SIDE-BY-SIDE COMPARISON

CompareIsomorphic LabsvsRecursion

Review features, pricing signals, strengths, and trade-offs before choosing.

Generated from current catalog profiles Catalog profile signals Use-case comparison
AI Drug Discovery & Molecule Design Software

Recursion

SF 8.1

AI biotech with cellular imaging and automation

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Quick decision guide

Choose based on your workflow

Not enough differentiated product data yet to make a strong automatic pick.

Overview

How each tool is described

Isomorphic Labs

Isomorphic Labs is a DeepMind spinout developing AI models for drug discovery, including protein structure prediction and molecule generation for pharmaceutical and biologics partners.

  1. Partnership model: Engagement centers on collaborative R&D programs rather than self-serve software subscriptions, making it most relevant to organizations prepared for long-term work.
  2. Scientific focus: Its platform applies computational biology and frontier AI to protein and molecule design to accelerate selected discovery workflows.
  3. Evidence expectations: The platform does not guarantee clinical outcomes; buyers should examine data access, timelines to validated leads, and each collaboration's cost structure.
  4. Buying considerations: Pricing is sales-led and scoped individually, while teams should confirm workflow fit, implementation scope, and available support before committing.

For suitably resourced partners, the central question is whether its collaborative model matches the program's scientific, operational, and commercial requirements.



View full Isomorphic Labs profile

Recursion

Recursion is an AI drug discovery company for pharma collaborators and internal programs, combining cellular imaging, automation, and machine learning to identify potential drug candidates.

  1. Scientific approach: The platform centers on phenomics and multimodal data, supporting computational biology and R&D acceleration rather than guaranteeing clinical outcomes.
  2. Engagement model: Access typically occurs through pharma partnerships instead of self-serve subscriptions, making it relevant to organizations prepared for collaborative discovery.
  3. Evaluation priorities: Buyers should examine data-access requirements, timelines to validated leads, validation depth, support coverage, and program-specific costs before committing.
  4. Commercial model: Pricing is sales-led and scoped per buyer rather than publicly posted, while direct buyer access remains limited to partnerships.

Its fit depends on whether Recursion’s data-rich discovery model, partnership structure, and development timelines align with the buyer’s overall program goals.

View full Recursion profile

Side-by-side

Key differences

Criteria
AI Drug Discovery & Molecule Design SoftwareIsomorphic Labs
AI Drug Discovery & Molecule Design SoftwareRecursion
Best for
AI Drug Discovery & Molecule Design Software
AI Drug Discovery & Molecule Design Software
Score
8.4/10
8.1/10
Pricing
Contact sales
Contact sales
Category / audience
AI Healthcare & Medical Software › AI Drug Discovery & Molecule Design Software
AI Healthcare & Medical Software › AI Drug Discovery & Molecule Design Software

OVERLAP

Where Isomorphic Labs and Recursion are similar

Both tools cover similar catalog signals. The deciding factor is usually workflow fit, implementation needs, and ecosystem fit.

5 capabilities4 workflows

Shared capabilities

Capability overlap

  • Virtual ScreeningScreens compound libraries against targets
  • Literature IntelligenceMines biomedical literature for relevant evidence
  • Multi OmicsIntegrates genomic and other omics data sources
  • Lab IntegrationConnects discovery insights with wet-lab workflows
  • Partner WorkflowsWorkflows for pharma partner collaborations

Shared workflows

Workflow overlap

  • Run virtual screens against targetsRun virtual screening across large compound libraries.
  • Collaborate with pharma partnersCollaborate with pharma partners on discovery programs.
  • Identify drug targets from biology dataSurface potential drug targets from biological data signals.

Feature check

Side-by-side feature check

Feature
Isomorphic Labs
Recursion
Knowledge GraphConnects biological and chemical knowledge sources
-
Target DiscoveryIdentifies potential drug targets from biological data
-
Virtual ScreeningScreens compound libraries against targets
Literature IntelligenceMines biomedical literature for relevant evidence
Multi OmicsIntegrates genomic and other omics data sources
Lab IntegrationConnects discovery insights with wet-lab workflows
7 capabilities compared.2 differentiating rows are shown first.

Use cases

Who they're built for

Isomorphic Labs

  • Mine literature for research signalsMine biomedical literature for research and target signals.
  • Bridge AI design and wet lab cyclesBridge AI-generated designs with wet-lab testing cycles.
View full Isomorphic Labs profile

Recursion

  • Bridge AI design and wet-lab cyclesBridge AI-generated designs with wet-lab testing cycles.
  • Support protein engineering programsSupport protein design and engineering programs.
View full Recursion profile

The trade-offs

Pros & cons of each tool

Trade-offs

Isomorphic Labs

Pros
  • Frontier AI models for protein and molecule design
  • Strong pharma partnership track record
  • Linked to DeepMind's research depth
Cons
  • Not available as self-serve software today
  • Engagement relies heavily on partnership exclusivity
  • Public access to platform tools is limited
Trade-offs

Recursion

Pros
  • Phenomics platform with extensive multimodal data
  • Active pharma collaborations and internal pipeline
  • Public company with transparent disclosures
Cons
  • Direct buyer access remains limited to partnerships
  • Drug development cycles can take many years
  • Recursion's brand is evolving through Exscientia integration

Final verdict

Best fit depends on your workflow

Catalog verdict · low confidence

Current catalog data shows meaningful overlap between Isomorphic Labs and Recursion. Use the signals below to decide based on workflow, ecosystem, pricing, and implementation fit.

Shared catalog overlap

Isomorphic Labs and Recursion share 9 catalog signals, so the decision should focus on fit rather than broad capability alone.

Differentiators available

Isomorphic Labs has 3 visible decision signals and Recursion has 3.

Score signal

Isomorphic Labs has the higher SoftFinders Score in the current catalog data.

TRY THEM YOURSELF

See which one fits your workflow

Both tools have their strengths, the best way to decide is to spend a few minutes inside each.