Multimodality · 2021
Highly Accurate Protein Structure Prediction with AlphaFold
Showed that a deep-learning system built on attention could predict a protein 3D structure from its amino-acid sequence at near-experimental accuracy, solving a decades-old grand challenge in biology and demonstrating that attention-based models generalize far beyond language.
Editorial record
Plain-language summary
AlphaFold pairs an attention-based Evoformer that reasons jointly over multiple-sequence alignments and residue-pair representations with a structure module that directly predicts atomic coordinates, trained end-to-end with protein geometry built in. At the CASP14 assessment it predicted many structures to within experimental error, a step change over prior methods. DeepMind released the code and a public database of predicted structures, reshaping computational structural biology.
Knowledge graph
Relationships
Descendants
Depends onEvidence: Strongly supported
Attention Is All You Need
AlphaFold Evoformer and structure module are built on attention
AlphaFold Nature 2021
Conceptual ancestorEvidence: Strongly supported
Mastering the Game of Go (AlphaGo/AlphaZero)
Continues DeepMind line of deep learning for hard structured problems
book
Source record
Provenance
- Record ID
- P-540
- Record created
- 2026-07-13
- Last reviewed
- 2026-07-14
- Record version
- 2
Citation caveat: Citation metadata is approximate and marked unverified in the source dataset.