AminoClaw
The AI Co-Scientist for Protein Design
Describe a protein target in plain language. AminoClaw runs the full design pipeline for you — so every biologist can move at the speed of their ideas.
Pre-Seed 2026
Raising £600K
London, UK
Confidential
wilbe.acetyl.online
One-liner|What does AminoClaw do?
AminoClaw is an AI co-scientist that puts
frontier protein design tools
directly in every biologist's hands — so researchers can go from idea to
validated candidate in hours,
not months of waiting on a specialist.
Product type
AI Agent Platform
Autonomous, multi-step, no code
First wave customer
Protein Biologists
Academic labs · Biotech · Pharma R&D
Value delivered
Hours, not months
Under $50 · Any biologist can run it
Secret sauce:
Four proprietary in-house models orchestrated by 211 autonomous skills — the only agent in biology with its own wet-lab validated training data loop.
Market|Three distinct markets — academic research, drug discovery, and synthetic biology
Academic Research
Mordor Intelligence 2024
Academic & research institutes segment · computational biology software spend
2024
$3.2B
→
2030
$9.6B+
~19% CAGR · fastest-growing segment
18,000+ protein labs in UK/EU alone. Researchers need AI tools they can actually use without a bioinformatics team.
Pharmaceutical & Drug Discovery
ResearchAndMarkets 2024
Global protein therapeutics sales — antibodies, biologics & protein drugs
2024
$423B
→
2030
$713B+
~9% CAGR
Monoclonal antibodies, cancer biologics, PROTAC degraders. Every program runs a protein design campaign — each one is a potential contract.
Synthetic Biology
Coherent Market Insights 2024
Industrial enzyme engineering, biomanufacturing, and green chemistry
2024
$19B
→
2030
$61B+
~22% CAGR
Plastic biodegradation, biofuels, food enzymes, CO₂ capture. Fastest-growing segment — driven by ESG mandates and energy transition.
Market size — 2024 baseline vs projected peak
Academic Research
$3.2B · 2024
$9.6B+ · 2030 · 3× growth
Pharma & Drug Discovery
$423B · 2024
$713B+ · 2030 · 1.7× growth
Synthetic Biology
$19B · 2024
$61B+ · 2030 · 3.2× growth
Problem|Three customer types — one shared bottleneck, very different consequences
Academic Labs
PhD students & PIs · 18,000 in UK/EU
Know the biology — can't run the AI tools
Problem
→
Frontier AI tools require coding skills they don't have
→
Wait weeks for a collaborator — projects stall or get dropped
→
Grant budgets can't cover a specialist hire
How AminoClaw solves it
✓
Natural language chat — describe the target, get results. No code, no compute setup.
✓
Hours instead of weeks — no collaborator wait, fits within grant budgets at £3K/yr
PRIMARY
Biotech Series A–C
Drug discovery startups · 2,500 in UK/EU
Burning runway — need a validated lead, fast
Problem
→
12–18 months to show investors a lead candidate — every week of delay costs runway
→
A bioinformatician hire costs £80–150K/yr and takes 3 months to onboard
→
CRO screening is slow and doesn't help with computational lead pre-selection
How AminoClaw solves it
✓
Starts day one — no hiring, no onboarding. Ranked lead candidates in hours.
