BigHat Biosciences

Phase A — Understand the business

Lens 1 · Company Overview

BigHat Biosciences is a San Mateo, CA antibody-engineering company founded in 2019 by Mark DePristo (then-CEO) and Peyton Greenside (now CEO) . The product is **Milliner™** — a "full-stack" antibody discovery and engineering platform that fuses **machine learning** with a **synthetic-biology-based, automated high-speed wet lab** to run rapid **closed-loop design-build-test cycles** . The thesis is explicitly not "AI designs the molecule in a vacuum" — it is "AI proposes, the in-house automated lab measures, the data trains the next round," optimizing antibodies on multiple axes at once (affinity, developability, manufacturability, safety/immunogenicity).

How it makes money — two engines:

1. Pharma collaborations (the cash engine today). BigHat runs partner-funded discovery programs: upfront + R&D/commercial milestones + tiered royalties. Disclosed partners: AbbVie (Dec 2023), Eli Lilly (Apr 2025 + Jan 2026 TuneLab), Merck/MSD (Nov 2022), Amgen (research collaboration), plus an undisclosed J&J Innovation–linked collaboration ``.
2. Proprietary pipeline (the value engine, just emerging). BigHat is now advancing its own wholly-owned assets toward the clinic — a next-gen ADC for GI cancers (clinic 2026) and an avidity-driven T-cell engager (TCE) for solid tumors (IND filing 2026) ``.

The only disclosed deal economics are the AbbVie terms: $30M upfront, up to ~$325M in aggregate R&D milestones, plus commercial milestones and tiered royalties ``. All other deal values are undisclosed.

Lens 2 · Supply Chain (manufacturing / CDMO map — clinical overlay)

For a clinical-stage biotech the "supply chain" is the build-and-make stack: who supplies the science inputs, who manufactures the drug, who runs the trials.

Upstream (design inputs) → BigHat → drug → patient:

• Compute / ML stack → BigHat's internal models (proprietary; pedigree traces to DePristo's Google DeepVariant/GATK lineage — see Lens 9) ``.
• Automated wet lab → BigHat's own synthetic-biology lab is the differentiator and is in-house, not outsourced — this is the deliberate vertical-integration choice ``.
• ADC payload/conjugation chemistry → Synaffix (a Lonza company): in Nov 2024 BigHat licensed Synaffix's site-specific ADC platform (GlycoConnect™, HydraSpace®, toxSYN®) for its lead GI-cancer ADC; the Lonza acquisition of Synaffix (Jun 2023) also opens Lonza's end-to-end ADC CDMO to BigHat ``. This is a single-source chokepoint for the lead program's linker-payload chemistry and likely its manufacturing.
• Clinical / regulatory de-risking partner → Lilly Catalyze360™ provides funding, lab space, and drug-development resources for the GI ADC while BigHat retains full global rights ``.

Named-stakeholder verdict: the chain is real and named — Synaffix/Lonza (chemistry + manufacturing), Lilly Catalyze360 (development scaffolding), AbbVie/Lilly/Merck/Amgen (demand for the platform). The single most important dependency is Synaffix/Lonza for the lead asset.

Lens 3 · Competitive Advantages (moats)

The moat is the lab-in-the-loop, not the model. Greenside's own framing (Lens 12) is that anyone can generate a sequence; the durable asset is the proprietary experimental data flywheel — a high-throughput automated wet lab generating consistent, multi-parameter measurements that train models competitors can't replicate from public data ``. Specific moat sources:

• Proprietary closed-loop data — the Jan 2026 Lilly TuneLab framing is telling: BigHat is positioned to generate "diverse, high-quality datasets suitable for a generalizable antibody-developability foundation model," i.e. its data is the scarce input even Lilly wants ``.
• Switching costs / validation — five blue-chip pharma partners (AbbVie, Lilly, Merck, Amgen, +J&J-linked) constitute third-party validation that is itself a moat against newer entrants and a barrier a startup can't fake ``.
• Developability focus — BigHat targets the unsexy but decisive properties (manufacturability, stability, immunogenicity) where wet-lab feedback beats pure compute — a defensible niche vs. de-novo-hit-rate showmen.

