Valence Discovery

Valence Discovery is an AI-first drug discovery company, now operating as Valence Labs, the AI research engine of Recursion. Formed from the merger of Datamol and InVivo AI, the team builds machine-learning methods and foundation models for molecular design, cellular perturbation prediction, and biological discovery, trained on petabyte-scale phenomics and transcriptomics data. Rather than a public REST API, Valence's developer surface is a suite of first-party open-source Python libraries - datamol, molfeat, graphium, SAFE and OpenQDC - published under the datamol-io and valence-labs GitHub organizations for cheminformatics, molecular featurization, graph neural networks and ML-ready quantum datasets.

Valence Discovery is profiled on the APIs.io network. Tagged areas include Company, Drug Discovery, Machine-Learning, Cheminformatics, and Molecular Design.

Valence Discovery’s developer surface includes documentation, engineering blog, and 6 more developer resources.

🌐 Visit website 📡 Source on GitHub

10.3/100 minimal ▬ flat Agent 0/100 human only Full breakdown ↓
scored 2026-08-20 · rubric v0.12.0

0 APIs

CompanyDrug DiscoveryMachine-LearningCheminformaticsMolecular DesignArtificial IntelligenceLife SciencesOpen-SourcePython

Kin Score

Kin Score How this is scored →
scored 2026-08-20 · rubric v0.12.0

Composite quality — 10.3/100 · minimal

Contract Quality 0.0 / 21

Developer Ergonomics 3.2 / 17

Access Clarity 1.8 / 17

Operational Transparency 0.3 / 11

Contract Governance 0.0 / 10

Discoverability 4.3 / 9

Regulatory Posture 1.9 / 15

Agent readiness — 0/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 0 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Regulatory Posture applies to this provider. Its tags matched the Health regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. This rating is computed from github.com/api-evangelist/valence-discovery: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

Documentation 1

Reference material describing how the API behaves

Documentation

Documentation

Build 3

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Packages

Packages

SDKs

SDKs

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Commercial 1

Pricing, plans, and the legal terms of use

PrivacyPolicy

PrivacyPolicy

Company 2

The organization behind the API

Website

Website

Blog

Blog

Source (apis.yml)

aid: valence-discovery
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/valence-discovery.png
name: Valence Discovery
description: Valence Discovery is an AI-first drug discovery company, now operating as Valence Labs, the AI research engine
  of Recursion. Formed from the merger of Datamol and InVivo AI, the team builds machine-learning methods and foundation models
  for molecular design, cellular perturbation prediction, and biological discovery, trained on petabyte-scale phenomics and
  transcriptomics data. Rather than a public REST API, Valence's developer surface is a suite of first-party open-source Python
  libraries - datamol, molfeat, graphium, SAFE and OpenQDC - published under the datamol-io and valence-labs GitHub organizations
  for cheminformatics, molecular featurization, graph neural networks and ML-ready quantum datasets.
url: https://raw.githubusercontent.com/api-evangelist/valence-discovery/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- techstars
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-21'
tags:
- Company
- Drug Discovery
- Machine-Learning
- Cheminformatics
- Molecular Design
- Artificial Intelligence
- Life Sciences
- Open-Source
- Python
tags_raw:
- Company
- Drug Discovery
- Machine Learning
- Cheminformatics
- Molecular Design
- Artificial Intelligence
- Life Sciences
- Open Source
- Python
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Website
  url: https://www.valencelabs.com/
- type: GitHubOrganization
  url: https://github.com/valence-labs
- type: Documentation
  url: https://docs.datamol.io
- type: Blog
  url: https://www.valencelabs.com/blog
- type: PrivacyPolicy
  url: https://www.valencelabs.com/privacy-policy
- type: Packages
  url: packages/valence-discovery-packages.yml
- type: SDKs
  url: packages/valence-discovery-packages.yml
- type: DomainSecurity
  url: security/valence-discovery-domain-security.yml
x-enrichment:
  date: '2026-07-21'
  status: enriched
  artifacts_added: 2
  pass: local-v1