AlphaFold 3 Tutorial: Predict Protein Structures Online Step-by-Step

By BioDockify Computational Research Team · September 18, 2026 · 11 min read

AlphaFold 3, released by Google DeepMind and Isomorphic Labs in May 2024, was the first AI model to predict complete biomolecular complexes — proteins together with ligands, DNA, RNA and ions — in a single run. On the PoseBusters benchmark it is roughly 50% more accurate than the best classical docking pipelines at predicting protein–ligand poses, and the work earned its creators the 2024 Nobel Prize in Chemistry.

Sections

• What AlphaFold 3 Actually Does (and What AlphaFold 2 Did Not)
• How the Model Works — Enough to Use It Correctly
• Step-by-Step: Predicting a Structure on the Free AlphaFold Server
• The Confidence Thresholds That Matter
• From AlphaFold Output to a Docking-Ready Receptor
• Limitations You Should Plan Around
• Quick Answers

Read the full interactive tutorial with tables and structure-preparation workflow on BioDockify.

References

1. Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024). DOI: 10.1038/s41586-024-07487-w
2. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). DOI: 10.1038/s41586-021-03819-2
3. EMBL-EBI Training. pLDDT: Understanding local confidence.
4. Isomorphic Labs. AlphaFold 3 announcement (PoseBusters 50% accuracy claim).
5. Wohlwend, J. et al. Boltz-2: binding affinity prediction. bioRxiv (2025).
6. The Nobel Prize in Chemistry 2024. NobelPrize.org.