Bargaining power: weak-to-balanced and structurally deteriorating. BigHat needs big pharma's capital and clinical machinery more than they need any single AI-antibody shop (there are ~6 credible competitors). The Lilly relationship — equity investment + two-program discovery deal + TuneLab data deal + Catalyze360 development support — is deep validation but also concentration risk (see Lens 13). A moat that depends on staying the preferred partner of one giant is a moat with a landlord.

Lens 4 · Segments (revenue mix)

n/a — private, not disclosed. BigHat does not report segments. Directionally , ~all near-term revenue is **collaboration/milestone revenue** from pharma partnerships (AbbVie's $30M upfront is the only hard datapoint); proprietary-pipeline revenue is **$0** (pre-clinical). A third-party aggregator lists **"2023 revenue $30M"** — this is unaudited and almost certainly the AbbVie upfront recognized, not recurring product revenue; treat as a one-time collaboration inflow, not a run-rate. No geographic split disclosed.

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Phase B — Pipeline & catalysts (clinical overlay — swaps the operating Phase B)

Lens 5 · Pipeline by phase (replaces "Earnings Result")

The asset table is the company. As of mid-2026 ``:

PoS / readout reality: these are pre-clinical to phase-entry assets — there is no human efficacy data yet and therefore no meaningful probability-of-success beyond generic IND-to-approval base rates (~7–10% for oncology, lower for first-in-class) ``. The lead ADC is BigHat's first-ever clinical candidate — 2026 is the year it stops being a platform-only story. The entire near-term thesis rides on the GI-cancer ADC actually entering the clinic in 2026 on schedule and the TCE IND landing.

Lens 6 · Founder/management signal (replaces "Earnings Calls" — no earnings calls exist)

No earnings calls. The substitute signal is founder communication + strategic posture, and it is unusually legible:

• CEO transition (Mar 2025): DePristo stepped down as CEO; Greenside (co-founder, ex-CSO/President) took over; DePristo moved to Advisor ``. A founder→founder handoff (not an outside-CEO parachute) — a positive governance signal that the science co-founder now runs the company as it pivots from platform-for-hire to drug-owner.
• Tone shift over time: 2022 messaging was platform capability ("partner anywhere along the discovery continuum" — DePristo). 2025–26 messaging is drug ownership ("advancing our own ADC into the clinic, retaining global rights" — Greenside) and anti-hype realism (STAT, May 2026). The narrative has matured from "we have a tool" to "we make drugs." ``
• What they stopped saying: the early "10x the lab capacity / aggressively hire" growth-at-all-costs language (2022 Series B) is gone, replaced by selective, capital-efficient partnering — consistent with the 2022–25 biotech funding winter ``.

Lens 7 · Catalyst calendar + funding-stage comps (clinical+private overlay — replaces P/E comps)

Multiples are meaningless for a private pre-revenue biotech. The two relevant comps are (a) catalysts and (b) capitalization vs. the AI-antibody peer set.

Catalyst calendar (``):

• 2026: Lead GI-cancer ADC enters the clinic (first-in-human) — the re-rating event.
• 2026: Avidity TCE IND filing.
• Ongoing: AbbVie / Lilly / Merck / Amgen program advancement → undisclosed milestone payments.
• Jan 2026: Lilly TuneLab data partnership (already announced).

Capitalization vs. AI-antibody peer set — BigHat is badly out-gunned ``:

The peer table's single loudest fact: BigHat has raised roughly one-seventh of what Xaira and Generate command, and ~1/9th of Chai's valuation alone. Its edge has to be capital efficiency and real lab data, because it cannot win a spending war.

