Nature · 2008
Kemp Elimination Catalysts By Computational Enzyme Design
D. Rothlisberger et al.; D. Baker laboratory — showed that enzymes could be designed from first principles to catalyze reactions not found in nature.
Read publicationThe science of designing proteins that nature never imagined
We combine Nobel Prize-winning computational methods with modern AI and a proprietary 15-year protein design dataset — so every program starts from physics, data, and manufacturing reality, not guesswork.
Engineering at a different scale
Traditional screening vs. computational design
Random mutagenesis was the best tool available for decades. Today, Arzeda designs enzymes with atomic precision — then proves them under conditions that match your application.
Traditional enzyme engineering
- Random mutagenesis and high-throughput guessing
- Slow iteration cycles and expensive wet-lab screening
- Limited outcomes tied to what nature already sampled
Arzeda's computational design
- Precise, physics-informed designs targeted to your reaction
- Faster exploration of sequence space with fewer dead-end experiments
- Scalable workflows from digital models to commercial manufacturing
- Access to an effectively unlimited design space beyond natural diversity
Design
AI + physics-based protein design
The amino acid search space for a single protein is larger than the number of drops of water in the Pacific Ocean. We navigate it with proprietary algorithms that blend machine learning with Rosetta-class physics — the same scientific lineage that produced the 2024 Nobel Prize in Chemistry and co-founder David Baker's decades of breakthroughs.
Every design is grounded in energetics, structural feasibility, and learnings from our own multi-year dataset — not generic models trained only on public sequences.
Test
Precision lab testing
We validate candidates in application-relevant conditions — pH, temperature, cofactors, and matrices that match what you will see at scale.
That means fewer false positives from assays that look impressive on a spreadsheet but fail in the plant or on the shelf. We optimize for real-world performance, not high-throughput guessing.
Manufacture
Commercial manufacturing
Most computational platforms stop at a sequence. Arzeda delivers protein at the scale you need — from milligrams for early development to metric tons for commercial launch.
Integrated production capability is how we close the loop between design and deployment, and it is a gap many pure-software competitors cannot bridge.
15+ years of proprietary protein design data
Our models and playbooks are trained on outcomes from real programs — successes, failures, and the subtle constraints that only appear when you design, test, and manufacture in one continuous system.
15+Years of structured design and assay data
50+Protein programs delivered end-to-end
Full-stackDesign, validation, and scale-up under one roof
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.
Science · 2008
De Novo Computational Design of Retro-Aldol Enzymes
L. Jiang et al.; University of Washington & collaborators — end-to-end computational design of a new enzyme fold and active site, validating the design-build-test loop.
Read publicationScience · 2010
Computational Design of an Enzyme Catalyst for a Stereoselective Bimolecular Diels-Alder Reaction
J. B. Siegel et al. — extended computational enzyme design to complex multi-substrate chemistry and industrial-grade selectivity.
Read publicationScienceDirect · 2014
De Novo Computational Enzyme Design
Review synthesising a decade of methods and results into a clear roadmap for computational enzyme discovery.
Read publicationOpen science
Co-founding OpenFold
Arzeda helped launch OpenFold, a consortium committed to open-source 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.