy (Iambic has a migraine deal with Lundbeck) • Immunology: Zeposia (AbbVie and Takeda partner with Iambic here) The prospectus lists Iambic’s own discovery programs in metabolic disease and endocrinology. All 3 of its named drugs are cancer drugs, and none hits a GPCR. If one of those metabolic programs is a GPCR pill, that’s the market Ozempic sells into. Iambic hasn’t said it is. For scale: GLP-1s did ~$73B in 2025, and Novo signed a deal worth up to $2.2B with Septerna for preclinical GPCR obesity pills. No GPCR program has been named yet. But you don’t hire a GPCR team and a Lefkowitz-trained head unless you plan to run GPCR programs. So the call option is: a possible path into commercial medicines like obesity and migraine, on top of the cancer pipeline. NFA
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What if?
🤖 Made with AI
Michael Carrick and Jensen sacked in one day. Year ruined
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$IAM picked up a lot of new eyes yesterday with four banks starting coverage. Here’s what you get for ~$665M, with ~$410M of that in cash. The AlphaFold comparison. NeuralPLexer was built with NVIDIA researchers at Caltech. NVIDIA co-owns a patent family and put $25M into the company ~15x faster than AlphaFold 3 by Iambic’s numbers. An independent benchmark clocked the first version at ~100x on the same chip Isomorphic, Alphabet’s AlphaFold drug company, is reportedly raising at ~$50B (Bloomberg) with no drug in humans Iambic, with a drug in 122 patients, is ~$665M. That’s about 1/75th of Isomorphic’s price, and the business beyond cash (~$255M) is under 1/190th, for a model that’s arguably on par on accuracy and ~15x faster by Iambic’s numbers. The drug IAM1363, a HER2 pill, 122 patients dosed. Iambic says it’s the only one known to bind the inactive form of HER2 It concentrates in tumours: 20-30x more drug in the tumour than in the blood, in biopsies from two patients 3 confirmed brain responses in breast cancer patients who’d already failed Enhertu and Tukysa. Nothing is approved for them Registrational trial as early as 2027, and two more drugs entering the clinic The flywheel Molecules designed Monday, made Tuesday, tested by Friday. A traditional pharma design cycle takes weeks to months. Iambic puts ~1,000 molecules through one every 7-10 days, and the CEO puts its pace at ~50x a typical pharma programme Every result goes straight back into the models, so each week’s designs start smarter than the last. More data makes better models, which make better molecules, which make more data That’s how IAM1363 went from programme start to the clinic in under 2 years, with the drug candidate found by month 7. The industry norm is ~6 years Enchant v3: 41B parameters, 4.5T tokens of training data, 6,000+ properties predicted, ~12M predictions a month. Google’s TxGemma tops out at 27B Against TxGemma, by Iambic’s numbers: kinase activity 0.69 vs 0.55, rat clearance 0.70 vs 0.35 With ~5% of the human training data, Enchant matched the best published human PK models trained on 100% The coverage All four of the banks that took Iambic public started coverage on Monday, all at Buy or Overweight Jefferies $35, BofA $30, JPMorgan $22, Citi $22. The average is ~$27, about 95% above $14 BofA’s case: Iambic uses AI to design new drugs, not just to find targets, and it trades below its listed AI peers The stock closed up 31.6% on the day Jefferies has the CEO on its AI drug discovery panel on 18 Nov Commercial validation AbbVie signed up the day Enchant v3 launched: $40M upfront, up to $670M in milestones Bayer’s deal names Enchant and NeuralPLexer. RevMed paid ~$25M for NeuralPLexer alone. Takeda signed a separate technology agreement on top of its drug deal Upfronts keep climbing: $10M (Lundbeck) → $27M (Takeda) → $40M (AbbVie). ~$2.4B in potential milestones across the book Isomorphic’s Lilly and Novartis deals brought $82.5M upfront. Iambic’s bring ~$77M. A similar deal book, at roughly 1/75th of the valuation Last December the CEO told investors more deals were coming. Three signed within nine months. The balance sheet ~$410M pro forma cash, plus AbbVie’s $40M upfront still to come. No debt Funded into late 2028: enough to start the registrational programme for IAM1363 and take two more drugs into the clinic The company is locked up too. No new shares before April without its banks’ sign-off Partner money stretches it further. The CEO says every new deal adds quarters of runway Therefore minimal dillution risk. Who’s buying IPO 5x oversubscribed (Bloomberg) Cathie Wood’s ARK has bought on 8 of 15 days since the IPO, ~2.4M shares across its funds (~5%) Druckenmiller’s Duquesne indicated interest in the IPO A director’s fund bought $11.5M in the IPO. No insider has sold The flow 80% of shares locked until April. The 10 biggest IPO buyers took 80%+ of the rest. Possible Russell add: preliminary list Friday, index buying 11 Dec CEO on Jefferies’ AI drug discovery panel, 18 Nov Of course NFA, Dyor
