{"id":"W3088578860","doi":"10.1016/j.cels.2020.08.016","title":"Fast and Flexible Protein Design Using Deep Graph Neural Networks","year":2020,"lang":"en","type":"article","venue":"Cell Systems","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":230,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Nvidia","keywords":"Computer science; In silico; Benchmark (surveying); Protein design; Graph; Artificial neural network; Constraint (computer-aided design); Algorithm; Protein structure prediction; Protein sequencing; Sequence (biology); Protein structure; Theoretical computer science; Artificial intelligence; Peptide sequence; Mathematics; Biology; Gene; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001393167,0.0001372504,0.0001356582,0.00001762458,0.00008501803,0.00007160568,0.0001243679,0.0001224948,0.000003619474],"category_scores_gemma":[0.00002628723,0.0001267744,0.00003620279,0.00008432988,0.000038783,0.000004286318,0.00008940027,0.0001193563,0.000004062078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005182838,"about_ca_system_score_gemma":0.00001451098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001938419,"about_ca_topic_score_gemma":5.872547e-7,"domain_scores_codex":[0.9992149,0.0001005424,0.0002054925,0.0001912091,0.00008404225,0.0002038248],"domain_scores_gemma":[0.9995679,0.000007116946,0.0001116893,0.000163871,0.00003079202,0.0001185943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006612406,0.00001474123,0.001858565,0.0003675339,0.00003112638,0.000005032948,0.0002606055,0.7602425,0.2353498,0.00003585192,0.0006797744,0.001088384],"study_design_scores_gemma":[0.0002761912,0.0001965653,0.00003159909,0.0000163312,0.00001155661,0.00001955638,0.0001275998,0.9848621,0.01236392,0.000001917814,0.001921222,0.000171409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2263312,0.002113657,0.7697571,0.00004328535,0.0001560942,0.0005738259,0.000002103484,0.0000411426,0.0009815906],"genre_scores_gemma":[0.9951259,0.00001383221,0.004164513,0.0001521223,0.000304163,0.00001387466,0.00002141784,0.00002372279,0.0001804832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7687947,"threshold_uncertainty_score":0.516971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01847110912025982,"score_gpt":0.2246390681826954,"score_spread":0.2061679590624356,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}