Statements (312)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:DNA
gptkb:Artificial_Intelligence gptkb:program gptkb:AI_technology |
| gptkbp:achieved_accuracy |
high accuracy in protein structure prediction
|
| gptkbp:aims_to |
protein structures
|
| gptkbp:analyzes |
amino acid sequences
|
| gptkbp:application |
predicted protein structures
|
| gptkbp:applies_to |
biological research
protein folding |
| gptkbp:available_on |
gptkb:open-source_software
|
| gptkbp:based_on |
gptkb:neural_networks
|
| gptkbp:can_provide |
understanding diseases
|
| gptkbp:can_transform_into |
biological sciences
research methodologies |
| gptkbp:collaborated_with |
academic institutions
pharmaceutical companies research institutions industry partners various research institutions international research projects |
| gptkbp:collaboration |
various universities
AI and biology AI and biology experts |
| gptkbp:competed_in |
gptkb:CASP14
|
| gptkbp:competes_with |
traditional methods of protein modeling
traditional experimental methods |
| gptkbp:contributes_to |
biological research
drug discovery understanding diseases |
| gptkbp:designed_for |
protein structure prediction
predicting protein structures |
| gptkbp:developed_by |
gptkb:Deep_Mind
protein structure prediction |
| gptkbp:enables |
drug discovery
understanding of diseases |
| gptkbp:exhibited_at |
demonstrations
|
| gptkbp:facilitates |
protein engineering
|
| gptkbp:fosters |
innovation in science
|
| gptkbp:has |
multiple versions
a user-friendly interface high throughput capabilities received numerous awards been adopted by pharmaceutical companies been featured in scientific journals been integrated into various research workflows been recognized by the scientific community been recognized for its impact on science been used in various scientific studies been utilized in vaccine development been validated experimentally contributed to the understanding of diseases enhanced the accuracy of protein modeling influenced the development of new algorithms inspired further research in protein folding inspired new research initiatives opened new avenues for research set new standards in protein structure prediction significant implications for medicine influenced educational programs in computational biology |
| gptkbp:has_a |
user-friendly interface
community of users |
| gptkbp:has_a_focus_on |
biomedical research
scientific studies |
| gptkbp:has_achieved |
Protein structures
Protein Structures protein folding protein structures protein interactions protein complexes protein-ligand interactions high accuracy in protein structure prediction 3 D structures of proteins high accuracy in CASP14 |
| gptkbp:has_applications_in |
gptkb:Medicine
molecular biology drug discovery genomics |
| gptkbp:has_been_validated_by |
experimental methods
experimental data |
| gptkbp:has_collaborated_with |
various universities
|
| gptkbp:has_community |
gptkb:researchers
|
| gptkbp:has_generated_data_for |
over 200 million protein sequences
|
| gptkbp:has_impact_on |
gptkb:biotechnology
life sciences biological research genomics |
| gptkbp:has_implications_for |
gptkb:biotechnology
|
| gptkbp:has_influenced |
structural biology
|
| gptkbp:has_version |
gptkb:Alpha_Fold_2
|
| https://www.w3.org/2000/01/rdf-schema#label |
Alpha Fold
|
| gptkbp:impact |
healthcare
clinical research |
| gptkbp:improves |
biological research
understanding of protein folding |
| gptkbp:input_output |
3 D protein structures
3 D protein models |
| gptkbp:is |
gptkb:open-source_software
a collaborative project a subject of public interest a tool for academic research a significant resource for researchers used in academia and industry a leader in protein structure prediction technology a significant achievement in the field of bioinformatics a benchmark for future AI models a breakthrough in computational biology a key advancement in AI applications a key player in the field of genomics a key resource for drug design a major milestone in structural biology a model for future AI developments a model for future innovations in AI and biology a model for interdisciplinary collaboration a part of the future of personalized medicine a result of years of research in AI and biology a significant advancement in AI-driven research a successor to Alpha Fold 1 a tool for structural biologists a tool for understanding protein interactions a transformative technology in life sciences part of the Alpha Fold Protein Structure Database widely adopted in the scientific community |
| gptkbp:is_a |
gptkb:machine_learning
|
| gptkbp:is_a_game_changer_for |
biomedical research
|
| gptkbp:is_a_major_advancement_in |
computational methods
protein modeling |
| gptkbp:is_a_platform_for |
collaborative research
|
| gptkbp:is_a_resource_for |
research institutions
scientists worldwide |
| gptkbp:is_a_software_that |
enhances protein analysis
|
| gptkbp:is_a_subject_of |
pharmaceutical companies
public interest ongoing studies many conferences |
| gptkbp:is_a_tool_for |
research and development
educational purposes protein interactions protein engineering protein design reduces time in research |
| gptkbp:is_accessible_by |
gptkb:Data
|
| gptkbp:is_adopted_by |
pharmaceutical companies
research labs |
| gptkbp:is_analyzed_in |
bioinformaticians
protein interactions |
| gptkbp:is_applicable_to |
various organisms
genomics |
| gptkbp:is_applied_in |
therapeutic development
|
| gptkbp:is_available_on |
gptkb:Google_Cloud
|