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$IAM’s AI drug-discovery flywheel: Monday: AI designs new drug molecules. Tuesday: Automated robots synthesise them. Wednesday–Friday: The lab tests them and feeds results back into the AI. Next Monday: The model is smarter and the cycle starts again. Traditional drug discovery: Months of design, synthesis and testing, with far slower feedback loops and more manual work. Iambic: A continuous design → make → test → learn cycle, potentially compressing years of discovery into months. Every cycle generates more data, better predictions and a potentially stronger competitive moat. IAM1363 went from programme start to clinical candidate in just 7 months. 3 drugs expected in the clinic by year-end 2026. The flywheel is spinning.
$IAM: Nvidia has been working with Iambic’s CEO since he was a professor at Caltech. They’ve written papers and filed patents together, and $NVDA engineers helped scale the models. On where that ended up: “they’ve also become major investors in the company and strong supporters.” That’s Tom Miller on Care Shift with Vik, filmed about 4 months ago, pre-IPO. A few other things I didn’t know. The lab runs on a weekly cycle. Molecules get designed on Monday, made on Tuesday by their automated chemistry setup, and tested later in the week. The results go straight back into the next design round. IAM1363 came out of that process. They found the clinical candidate in about 7 months and had human data at ESMO 3.5 years after starting the programme. The industry average to even reach the clinic is around 6 years. It was built to get into the brain, and by his numbers 40%+ of HER2 breast and lung patients end up with brain tumours. He also said they expect 3 drugs in the clinic by the end of 2026. If one candidate took 7 months, I doubt they stop at three.
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$IAM: Nvidia has been working with Iambic’s CEO since he was a professor at Caltech. They’ve written papers and filed patents together, and $NVDA engineers helped scale the models. On where that ended up: “they’ve also become major investors in the company and strong supporters.” That’s Tom Miller on Care Shift with Vik, filmed about 4 months ago, pre-IPO. A few other things I didn’t know. The lab runs on a weekly cycle. Molecules get designed on Monday, made on Tuesday by their automated chemistry setup, and tested later in the week. The results go straight back into the next design round. IAM1363 came out of that process. They found the clinical candidate in about 7 months and had human data at ESMO 3.5 years after starting the programme. The industry average to even reach the clinic is around 6 years. It was built to get into the brain, and by his numbers 40%+ of HER2 breast and lung patients end up with brain tumours. He also said they expect 3 drugs in the clinic by the end of 2026. If one candidate took 7 months, I doubt they stop at three.
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Iambic’s HER2 pill puts 20-30x more drug in the tumour than in the blood. They tested about six other HER2 drugs. None do this. $IAM From Tom Miller’s keynote at the Broad Institute, November 2025. The biopsy data comes from two patients. Iambic ran the same test on about half a dozen other HER2 drugs. None of them concentrate in the tumour. Most have less drug in the tumour than in the blood. Miller ties that to the safety: no significant rash or liver toxicity, the side effects that cap the dose on other HER2 pills. Clean safety is what lets you combine, and they’ve already started combinations with antibodies and ADCs in earlier-line breast cancer. One patient with leptomeningeal disease, where progression usually comes within about 6 weeks, had been on IAM1363 for 7+ months. On Enchant: with lots of cheap lab data and only ~5% of the human training data, it matched the best published human PK predictions trained on 100%. In one of their own programmes, standard lab assays couldn’t predict which compounds would last in dogs. Enchant could, and the programme moved forward towards an IND. Miller said readouts for a potential accelerated approval could come as early as mid-2028. The prospectus has a registrational trial starting as early as 2027.
