درباره این اپیزود
The Protein Data Bank holds about 10,000 antibody-antigen structures and grows by roughly 1,000 a year. David Younger's scaling work suggests that generalizable de novo antibody design needs hundreds of thousands to millions. At that pace, even 200,000 structures is nearly two centuries away.
In this episode, David Brühlmann hosts David Younger, founding CEO of A-Alpha Bio and the mind behind the AlphaSeq platform. Trained at the intersection of synthetic biology and computational protein engineering, David Younger cut his teeth at the Institute for Protein Design and sharpened his focus on the stubborn gap between theoretical promise and practical performance. His approach rewrites the rules of data generation—scaling up both the volume and the insight from protein interaction data to deliver the kind of information AI needs to actually make an impact in therapeutic design.
Topics discussed:
- Bottlenecks caused by insufficient and low-quality data in AI-enabled protein engineering (02:53)
- The limitations of traditional protein discovery methods and the persisting role of serendipity in antibody development (08:02)
- An overview of the AlphaSeq platform and how it enables large-scale measurement of protein interactions using engineered yeast (10:50)
- The differences between measuring protein-protein affinities with conventional techniques and AlphaSeq’s batch approach, including cost and speed advantages (16:54)
- Strategies for generating training data for machine learning models in protein engineering, including the need for structural and affinity data (18:23)
- The value and limitations of the Protein Data Bank (PDB) in training de novo design models (20:17)
- The challenge of benchmarking foundational models for protein engineering and the need for universal industry standards (23:11)
Smart insight: The future of biotherapeutic innovation is intertwined with a simple truth: more and better data unlock AI’s true potential. With breakthrough wet-lab technologies like AlphaSeq, and commitment to openness and benchmarking, the dream of efficient, precise, and truly rational protein engineering may soon become reality. The next big leap in biotech is not just smarter algorithms—but the scale, quality, and transparency of the data that powers them.
Curious how others are tackling the data problem in protein design? These episodes pick up right where David Younger leaves off.
- Episodes 185 - 186: How Generative AI Accelerates Protein Design for Biotech Discovery with Elise de Reus
- Episodes 115 - 116: Revolutionizing Biologics Development with Hyper Throughput Screening and AI with Jeremy Agresti
- Episodes 123 - 124: Manufacturability: Why Most Protein Candidates Fail (And How to Pick Winners Early) with Susan Sharfstein
- Episodes 219 - 220: From 10,000 Structures to 1.8 Billion Interactions: Breaking the Data Bottleneck to Engineer Efficacious Therapeutics with Troy Lionberger
Connect with David Younger
Company website: www.aalphabio.com
LinkedIn: www.linkedin.com/in/david-a-younger/
Data Atlas website: www.atlas.aalphabio.com
Free 5-day email course, The CMC Failure Chain: the five recurring CMC mistakes that put your promising program at risk → Get it here
