The science of designing proteins that nature never imagined
We combine Nobel-prize winning science with modern AI and a high-quality, structured, and proprietary dataset you only get by practicing protein design for 15+ years.
Intelligent Protein Design Technology™
Design
Leverage our agentic AI-driven design-build-test-learn cycle until we generate a game-changing protein that works in application-relevant conditions.
Productize
Engineer a production host, scalable bioprocess, product formulation, achieve regulatory milestones and reach quality standards that will ensure commercial success for all parties.
Deliver
Engage our dedicated manufacturing team to handle the manufacturing of products through our trusted network of partners and ship innovative product to our customers.
Design
AI trained on 15 years of data + physics-based protein design
Most platforms either tweak nature (mutagenesis) or extrapolate from public sequences (LLMs). We start from physics - which is why our designs work, and why we can reach problems no one else can.
- 15 years of structured data. AI without domain-specific data is limited. Our AI learns from a proprietary library of designs we've built and tested ourselves — including the work public databases never show.
- Closed-loop DBTL. AI agents drive every cycle. Test and Learn insights feed back into Design without information loss. Agents learn from lab results, form hypotheses, and run iterations on our automation.
- 10 to 100× higher hit rate. Our AI agents learn from smaller datasets — no need to screen millions of variants. They iterate on application-relevant conditions, so products work in your application, not just a lab model.
Productize
From validated design to shippable product
This is where most design platforms fail. Molecules that win in a screen often die at scale-up or in formulation. Ours do not - because manufacturability, performance, and commercial ‘must-haves’ were design constraints from day one.
- Integrated, not handed off. Strain engineering, bioprocess, and formulation talk to protein design from the start. Trade-offs happen in silico, not after a failed scale-up.
- Bioprocess-aware from day one. Expression, stability, and yield are AI design objectives, not problems we solve later.
- Formulation in the loop. Proteins are designed to perform in the actual product matrix - not just in a buffer at pH 7.
- AI-orchestrated handoffs. Agents move information across strain, process, and formulation specialists, so insights flow forward instead of getting lost between teams.
Deliver
Commercial manufacturing and Delivery
Design-only companies let you inherit the rest of the work — sourcing CMOs, managing productization, managing quality and regulatory needs across vendors. Arzeda is a product company. CMOs are part of our network, but we manage it ourselves to deliver finished products to our customers.
- A proven manufacturing network. CMOs sit inside a network we contract, orchestrate, and stand behind — you don't have to source, qualify, or coordinate anything yourself.
- Designed for the factory. Expression in commercial hosts, stability, and scale-up were design constraints from day one, so commercial production is not a multi-year retrofit.
- Quality & regulatory in parallel. Compliance and quality systems run alongside development, not after — so launch is not gated by paperwork.
- End-to-end accountability. One team, one commitment to delivery — not just discovery.
How Arzeda's Intelligent Protein Design™ compares
The Advantage of Our Approach
Proprietary dataset
Our models and playbooks are trained on outcomes from real programs, with designs tested in application-relevant conditions. We learn from real successes, failures, and the subtle constraints that only appear when you design, test, and manufacture in one continuous system.
Peer-reviewed foundation
Published science
Arzeda's platform stands on a long arc of peer-reviewed enzyme design — from first demonstrations in top journals to the methods we use in production today.
Browse the full peer-reviewed catalogue here →
Open science
Co-founding OpenFold
Arzeda helped launch OpenFold, a consortium committed to open-source, open-data, open-weight AI tools for protein structure prediction and design. We believe the field moves faster when foundational models and benchmarks are shared — while our partner-specific IP remains protected inside each program.
OpenFold is one expression of that balance: public infrastructure, private advantage where it matters.
Learn more at openfold.io.