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I finally picked up shares of $IAM this morning I read the research by @SmResearchLab & @rk8215 and decided to wait until the IPO banks initiated coverage, which they did this morning I bought a starter position around $10.50 this morning and price is already running away from me, so I'm hoping we see some dips soon to add more If you want to buy a lotto ticket that AI continues to revolutionize drug discovery, $IAM is the best high odds powerball
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"Not an AI infrastructure play." That line sums up $IAM. Most AI money is chasing picks and shovels. Iambic is using AI to make the actual product: medicines. Citi calls the early HER2 data "very encouraging." Three banks initiated today. Three Buys, targets ranging from $22 to $30. CEO Tom Miller will speak about AI drug discovery on a panel at Jefferies London, Nov 18. Good days are ahead…
Several banks initiated coverage of $IAM today: Citi: Buy, $22 price target JPMorgan: Overweight, $22 price target Bank of America: Buy, $30 price target Citi views the company as “differentiated” given its ability to use AI to design better medicines and called the early clinical data in HER2-driven cancers “very encouraging.” JPMorgan sees Iambic “at a key inflection point”, noting that its AI-driven discovery platform is beginning to gain traction through new partnerships and clinical results. BofA said Iambic, which uses AI to design new molecules, is “not an AI infrastructure play”, but is applying AI to create real medicines for patients. The bank added that real-world applications of AI in high-revenue areas like drug development is “where the industry is headed.” Separately, Iambic announced today that Co-Founder and CEO Tom Miller will attend the Jefferies Global Healthcare Conference in London (Nov 16–19). He will speak on a panel about AI in drug discovery on November 18.
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Big pharma’s biggest moat is decades of human clinical data. Iambic’s CTO thinks a small company no longer needs it. $IAM Enchant gets better at predicting how a drug behaves in humans by training on more lab data, which Iambic produces every week. For one human PK property, it made useful predictions after seeing human data on just 5 molecules. Lab work a startup can run every week starts to stand in for data a pharma giant spent decades collecting. That’s Fred Manby on the Data in Biotech podcast. It’s an older one, from around when NeuralPLexer3 came out, but it explains the tech better than anything else I’ve watched. Some discovery companies screen millions of compounds for one property. Iambic makes thousands and measures dozens of properties on each, because a drug has to get ~20 things right at once. The lab data flows back in and the models retrain automatically every week. Scientists come in on Monday to models that already know what happened last week. The model also says how confident it is, so they run the experiment with the best odds. At the time, Enchant was trained on 15 data types and he said they’d keep bolting on more. v3 launched with 16, and 41 billion parameters.
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Iambic took IAM1363 from programme start to human trials in under 2 years. The usual timeline, by the CEO’s numbers, is about 6 years. $IAM The lead series came within a few months and the clinical molecule by month 7. Tom Miller, on Catalio Conversations, puts their pace of AI-designed experiments at roughly 50x a typical pharma programme. A drug’s patent clock starts early in discovery, so every year saved before the clinic is an extra year of protected sales at the end, when sales are at their peak. His answer on why big pharma can’t just do the same: train a model on every dataset that exists and you hit a wall, because the data runs out. Iambic generates new data for each programme on demand through its weekly lab loop. Anyone can buy compute. You can’t download data that doesn’t exist yet. On NeuralPLexer vs AlphaFold: AlphaFold gives you a protein’s shape. NeuralPLexer predicts where a drug binds, what it does to the protein and how strongly. He called it step one of the 40-50 properties a drug has to get right, and Enchant covers the rest.
