This series isn’t just about summarizing papers at the intersection of AI and biology. It’s about grappling with what these tools are doing to science—how they’re changing the questions we ask, the shortcuts we take, and the skills we need to build.

Some fields are further along in wrestling with this shift. In medicine, for example, there’s already serious debate about what it means when a newly minted MD carries an LLM that aces the medical boards in their pocket, but still needs to develop intuition, judgment, and real-world experience to treat patients safely. The stakes are obvious. But other fields—biology, diagnostics, bioinformatics—should be asking similarly hard questions.

For me personally, in the field of oncology diagnostics, even if I’m not the one delivering care, I’m developing tools that shape decisions. If we implement tools that don’t work (or work in all contexts), it can mean that someone’s cancer goes undetected, or a costly treatment is guided by a false signal. The stakes are real here too.

Each week, I’ll highlight a paper at the intersection of AI and Biology, but I’ll go beyond the results to ask:

  1. How do I ground myself in the practical realities of where this technology actually is, so that I can embrace it safely?
  2. How do I attend to my own scientific development and help shape the next generation of scientists learning to do science in a world where these tools are already loose in the lab?

The goal: Read more deeply, ask better questions, and stay clear-eyed about the promise, and the limits, of this accelerating field. 

And yes, I’ll be using AI tools on this journey. But I’ll also be keeping my brain fully plugged in. I’m not your meat puppet yet, AI. 😉

Let’s learn together.

  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