Lens 8 · Catalysts that moved the company (private: funding/product events, not stock prints)

No stock to move. The events that re-rated BigHat's standing (``):

• Jul 2022 — $75M Series B (Section 32 lead; Amgen Ventures, BMS join) → $100M total; the credibility round.
• Nov 2022 — Merck collaboration (≤3 programs) → first big-pharma platform validation.
• Dec 2023 — AbbVie deal with disclosed economics ($30M up / ~$325M milestones) → the proof the platform commands real money.
• Nov 2024 — Synaffix/Lonza ADC license → BigHat acquires the chemistry to build its own drug.
• Mar 2025 — CEO transition to Greenside.
• Apr 2025 — Lilly discovery deal + Lilly equity investment (Series B-II, ~$45M tranche per aggregators) → the deepening Lilly relationship.
• Jan 2026 — Lilly TuneLab data partnership → BigHat's data validated as foundation-model-grade.

Pattern: the market (here = pharma counterparties + VCs) reacts to (1) disclosed deal economics and (2) Lilly's escalating commitment. The trajectory is up-and-to-the-right on validation, but every recent up-tick has Lilly's name on it — concentration the bear case will seize on.

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Phase C — Judge people & books

Lens 9 · Management

• Peyton Greenside, CEO & Co-founder. PhD Biomedical Informatics (Stanford); MPhil Cambridge (Herchel Smith scholar); BA Harvard; inaugural Schmidt Science Fellow; ex-computational biologist at the Broad Institute ``. Deep ML-×-biology pedigree; took CEO in Mar 2025. Skin in the game: co-founder equity (material, undisclosed %). The science founder running the company at the exact moment it becomes a drug company is the right archetype for this stage.
• Mark DePristo, Co-founder, now Advisor (ex-CEO). Led DeepVariant at Google Brain and was a pioneer of GATK (indel realignment, haplotype construction) — i.e. one of the people who made deep learning work in genomics ``. His move to Advisor (Mar 2025) concentrates execution under Greenside; his pedigree is the company's founding ML credibility.
• Track record: the founders built BigHat from 2019 to 5 blue-chip pharma partnerships + its own clinic-bound ADC in ~6 years on only ~$145M — that is capital-efficient execution, and it is the strongest pro in the file.
• Capital-allocation history (private proxy): disciplined — licensed ADC chemistry (Synaffix) rather than building it; used Lilly Catalyze360 for development scaffolding while keeping global rights; partner-funded the platform instead of burning equity. No value-destruction signals visible.
• Red flags: none disclosed. The structural watch-item is governance gravity toward Lilly (equity + multiple deals) — not a red flag yet, but the relationship to monitor.
• Founder vs. professional: founder-led, science-founder-CEO. Right for a platform-to-pipeline pivot.

Lens 10 · Forensic Red Flags + Regulatory

Accounting: n/a — private, unaudited, no financial statements disclosed. There is no income statement, balance sheet, or cash-flow statement to forensically examine — this absence is itself the disclosure-quality caveat for any investor. The one third-party "2023 revenue $30M" figure `` is unaudited and should be read as the AbbVie upfront, not run-rate revenue (Lens 4). Cash, burn, and runway are undisclosed — the single biggest information gap in the file (see Lens 11).

Regulatory findings (required sub-section):

• SEC (EDGAR LR + AAER): Per regulatory/regulatory-findings.md (Step 0): BigHat has no CIK — not an SEC filer; no EDGAR enforcement search possible. Zero SEC findings ``.
• Non-SEC (FTC/DOJ/FDA/CFPB): Web search "BigHat Biosciences" (FTC OR DOJ OR FDA OR lawsuit OR litigation OR settlement) returned no material hits attributable to BigHat ``.
• 10-K Item 3 (Legal Proceedings): n/a — no 10-K (private).
• Conclusion: No material regulatory or legal findings — verified via SEC EDGAR EFTS (no CIK), web search, as of 2026-06-24. Note: a private company has far less public disclosure surface, so "nothing found" carries less assurance than for a filer.

Science/exclusivity (clinical overlay sub-section):

• IP/exclusivity: lead ADC's differentiation rests partly on in-licensed Synaffix chemistry (target-specific license, not owned) — exclusivity on the linker-payload is leased, not owned, a structural IP dependency. The proprietary antibody and the ML/data are BigHat's own ``.
• Trial-design / going-concern: no clinical data yet; going-concern unknowable without financials.

