Recursion vs Schrödinger (2026)
The two public companies that define the poles of computational drug discovery, and they disagree about what the computer should be doing. Schrodinger simulates the physics, licenses that software broadly, and has the strongest published and independently benchmarked method literature in this category, with a molecule originated on the platform reaching late stage development. Recursion learns from its own biology, running high throughput cellular experiments at scale and training models on the images and multiomic data they produce, then testing model hypotheses back in the wet laboratory. For a pharmaceutical research organisation the practical question is whether you want software your chemists run or a partner whose platform generates hypotheses you buy into. Both disclose more than the private companies in this category, for the same reason: a public listing forces disclosure that a partnership structure hides.
- The models are the company and the data comes from its own experiments. High throughput biological experimentation feeds machine learning on cellular imaging and multiomic data, so the platform generates the training data rather than licensing it.
- Partner money is the market's verdict and it is disclosed: pharmaceutical partners have paid substantial sums against this platform, and as a listed registrant the partnership economics are public rather than described.
- The oversight mechanism is structural: model hypotheses are tested against wet laboratory experimentation rather than advanced on the strength of a prediction.
- The methods are published and independently benchmarked across a large peer reviewed literature, which is the strongest technical transparency position in this category and unusual anywhere in this index.
- Evidence exists at both platform and asset level, including a molecule originated on this platform that reached late stage development, which is the outcome the whole category is judged on.
- It is genuinely licensed software with the broadest coverage here, spanning small molecule discovery and beyond, so a pharmaceutical company can run it in house rather than entering a partnership.
This comparison is published by AI Health Index, an independent research platform that compares healthcare AI vendors objectively. Recursion and Schrödinger are each graded against the same capability taxonomy, from each vendor's own public materials and the regulatory record, under the AI Health Index verification standard. No vendor pays for placement, and no vendor has reviewed this page. How this evidence is graded
Plain facts
| Fact | Recursion | Schrödinger |
|---|---|---|
| Primary category | Drug Discovery AI | Drug Discovery AI |
| Headquarters | Salt Lake City, UT | New York, NY |
| Website | recursion.com | schrodinger.com |
Side by Side
Each record in one paragraph
Written to be quoted whole. Each paragraph states what the AI Health Index verified about the vendor, with the caveats attached. Generated from this pair’s live capability grades, so it moves when a grade moves.
The AI Health Index awards Recursion its top capability grade on AI Centrality and Commercial Transparency. Set against Schrödinger, Recursion grades higher on AI Centrality and Model Supply Chain Disclosure. Its thinnest published disclosure sits on AI Liability and Recourse. Grades reflect evidence the AI Health Index could verify at the last review, so a low grade records disclosure the vendor has not published rather than a capability it has been shown to lack.
Source: AI Health Index, August 2026
The AI Health Index awards Schrödinger its top capability grade on several axes, including Model and Technology Transparency, Clinical and Operational Evidence and AI Governance and Bias Disclosure. Set against Recursion, Schrödinger grades higher on several axes, including Model and Technology Transparency, Clinical and Operational Evidence and AI Safety and PHI Stewardship. Grades reflect evidence the AI Health Index could verify at the last review, so a low grade records disclosure the vendor has not published rather than a capability it has been shown to lack.
Source: AI Health Index, August 2026
Questions buyers ask
Should we choose Recursion or Schrödinger?
On the axes where the AI Health Index separates them, Recursion grades higher on AI Centrality and Model Supply Chain Disclosure, and Schrödinger grades higher on several axes, including Model and Technology Transparency, Clinical and Operational Evidence and AI Safety and PHI Stewardship. Schrödinger leads on the greater share of scored axes, but the split means the decision turns on which constraint is binding rather than on an overall winner.
Where do Recursion and Schrödinger differ most?
The widest separation the AI Health Index records between Recursion and Schrödinger is on AI Centrality, where Recursion grades A and Schrödinger grades C. That axis sits in the AI Capability group, so it should carry the most weight for a buyer whose binding constraint is how much of the work the model itself is trusted to do.
Where do Recursion and Schrödinger grade the same?
The AI Health Index grades Recursion and Schrödinger the same on several axes, including Autonomy and Oversight Model, Security Certifications and Trust Center and FDA and Regulatory Status. Neither holds an advantage the index can evidence on those axes, so they should not carry weight in a selection between these two.
What have Recursion and Schrödinger not disclosed?
At the last review, at least one of Recursion and Schrödinger published thin or absent detail on AI Liability and Recourse. The AI Health Index treats an absent disclosure as a gap in the public record rather than a failure of the product, so these are the axes to get in writing during diligence instead of inferring from the grade.
Related comparisons
Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Drug Discovery AI page.
These companies are graded on different mechanisms and a direct capability comparison is misleading. Schrodinger's platform is physics based molecular simulation, which is why it holds the lowest AI centrality grade in this category, a descriptive fact rather than a criticism; Recursion's is machine learning over its own experimental data.
Both are Nasdaq registrants, which is why their commercial disclosure is unusually complete: public listing forces disclosure that private peers decline. Neither publishes a SOC 2, ISO 27001 or equivalent attestation. Recursion narrowed its internal pipeline in 2025, so pipeline breadth claims predating that should be checked against the current programme list.