{"id":"W3211091287","doi":"10.1093/bib/bbab425","title":"Botanical drugs: a new strategy for structure-based target prediction","year":2021,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Plant-based Medicinal Research","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Wuhan University of Science and Technology; National Natural Science Foundation of China","keywords":"DrugBank; Computational biology; Ligand (biochemistry); Docking (animal); Drug discovery; Protein Data Bank (RCSB PDB); Chemistry; Protein ligand; Small molecule; Stereochemistry; Biology; Drug; Medicine; Biochemistry; Pharmacology; Receptor","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008314005,0.0002287355,0.000335532,0.0001536591,0.0001554287,0.00005501124,0.0002634531,0.0004738376,0.0007188382],"category_scores_gemma":[0.001125237,0.0002246112,0.0001005299,0.0003999629,0.0001417022,0.0002067315,0.00006158081,0.001002942,0.00003747459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001985138,"about_ca_system_score_gemma":0.001685789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005200791,"about_ca_topic_score_gemma":0.00005826482,"domain_scores_codex":[0.9978402,0.0001301775,0.0007064394,0.0002357788,0.0003926998,0.0006947112],"domain_scores_gemma":[0.9980012,0.001027003,0.0001601862,0.0002301469,0.0001862273,0.0003952761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005971688,0.001472841,0.01192201,0.003639742,0.0006244235,0.0005043593,0.006309433,0.05537009,0.04063907,0.01677283,0.5786541,0.2781194],"study_design_scores_gemma":[0.008370164,0.0004360549,0.001194402,0.0001507426,0.0001009181,0.00009652525,0.0005735127,0.5180315,0.06551488,0.003262589,0.4018744,0.0003944075],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7065256,0.003910316,0.1989145,0.03962751,0.006221483,0.007161519,0.007927001,0.001030139,0.02868185],"genre_scores_gemma":[0.870086,0.0002963707,0.08745058,0.03689246,0.0009498465,0.0001349734,0.002027286,0.00009079224,0.002071682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4626614,"threshold_uncertainty_score":0.915938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1189289309426957,"score_gpt":0.435040207385051,"score_spread":0.3161112764423553,"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."}}