Chai Discovery
Chai Discovery builds frontier AI to predict and reprogram the interactions between biochemical molecules. Its flagship Chai-1 is a multi-modal foundation model for biomolecular structure prediction across proteins, small molecules, DNA, RNA, glycans, and multi-component complexes, released as an open-source Python package (Apache 2.0) and as a hosted commercial server. Chai-2 extends the platform to de novo antibody design, generating novel antibodies directly from target epitopes without iterative wet-lab screening. Founded in 2023, Chai Discovery is a venture-backed digital-biology company whose developer surface centers on the chai_lab Python package, a command-line interface, and the hosted Chai Design platform.
More than an index entry, but the surface is still mostly links rather than artifacts — the cohort most likely to move a full band from modest, well-targeted work.
API Evangelist profiles Chai Discovery the way a machine reads it — 1 machine-readable artifact, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Chai Discovery scores 25.9/100 (emerging), with a separate agent-readiness read of 0/100 (human only). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Chai Discovery
Each block below is one kind of artifact we hold for Chai Discovery. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
Security Posture 1
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Resources
Every other property we hold for Chai Discovery — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 3
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Build 5
SDKs, sample code, and the tooling you integrate with
Access & Security 1
Authentication, authorization, and security posture
Operate 2
Status, limits, changes, and where to get help
Commercial 2
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
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