{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00241482,0.001491994,0.001176703,0.002437269,0.000430005,0.001249795,0.001322545,0.0008418819,0.001636619],"category_scores_gemma":[0.003909108,0.0005436033,0.001813254,0.001377436,0.000823609,0.001032643,0.0008158159,0.001333926,0.0004396644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001228296,"about_ca_system_score_gemma":0.001843765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001086422,"about_ca_topic_score_gemma":0.002343724,"domain_scores_codex":[0.9990043,0.0003673596,0.0000724414,0.0002125158,0.0002999939,0.00004342115],"domain_scores_gemma":[0.998021,0.001447962,0.0002123425,0.0001500002,0.0001174527,0.00005130555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001347448,0.001330438,0.008907797,0.003338302,0.001136902,0.0006876536,0.0001953858,0.4550815,0.2215226,0.04358794,0.003404787,0.2594592],"study_design_scores_gemma":[0.0001503308,0.0002443941,0.0008956896,0.00003984924,0.0002309168,0.0001324098,0.00003565797,0.907396,0.06832229,0.01746934,0.005038665,0.00004451159],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08390639,0.0009173427,0.8998585,0.0007152282,0.00005496911,0.000480762,0.001636899,0.006663409,0.005766518],"genre_scores_gemma":[0.3363317,0.00118295,0.6567891,0.0004569073,0.00004699897,0.0004573171,0.003937298,0.000210921,0.0005868328],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002437269,"threshold_uncertainty_score":0.01277095,"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."}}