Recursion vs Schrödinger
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.
Side by Side
| Axis | R Recursion |
S Schrödinger |
|---|---|---|
| AI Centrality | ||
| Autonomy and Oversight Model | ||
| Model and Technology Transparency | ||
| Clinical and Operational Evidence | ||
| AI Safety and PHI Stewardship | ||
| HIPAA and BAA Posture | ||
| Security Certifications and Trust Center | ||
| FDA and Regulatory Status | ||
| AI Governance and Bias Disclosure | ||
| EHR and Interoperability Depth | ||
| Deployment Model and Data Residency | ||
| Commercial Transparency | ||
| Setting and Specialty Coverage |
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.