Turbine

Simulated-cell platform running millions of in-silico experiments to guide oncology and immunology drug discovery.

Turbine, founded in 2016 and headquartered in Budapest, Hungary, develops a simulated-cell platform that runs large numbers of in-silico experiments to predict how cells respond to interventions, supporting target discovery and combination strategies in oncology and immunology. The company raised a $25M Series B and has disclosed partnerships including a first immunology collaboration with a top-10 pharma company. Atlas found no vendor-held security certifications documented on its site.

Headquarters
Budapest, Hungary
Founded
2016
Deployment options
cloud
Website
https://turbine.ai

Categories

AI Use Cases

  • Hit Identification & Lead Optimization
  • Target Identification & Validation

Vendor Scorecard

50% — Partial coverage. Coverage score — how completely Atlas documents Turbine across 7 buyer-evaluation areas. Reflects available Atlas data, not solution quality.

Compliance & Validation — Not documented by Atlas

Ask the vendor: Which compliance certifications (HIPAA, 21 CFR Part 11, SOC 2, GxP) do you hold?

Deployment & Hosting — Documented

cloud

Pricing Transparency — Not documented by Atlas

Ask the vendor: What is your pricing model, and what drives total cost of ownership?

Implementation & Onboarding — Not documented by Atlas

Ask the vendor: What is a realistic implementation timeline, and what onboarding support is included?

Organization & Role Fit — Documented

Biotech Startup, Mid-Size Biotech, Large Pharma, Drug Discovery, R&D Leadership

Solution Scope — Documented

Drug Repurposing, Target Identification

Company Viability — Partially documented

Series B, Founded 2016

Always ask the vendor

Not captured in Atlas data — confirm directly. Excluded from the score above.

Data Rights & IP

Who owns model outputs? Is our proprietary data used to train models others can access, and where does our data reside?

Model Credibility & Explainability

Can you share validation studies or benchmarks, explain how the model reaches its conclusions, and describe how bias and drift are monitored?

Questions to Ask This Vendor

  • How is the simulated-cell model validated against wet-lab results for our biology?
  • Which disease areas and cell contexts are best covered by the platform?
  • How is IP ownership structured in partnership programs?
  • What data-handling and security documentation is available for collaborators?