✓
Months of iteration compressed to an afternoon — more runway, faster investor milestones
Pharma R&D
GSK · AstraZeneca · Pfizer · 500 programs
Outsourcing protein campaigns to CROs at £50K–500K a time
Problem
→
Dozens of protein programs per year — each months via CRO outsourcing
→
CRO lock-in: slow iteration, IP risk, no proprietary data accumulation
→
Internal AI tooling is fragmented — no single platform covers all protein types
How AminoClaw solves it
✓
Replaces £500K CRO pre-screen with an always-on AI pipeline at a fraction of the cost
✓
One platform, all protein types — antibodies, enzymes, binders, de novo design
Monetization|Count × price × penetration — near-term achievable ARR
Customer segment
Count (UK/EU)
Price / year
Revenue target
Academic Labs
Subscription · viral via publications
18,000
protein labs · UK/EU
£5K
avg/yr · within grant budgets
=
£1.8M
360 labs · 2% penetration
PRIMARY
Biotech Series A–C
Annual contract · replaces £80–150K hire
2,500
active companies
£150K
avg/yr · immediate ROI on runway
=
£7.5M
50 companies · 2% penetration
Pharma R&D
Enterprise · replaces £500K CRO pre-screen
50
major R&D divisions · UK/EU
£500K
avg/yr · enterprise SaaS
=
£2.5M
5 divisions · 10% · warm intros
Near-term ARR · UK/EU · conservative mix
Cradle achieved $10M+ ARR · Isomorphic Labs raised at $3B · category forming now
£11.8M
ARR · near-term achievable
Biotech Series A–C · primary segment · first paying client
↓
YOHOLIFE
Biotech · Paying
Their need
Accelerate enzyme variant screening for their PURELIX biomanufacturing pipeline. Their team had no in-house computational capacity — each design-test cycle was taking months.
What we will deliver
9,426 variants screened in a single automated run. Top variant aims to deliver +1.84× activity gain. A 3-month project done in one afternoon — currently in wet-lab validation.
Product|How a biologist uses AminoClaw — from a single message to a publication-ready report
Dr. Sarah Chen
Protein Biochemist
Series A Biotech
"Design me 5 EGFR binders — ranked by predicted affinity. I need them by tomorrow."
AminoClaw Agent
● Active session
Design me 5 EGFR binders — ranked by predicted affinity. I need them by tomorrow.
PIPELINE RUNNING — 211 skills available
✓ Literature: 218 EGFR papers · 3 crystal structures identified
✓ Structures fetched · 6 orthologs aligned automatically
✓ Hotspot analysis · 14 epitope residues scored
✓ 5 binder sequences designed & cross-validated · 3 refinement rounds
✓ Report generated · 3D viewer + ranked PDF ready
EGFR_Binder_Design_Report.pdf
Done in 3h 12m · <$35 compute
KD 4.2 nM
>100× selectivity
88aa mini-binder
Download PDF
3D Viewer
Send to Bench
211
autonomous skills
4
proprietary models
20+
AI models orchestrated
Cost per run
<$35
vs £10–50K enterprise rate
>99% gross margin
Technology|Five proprietary models — built in-house, wet-lab validated, impossible to replicate by renting APIs
Protein LM
Protein Language Model
Foundation model capturing structural and functional properties at sequence level. Powers the agent's deep protein understanding — physical insight, not just pattern matching.
Variant Model
Variant Effect Predictor
Predicts how mutations affect function, stability, and binding affinity. Powers hotspot detection and mutation scoring. Co-authored with Imperial College collaborators.
Structure Model
Structure Prediction Model
Complementary to public models — particularly strong on designed and non-natural sequences. Enables accurate in-silico validation before wet-lab testing.
Antibody Model
Antibody Design Model
Specialised for antibody CDR design, affinity maturation, and immunogenicity reduction. Full pipeline with wet-lab validated outputs across multiple programs.
Family Model
Protein Family Language Model
Family-aware protein representations trained on clustered sequence space. Enables evolutionary context-aware design and cross-family generalisation beyond single-sequence models.
Market|Competition axes — where AminoClaw sits vs every alternative
AminoClaw's moat: the only agent covering all protein types, end-to-end, with proprietary in-house models and wet-lab validated results.
Why our approach is fundamentally different
→
Built, not rented
5 in-house models accumulate proprietary training signal with every job run. Competitors wrapping ESM or AlphaFold via public APIs produce commodity outputs — they can never improve with use.
→
Wet-lab feedback loop
Every bench result re-trains the next model iteration. The accuracy gap widens with every validated result we deliver — a compounding advantage no API-based competitor can replicate.