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Thread of $IAM interviews below 👇
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Iambic took IAM1363 from programme start to human trials in under 2 years. The usual timeline, by the CEO’s numbers, is about 6 years. $IAM The lead series came within a few months and the clinical molecule by month 7. Tom Miller, on Catalio Conversations, puts their pace of AI-designed experiments at roughly 50x a typical pharma programme. A drug’s patent clock starts early in discovery, so every year saved before the clinic is an extra year of protected sales at the end, when sales are at their peak. His answer on why big pharma can’t just do the same: train a model on every dataset that exists and you hit a wall, because the data runs out. Iambic generates new data for each programme on demand through its weekly lab loop. Anyone can buy compute. You can’t download data that doesn’t exist yet. On NeuralPLexer vs AlphaFold: AlphaFold gives you a protein’s shape. NeuralPLexer predicts where a drug binds, what it does to the protein and how strongly. He called it step one of the 40-50 properties a drug has to get right, and Enchant covers the rest.
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$IAM AI hype? Look at the drug it made. Most HER2 breast cancer pills also hit a near-identical protein in skin and gut. That causes the side effects that force dose cuts. Iambic's AI found a pocket on the cancer protein that its twin lacks. IAM1363 is the only known drug of its kind that binds there. It hits HER2 2,800x harder than the twin in lab tests. Closest rival: ~1,200x. It also reaches the brain, where up to half of these patients see their cancer spread, and beat the two leading drugs there in animal studies. It's in 122 patients now. For scale: Seagen paid $614M in 2018 for tucatinib, a brain-penetrant HER2 pill in a pivotal trial. IAM1363 is earlier but more selective. It's one of three $IAM drugs, before you count the NVIDIA-backed AI or the Enchant model AbbVie and Bayer pay to use. At ~$10: ~$480M market cap, ~$449M cash ($40M from AbbVie). Everything else is priced at ~$71M.
Added some $IAM here as it’s almost at cash value
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Let me get this straight: $IAM with $544M MC ($11.49) and $450M in cash has: - 12x faster engine - Drug already in trial - Druckenmiller buying at $16/share - ARK Invest buying at $16/share - Insiders buying with $11.5M at $16/share ...and Isomorphic Labs is valued 92x higher?
SITUATION DETECTED: Demis Hassabis’ Isomorphic Labs is in talks to raise $5 billion at a valuation of $50 billion to continue its AI-driven drug discovery work, per Bloomberg.
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This is big for the $IAM thesis. Isomorphic, a competitor Iambic names in its own prospectus, is reportedly raising at $50B. No drug in human trials yet. Iambic's NeuralPLexer is ~15x faster than AlphaFold 3 with similar accuracy, has a drug in 122 patients, and trades at ~$640M. 1/78th of the price.
SITUATION DETECTED: Demis Hassabis’ Isomorphic Labs is in talks to raise $5 billion at a valuation of $50 billion to continue its AI-driven drug discovery work, per Bloomberg.
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Great, now I have to go back and edit every $IAM article. "1/67th the price of Isomorphic" is already out of date. It's now ~1/78th. On a serious note, this is big for the thesis. Isomorphic is reportedly raising at $50B with no drug in human trials. Iambic has a drug in 122 patients, AbbVie paying for its AI, and trades at ~$640M. The gap keeps getting wider. Long $IAM, NFA.
SITUATION DETECTED: Demis Hassabis’ Isomorphic Labs is in talks to raise $5 billion at a valuation of $50 billion to continue its AI-driven drug discovery work, per Bloomberg.
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3. Enchant A 41B-parameter model that predicts how a drug behaves in the body before it’s made. AbbVie signed up the day it launched: $40M upfront, up to $670M in milestones. nitter.gq/smresearchlab/status/2…
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2. NeuralPLexer Iambic’s structure model: ~15x faster than AlphaFold 3 by Iambic’s numbers, with similar accuracy, and peer-reviewed at NeurIPS. Isomorphic, the Alphabet spinout that co-developed AlphaFold 3, is reportedly raising at ~$40B with no drug in human trials. $IAM is ~1/60th of that. nitter.gq/smresearchlab/status/2…
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1. The thesis What Iambic does, the pipeline, the partner deals, and why it trades at a fraction of its AI drug discovery peers. nitter.gq/rk8215/status/21158926…
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$IAM: three deep dives on Iambic Therapeutics in one place. An NVIDIA-backed AI drug discovery company with two models, a drug in the clinic, and AbbVie, Takeda and Bayer as partners. After cash, the market values all of it at ~$230M. The Iambicpedia so far 👇
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