{"id":"W4388615774","doi":"10.1093/nar/gkad976","title":"DrugBank 6.0: the DrugBank Knowledgebase for 2024","year":2023,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":1530,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Genome Alberta; Canadian Institutes of Health Research; Alberta Innovates; Genome Canada","keywords":"DrugBank; Biology; Bioinformatics; Computational biology; Pharmacology; Drug","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":[0.006758254,0.001710982,0.002587134,0.008075746,0.0009505149,0.004451263,0.0037462,0.00244546,0.05137734],"category_scores_gemma":[0.01943853,0.00138366,0.001447884,0.01061128,0.0005737902,0.004663235,0.004076368,0.002737942,0.05819491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002215446,"about_ca_system_score_gemma":0.007115696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009091758,"about_ca_topic_score_gemma":0.008227016,"domain_scores_codex":[0.9966372,0.0008019289,0.0006313666,0.0005904007,0.001130677,0.000208361],"domain_scores_gemma":[0.9925491,0.002049289,0.001166003,0.0010139,0.002183629,0.001038026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003125095,0.00004647547,0.0008035979,0.001785501,0.0001130561,0.00007405838,0.00005343196,0.0007012439,0.0008871945,0.004175694,0.9585733,0.032474],"study_design_scores_gemma":[0.0001325975,0.00002521113,0.001138892,0.0003650006,0.00007556365,0.0000712504,0.00003455635,0.0007012967,0.0008200378,0.003306527,0.9932855,0.00004360042],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008526692,0.004037139,0.008173635,0.002737606,0.0003292451,0.0002219169,0.9608256,0.01413521,0.008687026],"genre_scores_gemma":[0.001859704,0.002165327,0.0106951,0.001256461,0.0000659189,0.0002611301,0.9807091,0.001135525,0.00185173],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05137734,"threshold_uncertainty_score":0.1718743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1251609761501077,"score_gpt":0.4387112823119583,"score_spread":0.3135503061618506,"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."}}