Frekil vs Isomorphic Labs

Side-by-side comparison of features, pricing, and ratings

Analysis reviewed Live tool data as of 2026-10-09
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At a glance

DimensionFrekilIsomorphic Labs
PricingContact for enterprise pricingContact for partnership pricing
Target UserHEOR teams, biostatisticians, epidemiologists, market access teamsPharma companies, large-scale drug development programs
Core TechnologySelf-improving AI agents for real-world evidence generationAlphaFold-based predictive and generative AI for drug discovery
Key FeaturesNL querying, cohort building, causal DAG, SAP generation, survival analysisProtein-ligand interaction prediction, novel molecule design, digital biology simulation
Partnership ModelEnterprise sales with onboardingPharma collaborations only (Novartis, J&J)

Choose Frekil if your goal is to generate real-world evidence from clinical data quickly and transparently, especially for HEOR. Choose Isomorphic Labs if you're a pharma company seeking an AI partner for novel drug discovery. They serve entirely different stages of the drug lifecycle.

Frekil
Frekil

AI infrastructure that turns clinical data into publication-ready real-world evidence in minutes.

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Isomorphic Labs
Isomorphic Labs

Isomorphic Labs applies generative AI drug discovery to design novel molecules and predict how candidates will perform.

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Pricing
Contact Sales
Contact Sales
Plans
Contact sales
—
Popularity
2 views
7.5k views
Skill Level
Intermediate
Advanced
API Available
Platforms
Web
—
Categories
🧬 Drug Discovery & Life Sciences📊 Data & Analytics
🧬 Drug Discovery & Life Sciences
Features
Natural language querying of clinical data
Automated literature review across PubMed and proprietary studies
PECO question clarification with automatic ICD and NDC code resolution
Cohort builder with baseline, washout, index, and follow-up temporal rules
Automatic immortal time bias detection during cohort building
Causal DAG builder validated via simulated HCP reasoning
Statistical Analysis Plan (SAP) generator with primary, secondary, subgroup, and sensitivity analyses
Data extraction and schema mapping to OMOP CDM with missingness EDA
Sandboxed execution of R, Python, and SAS statistical code
Target trial emulation for comparative effectiveness
Survival analysis with Kaplan-Meier and Cox proportional hazards models
Propensity score matching and weighting
External control arms from historical real-world patient data
Sensitivity analysis with negative controls and empirical calibration
Report writer producing publication-ready tables, figures, listings, and an audit trail
Drug Design Engine spanning generative molecule design through lead optimization
Predictive AI models that anticipate how candidate drugs will perform
Generative models for designing novel molecules
Builds on and beyond the Nobel-winning AlphaFold system
End-to-end research collaborations from target identification to lead optimization
Digital biology approach aimed at discovery at digital speed
Named pharma partners include Johnson & Johnson, Novartis and Eli Lilly
Joined the Virtual Biology Initiative to build foundational data for AI disease models
Funder of Biohub's $1.8B initiative to train AI models that predict cell behavior
Bioresilience framework for biological threats and health emergencies
Series B investment round announced May 2026 to scale the Drug Design Engine
Follows a $600M external raise in March 2025
President Max Jaderberg appointed November 2025 to integrate AI and science
Interdisciplinary team of drug discovery experts and ML specialists
Founded by Demis Hassabis, who serves as CEO
Integrations
Databricks
Snowflake
AWS
GCP
Azure
PostgreSQL
CSV

Who should pick which

  • HEOR team at a pharma company
    Pick: Frekil

    Frekil automates RWE generation for comparative effectiveness, survival analysis, and market access dossiers.

  • Biostatistician in pharma
    Pick: Frekil

    Frekil provides target trial emulation, propensity score matching, and SAP generation with sandboxed execution.

  • Large pharma R&D team
    Pick: Isomorphic Labs

    Isomorphic Labs offers AI-driven drug discovery partnerships for novel molecule design and lead optimization.

  • Market access team
    Pick: Frekil

    Frekil generates payer value dossiers with auditable real-world evidence.

  • Research institution exploring digital biology
    Pick: Isomorphic Labs

    Isomorphic Labs provides cutting-edge predictive models built on AlphaFold for digital biology simulation.

Frequently Asked Questions

Frekil vs Isomorphic Labs: which should you choose?

Choose Frekil if your goal is to generate real-world evidence from clinical data quickly and transparently, especially for HEOR. Choose Isomorphic Labs if you're a pharma company seeking an AI partner for novel drug discovery. They serve entirely different stages of the drug lifecycle.

What does Frekil do?

Frekil uses self-improving AI agents to generate real-world evidence from clinical data, automating literature review, cohort building, causal analysis, and report writing.

What does Isomorphic Labs do?

Isomorphic Labs is an AI-first drug discovery company using AlphaFold and proprietary models to predict protein-ligand interactions and design novel molecules.

Can I use Isomorphic Labs as a standalone API?

No. Isomorphic Labs operates only through deep pharma collaborations; there is no self-serve API or software.

Does Frekil require access to clinical data?

Yes. Frekil works with structured clinical data from EHRs, claims, registries, or proprietary sources.

What recent funding did Isomorphic Labs receive?

A $600M external investment round in 2025 and a Series B in early 2026 (amount undisclosed).

What integrations does Frekil have?

Frekil integrates with clinical data sources (EHR, claims, registries) and supports OMOP CDM, but specific integrations are not listed.

Who are Isomorphic Labs' partners?

Novartis and Johnson & Johnson (news 2026-01-20).

Can Frekil be used for small academic projects?

It is not recommended for small datasets; Frekil is designed for enterprise-scale RWE generation.

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Last reviewed: July 3, 2026