Endura Therapeutics uses AI to cut drug discovery costs by triaging 500 diseases
Endura Therapeutics, a biotech company developing CRISPR-based pills, leverages AI to accelerate drug discovery by triaging 500 disease targets. Using language models, the team generated reports on each target, filtering based on prevalence, existing treatments, and potential efficacy. The first pass narrowed the list to about 100 diseases, with the second pass producing detailed analyses…
Key points
- Endura used AI agents to analyze 500 diseases, generating reports to filter viable candidates in hours instead of years
- First pass screened targets based on prevalence, existing treatments, and potential efficacy, narrowing to ~100 diseases
- Second pass produced expert-level analyses, reducing human effort from decades to weeks while maintaining oversight
The company’s method contrasts with traditional biotech practices, where teams focus on a small set of diseases due to resource constraints. By automating initial triage, Endura avoids early-stage mistakes and allocates resources more efficiently. Adrian Sanborn, CEO and co-founder, emphasizes that AI’s impact in science extends beyond data generation—it enables dynamic, iterative experimentation and internal tool-building, democratizing expertise that was previously inaccessible at scale.
Foundries vs Navigators: Lowering the Cost of Science
Latent Space · 24 September 2026
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This text was published by Latent Space and written by Adrian Sanborn. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗
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