|
gptkbp:instanceOf
|
gptkb:artificial_intelligence
|
|
gptkbp:AlphaFold2Announced
|
2020
|
|
gptkbp:AlphaFold2Performance
|
outperformed other methods at CASP14
|
|
gptkbp:awarded
|
CASP13 winner
CASP14 winner
|
|
gptkbp:citation
|
Nature 2021 paper
thousands (as of 2024)
|
|
gptkbp:database
|
gptkb:AlphaFold_Protein_Structure_Database
|
|
gptkbp:databaseLaunched
|
2021
|
|
gptkbp:databasePartners
|
gptkb:EMBL-EBI
|
|
gptkbp:developedBy
|
gptkb:DeepMind
|
|
gptkbp:field
|
computational biology
bioinformatics
|
|
gptkbp:firstReleased
|
2018
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
AlphaFold
|
|
gptkbp:impact
|
accelerated biological research
enabled new drug discovery
|
|
gptkbp:influencedBy
|
advances in neural networks
protein data bank
|
|
gptkbp:input
|
amino acid sequence
|
|
gptkbp:language
|
gptkb:Python
|
|
gptkbp:license
|
gptkb:Apache_License_2.0
|
|
gptkbp:limitation
|
does not predict ligand binding
does not predict protein dynamics
less accurate for disordered regions
less accurate for protein complexes
|
|
gptkbp:mediaCoverage
|
gptkb:BBC
gptkb:Nature
gptkb:The_New_York_Times
gptkb:Wired
gptkb:science
|
|
gptkbp:notableFeature
|
attention mechanism
confidence metric (pLDDT)
end-to-end differentiable model
evolutionary information
multiple sequence alignment
|
|
gptkbp:notableRelease
|
gptkb:AlphaFold2
|
|
gptkbp:notableUser
|
academic institutions
biomedical researchers
pharmaceutical companies
|
|
gptkbp:openSource
|
yes
July 2021
|
|
gptkbp:output
|
3D protein structure
|
|
gptkbp:purpose
|
protein structure prediction
|
|
gptkbp:relatedTo
|
gptkb:machine_learning
gptkb:Rosetta
gptkb:CASP_(Critical_Assessment_of_Structure_Prediction)
protein folding problem
|
|
gptkbp:repository
|
gptkb:GitHub
|
|
gptkbp:technology
|
deep learning
|
|
gptkbp:usedBy
|
gptkb:learned_society
|
|
gptkbp:website
|
https://deepmind.com/research/open-source/alphafold
|
|
gptkbp:bfsParent
|
gptkb:artificial_intelligence
|
|
gptkbp:bfsLayer
|
4
|