{"id":"W2009313526","doi":"10.1093/nar/gkm958","title":"DrugBank: a knowledgebase for drugs, drug actions and drug targets","year":2007,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":2917,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Genome Alberta; Genome Canada","keywords":"DrugBank; Drug; Drug discovery; Drug action; ADME; Pharmacogenomics; In silico; Pharmacology; PubChem; Computational biology; Bioinformatics; Computer science; Biology; Genetics","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":[0.002870892,0.001790133,0.003290383,0.01164638,0.001441063,0.004328643,0.004281755,0.002667255,0.05279372],"category_scores_gemma":[0.01074628,0.001447343,0.001616647,0.011335,0.0006575499,0.005367721,0.003566723,0.0032542,0.03238096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001837245,"about_ca_system_score_gemma":0.008282001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008944948,"about_ca_topic_score_gemma":0.009570674,"domain_scores_codex":[0.998212,0.0003787414,0.0004611311,0.0003637235,0.0004700961,0.0001143371],"domain_scores_gemma":[0.9954396,0.002076017,0.0006477506,0.0006408381,0.0007056128,0.0004901713],"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":[0.0004744485,0.0001589182,0.001169275,0.004440915,0.0001891105,0.0004690528,0.0001601721,0.003313224,0.002027635,0.01893277,0.8630617,0.1056028],"study_design_scores_gemma":[0.0001896818,0.00004301536,0.00109316,0.0004892598,0.0001384443,0.0003639676,0.00005558125,0.002265828,0.001234187,0.01145913,0.9826093,0.00005843649],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.001937794,0.008852394,0.05527876,0.002547339,0.0004781034,0.0008211061,0.8836403,0.02329839,0.02314573],"genre_scores_gemma":[0.005878598,0.007250476,0.0564297,0.001275548,0.0001495001,0.0007912589,0.9213078,0.001533816,0.00538335],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.05279372,"threshold_uncertainty_score":0.1766126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07127227794910762,"score_gpt":0.416100006730903,"score_spread":0.3448277287817954,"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."}}