{"id":"W3204768651","doi":"10.1016/j.sbi.2021.11.008","title":"Deep generative modeling for protein design","year":2021,"lang":"en","type":"preprint","venue":"Current Opinion in Structural Biology","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"Institute of Genetics; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Generative grammar; Discriminative model; Generative model; Computer science; Generative Design; Protein design; Artificial intelligence; Function (biology); Machine learning; Protein structure; Biology; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007119905,0.0008895335,0.001159739,0.000710014,0.0003896187,0.001462577,0.00128612,0.001976939,0.004812919],"category_scores_gemma":[0.001893324,0.0006963374,0.001236993,0.0008493303,0.001520073,0.001721795,0.001132073,0.002176063,0.001413463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001791904,"about_ca_system_score_gemma":0.0009375022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003167126,"about_ca_topic_score_gemma":0.003612402,"domain_scores_codex":[0.9997309,0.0001043423,0.0000116621,0.00005047142,0.00007869271,0.00002400285],"domain_scores_gemma":[0.9994186,0.0003778769,0.00005489339,0.0000563698,0.00005490407,0.00003733678],"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":[0.00001821653,0.00001827297,0.0003289024,0.000182343,0.00005404441,0.0000589437,0.00005381153,0.6384395,0.0009490387,0.3362772,0.003472527,0.02014719],"study_design_scores_gemma":[0.000006235319,0.00000782932,0.00006610927,0.00002481127,0.000007436435,0.00002473357,0.000006770958,0.7697843,0.0002090402,0.2240245,0.005829135,0.000009217542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007685308,0.008699687,0.9682906,0.002143119,0.0001737511,0.00002607132,0.0004698905,0.0005921477,0.01191946],"genre_scores_gemma":[0.5790641,0.02635346,0.3548732,0.001476881,0.0006821932,0.0005793278,0.002001581,0.0008259732,0.03414317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004812919,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05946952344950198,"score_gpt":0.3520063394774142,"score_spread":0.2925368160279122,"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."}}