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Research dossier · version 1 · updated Oct 30, 2026

AI Protein Design & Structure Prediction: State of the Field (October 2026)

Covers single-chain and complex structure prediction, deep-learning sequence design and de novo binder design as of late October 2026. The main takeaway: structure prediction is now a routine input to design, but experimental hit rates, affinity claims and confidence scores still vary widely by method, target and assay.

3,082 words41 sources29 evidence-mapped claims8 benchmarks14 timeline events✓ citation-verified Oct 30, 2026

AI-assisted synthesis of the cited literature, independently fact-checked against its sources; not peer reviewed. Research summary, not medical advice.

Contents

  1. Executive summary
  2. Background and core concepts
  3. Timeline of key milestones
  4. State of the art
  5. Single-chain and complex structure prediction
  6. Conformational states and disorder
  7. Sequence design and protein language models
  8. Backbone generation and binder design
  9. Quantitative benchmarks
  10. Open problems and active debates
  11. Major programs and players
  12. Outlook: what to watch (near term)
  13. Key papers
  14. Glossary

Sample findings

  • AlphaFold 2 reached a median backbone accuracy of about 0.96 Å (RMSD95) on CASP14 targets, substantially ahead of the other CASP14 entries.
  • The AlphaFold Protein Structure Database exceeded 214 million predicted structures in its 2023 update, which was published in the 2024 database volume.
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