Statements (62)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:artificial_intelligence
|
| gptkbp:citation |
Jumper et al., Nature, 2021
|
| gptkbp:database |
gptkb:AlphaFold_Protein_Structure_Database
|
| gptkbp:databaseCoverage |
over 200 million proteins
|
| gptkbp:databaseLaunchYear |
2021
|
| gptkbp:databasePartner |
gptkb:EMBL-EBI
|
| gptkbp:developedBy |
gptkb:DeepMind
|
| gptkbp:field |
computational biology
bioinformatics |
| https://www.w3.org/2000/01/rdf-schema#label |
AlphaFold 2
|
| gptkbp:impact |
accelerated protein research
|
| gptkbp:input |
amino acid sequence
|
| gptkbp:language |
gptkb:Python
|
| gptkbp:license |
gptkb:Apache_License_2.0
|
| gptkbp:notableAchievement |
CASP14 winner
|
| gptkbp:notableFeature |
high accuracy
attention mechanism fast inference confidence metric (pLDDT) end-to-end differentiable model evolutionary information multiple sequence alignment end-to-end training MSA representation distogram prediction domain knowledge integration end-to-end differentiable loss pair representation public database of predicted structures recycling architecture structure module structure prediction head template-based modeling transformer network |
| gptkbp:notableUser |
academic institutions
medical researchers bioinformaticians genomics researchers biotechnology companies biochemists structural biologists AI researchers global research community biophysicists molecular biologists computational chemists drug discovery companies protein engineers |
| gptkbp:openSource |
2021
yes |
| gptkbp:output |
3D protein structure
|
| gptkbp:predecessor |
gptkb:AlphaFold
|
| gptkbp:purpose |
protein structure prediction
|
| gptkbp:relatedTo |
gptkb:Protein_Data_Bank
gptkb:RoseTTAFold |
| gptkbp:releaseYear |
2020
|
| gptkbp:repository |
https://github.com/deepmind/alphafold
|
| gptkbp:supportsAlgorithm |
deep learning
|
| gptkbp:usedBy |
gptkb:learned_society
pharmaceutical industry |
| gptkbp:bfsParent |
gptkb:Protein_Modeling
|
| gptkbp:bfsLayer |
5
|