Blind Tests, Compliance Templates, and Design Over Prediction: Inside AION Labs
Notas do episódio
Four rival pharma companies build AI startups together. The science is rarely what decides which ones succeed.
You've built a model that works on public data. Now pharma wants a security review, a test you can't design yourself, and proof your tool fits a program they already run.
Yair Benita is CTO of AION Labs, a venture studio owned by Teva, Merck, Pfizer, AstraZeneca and AWS that builds AI drug discovery companies from scratch. His career runs from immuno-oncology at Merck in the early Keytruda days to immune checkpoint discovery at Compugen and patient data work at CytoReason.
Ross Katz and Yair break down the barriers between good technology and a pharma team willing to use it. You'll hear how blind validation won over skeptical chemists, why a design tool sells where a prediction stalls, and how a year of compliance work became a one-click AWS deployment.
Key Takeaways
- A startup's model ranked a pharma compound library it had never seen, against targets it didn't know. The chemists hoped for two binders in the top 100. The results kept them testing for ten targets.
- Telling a team its CRISPR guide is toxic after months and $1 million of work sends them back to the start. Yair's rule: if you can predict it, you can design it.
- Splitting antibody discovery and optimization into two companies made scientific sense. Commercially, pharma wanted the whole step.
- If pharma partners can't agree on a nonclinical test for success, don't start the company. That's why AION Labs never entered drug combination.
Chapter Markers
00:00 Introduction: Rival pharma companies building AI companies together
01:30 From pharmacology to computational biology
03:13 Merck and Compugen: discovering novel immune checkpoints from genome structure
07:00 Go it alone or reveal early? Lessons on proving a computational discovery
11:14 Cytoreason: deconvolving cell composition to solve pharma problems
15:18 Start with low-hanging fruit: indication selection vs. target discovery
17:30 Business models: why tech-for-pharma is hard, and why that is changing
19:58 What AION Labs is and how four pharma companies and AWS built it
23:10 Limitations of the model: shared challenges and the data-for-training problem
27:13 Blind validation: the small molecule ranking story
32:30 Baby steps, blinded competitions and building pharma trust
35:39 How seed-stage startups innovate on limited data and funding
41:54 Lesson: predictions are useless without a fix (Cassidy Bio)
44:00 Lesson: pharma wants the full process, not a piece (antibody discovery and optimization)
46:29 Defining the problem and the test: why no drug combination company was started
51:00 Eon’s evolving model: access to pharma, co-development and funding partners
54:40 The compliance bottleneck: ISO 27001 and one-click compliant AWS
58:07 Vision: an end-to-end ecosystem of specialized AI companies
01:00:27 Who should reach out to AION Labs
01:01:24 Four barriers of data, product, proof and access
Useful Links & Resources
- AION Labs: https://www.aionlabs.com
- CASP (Critical Assessment of Structure Prediction): https://predictioncenter.org
- ISO/IEC 27001 information security standard: https://www.iso.org/standard/27001
- AlphaFold Protein Structure Database: https://alphafold.ebi.ac.uk
- Yair Benita on LinkedIn: https://www.linkedin.com/in/ybenita/
Connect With the Show
- Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/
- CorrDyn on LinkedIn: https://www.linkedin.com/company/corrdyn/
- CorrDyn website: https://www.corrdyn.com
Which barrier has slowed your team most: data access, proof, product fit, or security review? Tell us in the comments. If you've run a blind validation with a pharma partner, we'd like to hear how you set it up.
Visit corrdyn.com to see how CorrDyn can help your organization extract value from data.
#DataInBiotech #DrugDiscovery #BiotechDataScience #MachineLearning #LifeSciences