Statements (107)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:software
|
| gptkbp:bfsLayer |
3
|
| gptkbp:bfsParent |
gptkb:philosopher
|
| gptkbp:applies_to |
biological research
|
| gptkbp:based_on |
gptkb:Transformers_character
gptkb:Alpha_Fold |
| gptkbp:collaborated_with |
gptkb:European_Molecular_Biology_Laboratory
various universities biotechnology firms |
| gptkbp:collaborations |
academic institutions
|
| gptkbp:competes_with |
gptkb:Rosetta
traditional methods of protein modeling |
| gptkbp:contributed_to |
understanding of diseases
|
| gptkbp:developed_by |
gptkb:philosopher
gptkb:Library |
| gptkbp:field |
bioinformatics
|
| gptkbp:has_achievements |
protein complexes
high accuracy in CAS P14 high accuracy in protein structure prediction |
| gptkbp:has_impact_on |
drug discovery
biomedical research |
| gptkbp:has_programs |
gptkb:item
drug discovery genomics |
| https://www.w3.org/2000/01/rdf-schema#label |
Alpha Fold 2
|
| gptkbp:improves |
protein structure prediction
|
| gptkbp:input_output |
3 D protein structures
3 D protein structure amino acid sequence |
| gptkbp:is_a_solution_for |
gptkb:protein_folding_problem
|
| gptkbp:is_adopted_by |
research institutions
biotech companies |
| gptkbp:is_analyzed_in |
data scientists
|
| gptkbp:is_available_on |
gptkb:archive
|
| gptkbp:is_cited_in |
numerous research papers
scientific publications |
| gptkbp:is_compared_to |
traditional methods
|
| gptkbp:is_discussed_in |
research papers
media articles |
| gptkbp:is_evaluated_by |
peer review process
scientific competitions benchmarking datasets CAS P14 protein folding competitions |
| gptkbp:is_explored_in |
gptkb:Educational_Institution
|
| gptkbp:is_featured_in |
gptkb:documentaries
|
| gptkbp:is_influenced_by |
gptkb:Alpha_Fold_1
previous protein folding models |
| gptkbp:is_integrated_with |
bioinformatics tools
|
| gptkbp:is_open_source |
gptkb:theorem
|
| gptkbp:is_part_of |
computational biology
AI research initiatives AI for Science AI advancements in healthcare AI in biology Alpha Fold project AI-driven research |
| gptkbp:is_promoted_by |
scientific organizations
|
| gptkbp:is_recognized_by |
gptkb:Research_Institute
Nobel Prize winners |
| gptkbp:is_recognized_for |
solving protein folding problem
|
| gptkbp:is_supported_by |
gptkb:Google_Cloud
government grants pharmaceutical companies government funding supercomputers |
| gptkbp:is_used_by |
pharmaceutical companies
research institutions |
| gptkbp:is_used_for |
protein engineering
|
| gptkbp:is_used_in |
structural biology
|
| gptkbp:is_used_to |
predict protein interactions
|
| gptkbp:is_utilized_in |
molecular biology
biomarker discovery personalized medicine agricultural biotechnology genomic studies understanding diseases therapeutic development vaccine development metabolic engineering biophysics structural biology protein-protein interactions environmental biotechnology protein engineering protein-ligand interactions protein design enzyme engineering synthetic biology applications therapeutic target identification protein stability analysis protein dynamics studies |
| gptkbp:performance |
experimental methods
|
| gptkbp:presented_by |
scientific conferences
|
| gptkbp:published_by |
gptkb:Nature
|
| gptkbp:release_date |
gptkb:2020
|
| gptkbp:release_year |
gptkb:2020
|
| gptkbp:requires |
computational resources
|
| gptkbp:successor |
gptkb:Alpha_Fold
|
| gptkbp:supports |
COVID-19 research
|
| gptkbp:training |
protein sequences
large protein databases |
| gptkbp:uses |
gptkb:microprocessor
gptkb:Artificial_Intelligence deep learning |
| gptkbp:utilizes |
gptkb:microprocessor
|
| gptkbp:winner |
CAS P14 competition
|