{"id":"W2977898125","doi":"10.1101/792671","title":"Targeting the Pregnane X Receptor Using Microbial Metabolite Mimicry","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Pharmacogenetics and Drug Metabolism","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Albert Einstein College of Medicine, Yeshiva University; National Institutes of Health; Georgia Clinical and Translational Science Alliance; Crohn's and Colitis Foundation of America; U.S. Department of Defense","keywords":"Pregnane X receptor; Metabolite; Biology; Drug metabolism; G protein-coupled receptor; Secondary metabolite; Drug discovery; Chemical space; Regulator; Receptor; Pharmacology; Chemistry; Biochemistry; Cell biology; Nuclear receptor; Gene; Drug; Transcription factor","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":[0.0001364761,0.0002103419,0.0001766803,0.0000999023,0.0000773127,0.0002512368,0.0001729137,0.0002608581,0.001325333],"category_scores_gemma":[0.00008679123,0.0000758947,0.0001994984,0.00006805507,0.0002020017,0.0001648927,0.000223119,0.0003062224,0.0004120125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002106971,"about_ca_system_score_gemma":0.0001257436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002289422,"about_ca_topic_score_gemma":0.0002339085,"domain_scores_codex":[0.9999125,0.00001955743,0.000004842032,0.00001951216,0.00002624687,0.00001731894],"domain_scores_gemma":[0.9999703,0.00000435893,0.00001217313,0.000004553427,0.000002710205,0.000005917757],"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":[0.00006402277,0.00001067716,0.00008541837,0.00004407288,0.000003924103,0.00005477578,0.000005802413,0.0002872428,0.9974523,0.0004432641,0.00008196527,0.001466531],"study_design_scores_gemma":[0.00001084196,0.000156979,0.0002577372,0.000002351421,0.000004806149,0.0001534929,0.000004767328,0.0008198134,0.9956291,0.00009342031,0.002863232,0.000003579278],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9664148,0.004215166,0.02265629,0.0004759314,0.0001110785,0.00008019472,0.0005927447,0.0003401664,0.005113679],"genre_scores_gemma":[0.9882665,0.001130942,0.007450551,0.00008344201,0.00001496596,0.00002783378,0.0001527306,0.00001599531,0.002856991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001325333,"threshold_uncertainty_score":0.004433692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06159226196383241,"score_gpt":0.3362175906060095,"score_spread":0.274625328642177,"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."}}