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Phase D — Project & stress-test

Lens 11 · IPO-readiness & runway-to-catalyst (private+clinical overlay — replaces EPS projection)

No EPS to project (private, pre-revenue). forecast.ts create step skipped (watchlist breadth rule + no scoreable EPS/binary readout date disclosed). The two questions that matter:

(a) Runway to the next value-inflection catalyst. Cash and burn are undisclosed . What we know: ~$145M raised over 8 rounds through Apr 2025, with a Lilly equity tranche (~$45M, per aggregators) being the most recent . Headcount ~98–99 (PitchBook/Tracxn, early 2026) vs. 37 at the 2022 Series B — roughly tripled. A ~100-person antibody shop running an automated wet lab + IND-enabling work typically burns ~$40–70M/yr; against ~$145M raised plus undisclosed milestone inflows (AbbVie $30M up + ongoing), runway plausibly reaches the 2026 clinic-entry catalysts but likely requires a fresh raise (Series C) in 2026–27 to fund a Phase 1 — the financing watch-item. Partner milestones partially offset burn but are lumpy and undisclosed.

(b) IPO-readiness. private-watch.json absent — no stored readiness grade. `` BigHat is not IPO-imminent: no clinical data, a funding winter for early biotech, and a private market that is still funding it. S-1 unlocks would require (1) the lead ADC posting clean Phase 1 safety/early-efficacy, and (2) a friendlier biotech IPO window — earliest realistic 2027–28, and an M&A/acqui-partner outcome (Lilly the obvious acquirer) is at least as likely as an IPO. Action for the be-early ledger: BigHat should be added to research/private-watch.json (currently missing) with stage = "clinical-entry 2026", readiness = low-but-rising, catalyst = "GI ADC FIH + TCE IND".

Lens 12 · Bull vs Bear

Bull case. BigHat is the disciplined adult of the AI-antibody class. While Xaira ($1B) and Generate (>$1B) raise on de-novo-design spectacle, BigHat built a real automated wet lab that generates the proprietary, multi-parameter data that is the actual scarce asset — and it proved that data is valuable by (a) signing AbbVie/Lilly/Merck/Amgen and (b) having Lilly want its data for a foundation model (TuneLab). It did this on ~$145M — ~1/7th of peers — and is now converting platform credibility into owned drugs (GI ADC to clinic 2026, TCE IND 2026) while keeping global rights. If the lead ADC reads out clean, BigHat re-rates from "services platform" to "clinical-stage oncology company with an AI engine," and the Lilly relationship becomes an obvious M&A on-ramp.

Bear case (permanent-impairment risks).

1. Out-capitalized into irrelevance. In a field where compute and data scale win, being at ~1/7th the capital of Xaira/Generate and ~1/9th of Chai's valuation is an existential structural disadvantage; a better-funded rival's foundation model could leapfrog the lab-in-the-loop edge.
2. The drug just doesn't work. The whole "we make real drugs, not demos" narrative inverts if the first-ever clinical candidate fails in Phase 1 — and it's a first-in-class-ish ADC in tough GI tumors with leased linker chemistry. One bad readout and the platform's credibility takes the hit too.
3. Lilly capture. Equity + two-program deal + TuneLab + Catalyze360 = BigHat is drifting into being a Lilly satellite. Great until Lilly's priorities shift or it lowballs an acquisition from a position of strength.

Pre-mortem (18 months out, thesis broke): the GI ADC's clinic entry slipped past 2026 (IND-enabling tox or CMC delay on the Synaffix-derived ADC), the TCE IND also slipped, no Series C closed in a frozen biotech market, a foundation-model competitor (Lilly's own TuneLab, ironically, or Chai/Xaira) commoditized "developability prediction," and BigHat quietly became a fee-for-service CRO with a famous logo — or sold to Lilly at a down-round mark.

Are multiples too high? n/a — private, no public multiple. The relevant question is whether the last private mark (undisclosed) prices in clinical success that hasn't happened — unknowable without the cap table.

