This week’s paper is personal for me.

Dan Herschlag was my postdoc advisor. Dan taught me how to think mechanistically, how to test assumptions, and how to pursue scientific clarity with unflinching rigor.

That legacy is all over this paper. And is incredibly needed in this era of AI hype where data volume often sidelines careful model-driven science.

Herschlag et al. show that while AlphaFold3 predicts protein structures with backbone-level structural precision, it struggles to capture the physical rules that govern biological function.

But this paper isn’t a takedown: it’s a roadmap for how to go further.

What They Did

Rather than rely on RMSD alone, the team evaluated AlphaFold2 and AlphaFold3 against:

  • Energetic rules: bond torsions, hydrogen bonds, and van der Waals contacts
  • Experimental ensembles: from multi-temperature crystallography
  • Model confidence: comparing pLDDT to physical plausibility

What They Found

  • ~30% of side-chain interactions deviated from experimental observations, often with incorrect partners or implausible geometries
  • High-confidence predictions (>90 pLDDT) still showed strained or physically invalid conformations
  • Energetically-favorable conformations could increase RMSD and be penalized by the model
  • AlphaFold3 missed ~85% of conformational variability seen in real experimental ensembles

Key Insight: Physics ≠ Proximity

AlphaFold can recapitulate Ramachandran and Lennard-Jones-like patterns. But that doesn’t mean it understands physical constraints.
To improve these tools, we need evaluation metrics grounded in molecular energetics, not just geometry.

AI ∩ Bio

Illustration of scientific paper with the title "AI ∩ Bio: Reading the Revolution"

More in this series

  1. ChatNT: The future of biological assistants—or a mirage in a lab coat?
  2. GET: A Foundation Model for Transcription, Still Between Promise and Proof
  3. X-Atlas/Orion: Your Model is Only as Good as Your Training Data
  4. From Better Models to Better Questions: A Pathology AI Rethink
  5. The Model Isn’t the Magic: How Cytoland shows that domain expertise—not just deep learning—is what makes AI in biology work.
  6. Boltz-2: How much can 3D structure really tell us about molecular binding energetics?
  7. Investigating the volume and diversity of data needed for generalizable antibody–antigen ΔΔG prediction
  8. Beyond Binding: Rethinking Drug Design in the Age of AI and Structural Biology
  9. Testing the Physics Beneath the Predictions Beyond RMSD: What AlphaFold3 Really Understands
  10. Hype, Hurdles, and Hepatotoxicity: A Bold Step for AI-Designed Drugs, But Still Miles to Go
  11. When Complexity Misleads
  12. What Are Genomic Transformers Actually Learning?
  13. mRNABench and the Future of AI in Biology: Why Domain Knowledge Wins
  14. OncoGAN Creates Synthetic Cancer Genomes, Opening New Paths for Privacy-Preserving Precision Oncology
  15. Readable Rules, Testable Models: A New Grammar for Virtual Cells
  16. Multimodal CustOmics: Fusing Pathology Images and Tumor Genomics for Next-Gen Cancer Diagnostics
  17. Beyond Perturbation Simulations: PDGrapher Shows a Faster Way to Identify Actionable Targets
  18. Can AI design epigenetic anti-aging strategies?
  19. What happens when physicians use GPT-4 for diagnosis
  20. Can generative AI predict emergent phenomena?
  21. DeepSomatic and the question of how AI learns from itself
  22. Toward Mechanism-Centric Interpretability in Genomic Machine Learning
  23. From AlphaEvolve to DeepEvolve: What We’re Learning About Machine-Led Scientific Discovery
  24. Kosmos and the Culture of Discovery
  25. From Embeddings to Insight
  26. From Prediction to Explanation: How BIOREASON Reframes Genomic AI as a Reasoning Problem
  27. AI Models Need Better Truth—Platinum Pedigree Shows How
  28. From Evolutionary Intolerance to Clinical Insight: What popEVE Teaches Us About Missense Variants
  29. Interpretable Latent Spaces, Messy Biology: What AUTOENCODIX Teaches Us About Autoencoders in the Wild
  30. The Gene Ontology Knowledgebase in 2026

Takeaway for Scientists

This paper is a reminder that progress requires more than prettier predictions. It demands models that reflect the physics that drive biology.