→
All protein types, end-to-end
Antibodies, enzymes, binders, de novo design — one agent, one conversation. Latent-Y is antibody-only. Cradle requires expert scientists to configure. We cover the full space.
Competitive landscape
ChatGPT / Claude
General AI — no protein tools, no models, no pipeline.
Latent-Y
Closest competitor — but antibody-only.
Cradle.bio
Requires specialist scientists to configure — not accessible to general labs.
Isomorphic Labs
Enterprise partnerships only — inaccessible to biotech and academic labs.
Chai / ESM
Single-task infrastructure — need specialists to build on top of.
Financial|3-Year ARR projection — £600K → £4M → £12M as UK/EU penetration builds
Academic Labs
Biotech Series A–C
Pharma R&D
Gross profit (80% margin)
Gross margin 80%+
Net Revenue Retention 140%+
Path to breakeven ~Q3 2027
Roadmap|Two-year plan — product and customer growth, quarter by quarter
Q2 '26
Q3 '26
Q4 '26
Q1 '27
Q2 '27
Q3 '27
Q4 '27
Q1 '28
Product
🚀
Agent v1 launch
Enzyme screening + antibody CDR in one conversation
🔬
Wet-lab loop v1
Bench results auto-ingest → model re-training pipeline
⚗️
De novo design
Binders, scaffolds, novel enzymes from scratch
👥
Collaboration layer
Multi-user workspaces, PI + student roles
🔌
API + integrations
REST API, Benchling + LabArchives connectors
🧠
Model v3
Retrained on accumulated wet-lab validated data
🏢
Enterprise tier
SSO, audit logs, private deploy for pharma
🌎
US readiness
SOC2, HIPAA groundwork for North America entry
Customers
💰
3 paying clients
YOHOLIFE + 2 biotech. £150K ARR. First renewals.
🎓
Academic beta
10 labs via UCL network. First publication citations.
📈
£600K ARR
3 biotech + 30 labs. First pharma discovery call.
💊
Pharma pilot
1 pharma R&D pilot via FRS advisor intro. POC signed.
🎯
Series A
£4M ARR. Begin raise with 20+ biotech accounts.
🇪🇺
EU expansion
DE, NL, CH clusters. 50 academic labs, 5 biotech.
🤝
3 pharma deals
Enterprise contracts. £1.5M ARR from pharma alone.
🏆
£12M ARR path
360 labs · 50 biotech · 5 pharma in sight.
Big Picture Vision|Geographic expansion — own UK/EU first, then North America, then global
🇬🇧🇪🇺
Phase 1 · Now → 2028
UK & Europe
UCL network → 50+ institutions — MRC, Wellcome, Horizon-funded labs already running our pipelines
Oxford–Cambridge–London biotech cluster — first enterprise proving ground; 3,000+ life science companies in the UK alone
EU expansion: DE, NL, CH clusters — top 3 European biotech hubs by number of active pipeline companies
Target by 2028: 360 academic labs · 50 biotech · 5 pharma · £12M ARR
🇺🇸🇨🇦
Phase 2 · 2028 → 2031
North America
US is the world's largest protein therapeutics market — $280B+ in 2024, 40%+ of global biotech funding flows through Boston and Bay Area
FRS advisor network reaches MIT, Stanford, Broad — warm intros into the institutions that set the standard for protein AI globally
SOC-2 + HIPAA groundwork laid in Phase 1 — enterprise pharma procurement gate already cleared before we land
Target by 2031: 1,000+ lab subscriptions · big pharma contracts · £50M+ ARR
🌏🌍🌎
Phase 3 · 2031 → beyond
Global
China is a structural advantage. Our founding team has deep connections across Chinese academia and biotech — PKU, Tsinghua, CAS, and major pharma R&D groups. China's biotech sector grew 25%+ YoY and is the world's second largest by pipeline count.
