DeepMind and Biohub debate AlphaFold’s limits in protein folding
Google DeepMind’s Pushmeet Kohli and Biohub’s Sal Candido discussed why AlphaFold’s breakthrough in protein structure prediction did not fully solve biology’s challenges. They argued that scaling compute and data alone won’t advance the field, and that progress requires rethinking data quality, scientific intuition, and model design. AlphaFold’s success in predicting static protein structures…
Key points
- AlphaFold’s static structure prediction doesn’t solve protein dynamics, disorder, or functional design, panelists say
- Better data (e.g., cryo-EM micrographs) and interdisciplinary collaboration are needed for 10x breakthroughs in biology
- Trustworthiness and uncertainty calibration matter more than full interpretability for AI’s clinical use
The conversation highlighted trade-offs between handcrafted models (like AlphaFold’s inductive biases) and scalable architectures. Both experts agreed that interpretability and trustworthiness—rather than full human-level understanding—are critical for AI’s role in medicine. While AI is already used in drug discovery, they cautioned that meaningful acceleration requires deeper biological modeling, not just incremental improvements.
Why AlphaFold Didn't Solve Protein Folding — Pushmeet Kohli, Google DeepMind & Sal Candido, Biohub
Latent Space · 10 October 2026
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