# Evozyne

**Canonical:** https://apis.io/providers/evozyne/  
**Website:** https://evozyne.com/  
**APIs profiled:** 0

Evozyne is an AI-native protein therapeutics company founded in 2020 by Paragon Biosciences and headquartered in Chicago, Illinois. It combines generative machine learning with directed evolution and high-throughput biology to design de novo proteins — which it brands Natural Machines — and advance them as engineered protein therapeutics for immune-mediated diseases. The platform pairs AI protein design with wet-lab build-and-test cycles, and the company has collaborated with NVIDIA on model work and with the Gates Foundation on sustainability applications. Evozyne is a drug-discovery organization rather than a software vendor: it publishes a corporate website, a pipeline page, scientific publications and news, but no public API, developer portal, SDK or machine-readable specification of any kind.

## Kin Score — 11.7 / 100 (minimal)

Scored 2026-08-17 under rubric 0.11.0. Trend: flat (+0.0 from 11.7).

| Facet | Score |
|---|---|
| Discoverability | 57.4 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Operational Transparency | 5.3 |
| Developer Ergonomics | 0.0 |
| Commercial Clarity | 21.1 |

Regulatory layer — **Health**: 17.5 (matched via tags).

## Agent readiness — 0.0 (human-only)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| MCP Server | no |
| Auth Clarity | no |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |

## Security (1)

- **Evozyne Domain Security** — TLSv1.3 · DMARC

## Tags

Company, Biotechnology, Life Sciences, Artificial Intelligence, Protein Design, Drug Discovery, Therapeutics, Immunology, Machine Learning

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/evozyne/). Scores are computed from the provider's own public artifacts under a published rubric.