Contrarian view (what the market refuses to see): The crowd sorts AI-bio by raise size and de-novo hit-rate (Xaira/Chai win that contest). What it under-weights is that the binding constraint in antibody drugs is downstream wet-lab validation and developability, not sequence generation — exactly BigHat's lane — and that a company spending 1/7th as much while landing the same pharma logos is demonstrating capital efficiency the spenders are not. If the field's bubble deflates (the StatNews skeptic thread), the survivor may be the one that always insisted the lab — not the model — is the moat.

Lens 13 · Devil's Advocate (short-seller)

Dismantling the bull case:

• Revenue is concentrated in partnerships, and partnerships are cancelable. Pharma research collaborations get quietly shelved constantly; only AbbVie's economics are even disclosed. If two partners walk, the "validation" narrative and the cash both thin out — and you'd likely never see a press release about the cancellation.
• The moat may be weaker than bulls think. "Proprietary wet-lab data" is a moat only until a $1B-funded rival builds a bigger automated lab or a foundation model trained on enough public+synthetic data closes the developability gap. Lilly's own TuneLab is the tell — the big partner is building the generalizable developability model using BigHat's data. BigHat may be training its own eventual replacement.
• Most dangerous competitor bulls underestimate: not Xaira's science halo — it's Lilly itself (vertical-integrating AI biologics via TuneLab/Catalyze360) and Chai (de-novo at a $1.3B mark with a 20% hit rate that, if real, attacks the discovery step BigHat also sells).
• Capital-allocation / governance: taking Lilly equity + stacking four Lilly touchpoints is the kind of cozy-with-one-giant setup that caps optionality and invites a cheap takeout. The leased Synaffix chemistry means the lead asset's differentiation isn't even fully owned.
• What must hold for today's (private) mark: that the GI ADC enters the clinic on time and the platform keeps signing/renewing pharma deals and a Series C clears in a hostile market. Miss the 2026 clinic-entry by a year and the story is "another AI-bio platform that never shipped a drug."
• Permanent-impairment scenario: lead ADC Phase 1 safety failure (it's the first clinical asset and the credibility test) → simultaneously kills the drug and dents the "our AI makes better molecules" claim. Plausibility: moderate — first-in-human oncology is brutal and base rates are low.

Lens 14 · Management Questions (ordered by information value)

1. What is your current cash runway (in months), and does it reach both the GI-ADC first-in-human readout and the TCE IND — without a new raise?
2. Is the GI-cancer ADC still on track to dose its first patient in 2026, and what is the gating risk (tox, CMC, Synaffix supply) most likely to slip it?
3. With ~$145M raised vs. peers' $1B+, what is the capital-efficiency argument that makes lab-in-the-loop win over scale — quantitatively (cost per validated lead vs. a pure-compute shop)?
4. The Lilly TuneLab deal has you generating data for Lilly's generalizable developability foundation model — how do you ensure you're not training your own replacement?
5. How concentrated is your revenue/relationship in Lilly (equity %, share of partnered programs), and what's your plan to avoid becoming a single-pharma satellite?
6. Of the AbbVie / Lilly / Merck / Amgen programs, how many remain active and how many have hit a disclosed or undisclosed milestone?
7. The lead ADC's linker-payload is in-licensed from Synaffix — what's owned vs. leased, and what are the royalty/cost terms that flow through to your economics?
8. What is your proprietary-pipeline vs. partnership revenue/value split target for 2028, and is the strategic direction "drug company" or "platform-for-hire"?
9. What PoS do you assign your lead ADC, and against which GI indication/benchmark?
10. What's the Series C plan — size, timing, and would you accept a strategic (Lilly) lead, accepting the takeout-signal that sends?
11. What does the avidity TCE do that competing solid-tumor TCEs (which have struggled) don't — is the differentiation in the avidity engineering specifically?
12. How do you defend the data moat if a competitor builds an equally automated wet lab at 5× your capital?
13. What's your manufacturing path for the ADC at clinical then commercial scale — fully Lonza, or dual-sourced?
14. Which single program would, if it failed, do the most damage to the company's standing — and why is that risk acceptable?
15. Founder-CEO transition is done — what does the next two years of org build-out (clinical/regulatory/CMC leadership) look like as you become a drug developer?

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