{"id":"W2296201942","doi":"10.1021/acschembio.5b00612","title":"Activity-Independent Discovery of Secondary Metabolites Using Chemical Elicitation and Cheminformatic Inference","year":2015,"lang":"en","type":"article","venue":"ACS Chemical Biology","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; Hospital for Sick Children; Canada Research Chairs; McMaster University; University of Toronto","funders":"National Institute of General Medical Sciences; Canadian Institutes of Health Research","keywords":"Biology; Computational biology; Secondary metabolism; Gene; Streptomyces; Eukaryote; Drug discovery; Yeast; Secondary metabolite; Saccharomyces cerevisiae; Chemical genetics; Small molecule; Biochemistry; Genome; Bacteria; Genetics; Biosynthesis","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":[0.0001689197,0.0001301967,0.0003784144,0.00005117565,0.0000126243,0.00001017748,0.00007161676,0.0001980048,0.00001469956],"category_scores_gemma":[0.0009624427,0.00009170218,0.0000450061,0.0001076013,0.0002127937,0.0001750848,0.0001186691,0.0002153736,0.000002926604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003865789,"about_ca_system_score_gemma":0.0001000416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004073932,"about_ca_topic_score_gemma":3.466147e-7,"domain_scores_codex":[0.9992588,0.00001617677,0.0002529888,0.000203162,0.00009027379,0.0001786561],"domain_scores_gemma":[0.9993692,0.0001422147,0.0001229336,0.0001530273,0.0001132516,0.00009935121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001734877,0.00005376567,0.000583926,0.000103356,0.00004060274,7.552007e-7,0.000106949,3.962343e-8,0.9856297,0.0001491072,0.00005466126,0.01310366],"study_design_scores_gemma":[0.0005963589,0.00005576469,0.0002568846,0.00003656101,0.00007749702,0.00004835461,0.00005865945,0.00005605088,0.9979915,0.0004584808,0.0002592461,0.0001046507],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998522,0.0004864553,0.00003372811,0.0003958013,0.00005138865,0.0001353681,0.00002037759,0.00001533569,0.0003395269],"genre_scores_gemma":[0.9982726,0.00004315265,0.001308498,0.0001928196,0.00009948742,8.126961e-7,0.00005008985,0.000006485652,0.0000260392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01299901,"threshold_uncertainty_score":0.3739507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03155750217065807,"score_gpt":0.2951803048609812,"score_spread":0.2636228026903231,"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."}}