{"id":"W4388338727","doi":"10.1016/j.copbio.2023.103007","title":"Advances in generative modeling methods and datasets to design novel enzymes for renewable chemicals and fuels","year":2023,"lang":"en","type":"review","venue":"Current Opinion in Biotechnology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Generative grammar; Renewable energy; Computer science; Biochemical engineering; Generative Design; Generative model; Bioenergy; Synthetic biology; Artificial intelligence; Engineering; Computational biology; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001581159,0.001703019,0.002377874,0.001039862,0.0002739316,0.001814782,0.002621154,0.001377887,0.004844467],"category_scores_gemma":[0.002948499,0.0007568478,0.002194042,0.001739281,0.0008565647,0.002227813,0.001439998,0.002951769,0.002421744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009548863,"about_ca_system_score_gemma":0.00140881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003552195,"about_ca_topic_score_gemma":0.004170601,"domain_scores_codex":[0.9995819,0.0001074606,0.00003045556,0.000115509,0.0001461323,0.0000185428],"domain_scores_gemma":[0.9987819,0.0008747831,0.00005513766,0.0001160357,0.0001336658,0.00003853005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001025809,0.0001216519,0.001231797,0.006375595,0.0007076662,0.0001520048,0.0000680687,0.1994509,0.004992979,0.1065989,0.02155156,0.6586463],"study_design_scores_gemma":[0.00008600089,0.0001244615,0.0008770744,0.002065013,0.000543757,0.000446942,0.00005247961,0.3937204,0.006847046,0.1743212,0.4207409,0.0001747341],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.003720174,0.3218989,0.6567873,0.002830753,0.001198516,0.00007914232,0.001851599,0.00216007,0.009473573],"genre_scores_gemma":[0.06117507,0.5807111,0.3400543,0.001806007,0.001713173,0.0004283567,0.006065265,0.001448234,0.006598422],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.004844467,"threshold_uncertainty_score":0.01620632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2006881765289842,"score_gpt":0.4684272631513308,"score_spread":0.2677390866223465,"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."}}