APAC hubs: SG, KR, AU — Singapore's $1.5B National Research Foundation, South Korea's $2B+ biotech push, Australia's proximity to Asian supply chains
Largest validated protein design corpus — by Phase 3, every wet-lab result from every customer feeds a dataset no competitor can replicate
Target: 10,000+ active research groups · platform becomes infrastructure for global biology
The sequence matters. We win UK/EU first — deep relationships, existing customers, a proven product — then use that as the wedge into North America and beyond.
The Ask|Raising £600K Pre-Seed at £8M pre-money — 18 months to £4M ARR and Series A
Use of Funds — £600K
Wet-lab Validation & Showcase
40% · £240K
Close the dry→wet feedback loop; produce validated case studies for each segment
User Growth & Go-to-Market
25% · £150K
Sales, BD, pharma/biotech design partner acquisition — 10 enterprise accounts
Platform Engineering & Infrastructure
25% · £150K
Agent platform, 300+ skills, GPU infrastructure, SOC-2 certification
Operations & Legal
10% · £60K
£8M
pre-money valuation
7%
equity offered
18 mo
runway to Series A
Your return — three scenarios
£600K → 7% equity at £8M pre-money · post-Series A dilution ~20% · comps: Cradle $400M+, Isomorphic Labs $3B
Scenario
Exit ARR
Multiple
Valuation
Your return
Conservative
£5M
10×
£50M
~£2.8M
4.7×
Base case
£12M
15×
£180M
~£10M
17×
Upside
£25M
22×
£550M
~£31M
51×
Series A checklist — what this round delivers
£4M ARR · 30 labs, 20 biotech, 1 pharma
NRR > 140% · seat + module expansion
Wet-lab loop closed · 3 bench-validated case studies
SOC-2 certified · pharma procurement gate open
Why now
⚡
Foundation models just matured. AlphaFold, ESM3, and RFdiffusion have collapsed the compute barrier. The value has shifted to the agent layer on top — and that layer doesn't exist yet.
💊
Pharma AI budgets are live. R&D teams are being told to show AI ROI in 2026. They need a tool they can run today, not a platform build. We are that tool.
🏁
First-mover window is months, not years. Paying customer, bench-validated results, proprietary models. No competitor has all three. That gap is closable — but not quickly.
Team|Frontier biology and production AI engineering, in one team
Tiejun Wei
CEO & Co-founder
Computational Physics → AI Drug Discovery
- Peking University B.Sc. · UCL M.Sc. · QMUL Ph.D. in Computational Science
- UCL Research Fellow — quantum chemistry, biophysics, bioinformatics
- Developer of proprietary protein language and structure prediction models
- Extensive de novo protein design and molecular simulation experience
Weining Lin
CSO & Co-founder
AI for Protein Design
UCL B.Sc. · MRes Neuroscience · Ph.D. AI Computational Biology
- UCL AI Centre Research Fellow — AI for protein science
- Co-developer of proprietary models for variant effect prediction and antibody design
- Author of proprietary models for protein family modelling and function annotation
- Expert in antibody engineering, directed evolution & de novo design
- Worked inside a Series A biotech — from investor meetings to pricing decisions
Scientific Advisory Board
Prof. David T. Jones FRS
UCL · AlphaFold team · PSIPRED creator · 67K+ citations
Prof. Christine Orengo FRS
UCL · CATH database creator · 49K+ citations · EMBO Member
Dr. Jude Wells Ph.D.
Imperial College · DeepMind hackathon winner · protein model co-author
Why AminoClaw
1
An end-to-end protein AI agent with results that went to the wet lab.
211 production skills. Under $50 per campaign. Hours, not months. First paying customer signed while most competitors are still building demos.
2
Five proprietary models building a training dataset that grows with every result.
All five built in-house. Each wet-lab result feeds back into our models. Competitors using off-the-shelf APIs don't have this loop.
3
The agent layer in biology is winner-takes-most, and we're first in.
100,000+ labs. $400B+ market. Two FRS advisors who built the field's foundational tools. The category is forming now. We have the customer, the models, and the team.
AminoClaw
London, UK · wilbe.acetyl.online
Confidential · April 2026