| gptkbp:is_based_on |
gptkb:neural_networks
deep learning techniques |
| gptkbp:is_celebrated_for |
its contributions to science
|
| gptkbp:is_challenged_by |
biological complexities
|
| gptkbp:is_cited_in |
research papers
numerous research papers numerous scientific papers |
| gptkbp:is_collaborative_with |
international research teams
|
| gptkbp:is_compared_to |
traditional methods
|
| gptkbp:is_considered |
experimental data
|
| gptkbp:is_considered_as |
breakthrough in biology
|
| gptkbp:is_described_as |
tutorials
|
| gptkbp:is_designed_to |
predict protein structures rapidly
|
| gptkbp:is_discussed_in |
conferences
scientific conferences media articles |
| gptkbp:is_documented_in |
gptkb:academic_journals
technical reports |
| gptkbp:is_encouraging |
cross-disciplinary research
|
| gptkbp:is_enhanced_by |
gptkb:biotechnology
collaborative research agricultural biotechnology understanding of diseases computational methods computational power |
| gptkbp:is_evaluated_by |
gptkb:CASP14
gptkb:scientific_community peer review process benchmarking studies scientific metrics CASP (Critical Assessment of protein Structure Prediction) scientific benchmarks blind tests CASP14 metrics |
| gptkbp:is_explored_in |
gptkb:educational_programs
therapeutic applications bioinformatics studies |
| gptkbp:is_facilitating_research_in |
neuroscience
|
| gptkbp:is_featured_in |
major scientific journals
|
| gptkbp:is_generating_interest_in |
AI ethics
|
| gptkbp:is_influenced_by |
gptkb:Alpha_Go
previous AI models previous CASP competitions |
| gptkbp:is_influential_in |
molecular biology
structural biology |
| gptkbp:is_informed_by |
biophysical principles
|
| gptkbp:is_integrated_with |
bioinformatics tools
biological workflows |
| gptkbp:is_open_source |
gptkb:true
|
| gptkbp:is_part_of |
gptkb:AI_technology
gptkb:scientific_community scientific advancements computational biology AI research initiatives structural biology biological databases the open science movement AI for Science the future of medicine the AI revolution Deep Mind's research portfolio AI advancements in healthcare AI in biology AI-driven research initiatives Deep Mind's AI solutions. AI for Science initiative the future of AI in science the AI revolution in science |
| gptkbp:is_promoted_by |
scientific organizations
|
| gptkbp:is_providing_insights_into |
evolutionary biology
|
| gptkbp:is_recognized_as |
a breakthrough in AI
a significant advancement in AI technology |
| gptkbp:is_recognized_by |
gptkb:scientific_community
Nobel laureates Nobel Prize winners scientific awards |
| gptkbp:is_recognized_for |
its accuracy
accuracy and speed advancing protein folding research advancing computational biology |
| gptkbp:is_related_to |
gptkb:Alpha_Fold_2
|
| gptkbp:is_studied_for |
enzyme functions
|
| gptkbp:is_supported_by |
gptkb:Google_Cloud
gptkb:Google_Research gptkb:European_Molecular_Biology_Laboratory large datasets government grants government funding international collaborations AI ethics discussions academic collaborations various funding agencies collaborative grants |
| gptkbp:is_tested_for |
real-world scenarios
experimental data various protein sequences |
| gptkbp:is_trained_in |
protein sequences
protein sequence data protein databases |
| gptkbp:is_used_by |
gptkb:scientists
research institutions scientists worldwide |
| gptkbp:is_used_for |
predicting protein interactions
|
| gptkbp:is_used_in |
gptkb:academic_research
COVID-19 research academic institutions drug discovery biomedical research |
| gptkbp:is_utilized_by |
pharmaceutical companies
researchers worldwide biochemists |
| gptkbp:is_utilized_for |
genomic research
vaccine development |
| gptkbp:is_utilized_in |
academic institutions
clinical trials |
| gptkbp:key |
understanding protein functions
|
| gptkbp:key_player |
protein research
biological discoveries |
| gptkbp:leadership |
protein structure prediction
|
| gptkbp:model |
future AI applications
learns from data learns from data. |
| gptkbp:notable_achievement |
gptkb:Artificial_Intelligence
|
| gptkbp:notable_contributor |
environmental science
|
| gptkbp:notable_release |
in December 2020
|
| gptkbp:notable_technology |
computational biology
structural biology |
| gptkbp:originated_in |
personalized medicine
|
| gptkbp:performance |
experimental data
|
| gptkbp:popularized_by |
computational biology
|
| gptkbp:project |
aims to solve protein folding problem.
|
| gptkbp:promotes |
data sharing
open science |
| gptkbp:provides |
new research opportunities
predictions of protein structures protein structure predictions high accuracy predictions 3 D models of proteins |
| gptkbp:provides_support_for |
genetic engineering
|
| gptkbp:published_by |
gptkb:academic_journals
|
| gptkbp:published_in |
gptkb:Nature
|
| gptkbp:released_in |
gptkb:2020
|
| gptkbp:research |
biochemists
|
| gptkbp:result |
gptkb:Nature
|
| gptkbp:successor |
gptkb:Alpha_Fold_2
|
| gptkbp:user_interface |
gptkb:true
|
| gptkbp:uses |
gptkb:Artificial_Intelligence
deep learning deep learning techniques |
| gptkbp:utilizes |
gptkb:machine_learning
multiple sequence alignments attention mechanisms |
| gptkbp:was_a_demonstration_of |
state-of-the-art performance
generalization to diverse protein families |
| gptkbp:was_a_result_of |
years of research
|
| gptkbp:won |
gptkb:CASP14_competition
first place in CASP14 |
| gptkbp:bfsParent |
gptkb:Deep_Mind
gptkb:Alpha_Go gptkb:Deep_Mind_Technologies gptkb:Google_Deep_Mind |
| gptkbp:bfsLayer |
5
|