AlphaFold
E39542
AlphaFold is an artificial intelligence system developed by DeepMind that predicts protein 3D structures from amino acid sequences with unprecedented accuracy, revolutionizing structural biology.
Aliases (1)
- AlphaFold2 ×6
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
artificial intelligence system
→
protein structure prediction system → protein structure prediction system → |
| achievement |
top performance in CASP13 protein structure prediction competition
→
top performance in CASP14 protein structure prediction competition → |
| announced |
2020
→
|
| announcedAt |
CASP13
→
|
| approach |
end-to-end differentiable learning
→
structure prediction from sequence alone → |
| basedOn |
attention mechanisms
→
evolutionary information → multiple sequence alignments → transformer architecture → |
| citation |
Jumper et al., Nature 2021
→
|
| dataSource |
UniProt protein sequences
→
|
| developer |
DeepMind
→
DeepMind → Google DeepMind → |
| evaluationMetric |
GDT_TS
→
|
| field |
bioinformatics
→
computational biology → structural biology → |
| hostedOn |
EMBL-EBI AlphaFold Protein Structure Database
→
|
| impact |
accelerated determination of protein structures
→
reduced need for experimental structure determination → |
| input |
amino acid sequence
→
|
| language |
Python
→
|
| license |
open-source license
→
|
| majorUpdate |
AlphaFold2
→
|
| notableFor |
high accuracy in protein structure prediction
→
near-experimental accuracy in protein structure prediction → revolutionizing structural biology → |
| organization |
Alphabet Inc.
→
|
| output |
protein 3D structure
→
|
| parentOrganization |
DeepMind
→
|
| published |
2021
→
|
| relatedTo |
CASP (Critical Assessment of protein Structure Prediction)
→
Protein Data Bank → |
| releaseDate |
2018
→
|
| repository |
GitHub
→
|
| sponsor |
Alphabet Inc.
→
|
| supports |
proteins from many organisms
→
|
| task |
protein structure prediction
→
|
| usedBy |
academic researchers
→
pharmaceutical companies → |
| usedFor |
drug discovery
→
functional annotation of proteins → protein engineering → |
| uses |
deep learning
→
neural networks → |