{"id":"W3125794209","doi":"10.1016/j.drudis.2021.01.008","title":"Integration of AI and traditional medicine in drug discovery","year":2021,"lang":"en","type":"review","venue":"Drug Discovery Today","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":74,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada Research Chairs; Toronto General Hospital; University of Toronto","funders":"Diabetes Canada","keywords":"Drug discovery; Drug; Pharmaceutical sciences; Traditional medicine; Medicine; Pharmacology; Data science; Computer science; Biology; Bioinformatics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000630979,0.0003608747,0.001057869,0.000226103,0.00004030344,0.00007499626,0.000319513,0.0002991068,0.0000367105],"category_scores_gemma":[0.0004142139,0.0002579968,0.0002711897,0.0002488806,0.0004964846,0.00003267339,0.0002397702,0.0004283068,0.000003760268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004519258,"about_ca_system_score_gemma":0.0006117155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001176677,"about_ca_topic_score_gemma":0.000245804,"domain_scores_codex":[0.9976094,0.0002129756,0.0009013803,0.000464099,0.0005018449,0.00031032],"domain_scores_gemma":[0.9990059,0.0001216978,0.0002438598,0.0004125634,0.0000900734,0.0001259031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006122013,0.000475361,0.0000732617,0.01538821,0.0003746835,0.00003317411,0.0006067747,0.000003330474,0.002342113,0.003138386,0.05612242,0.9213811],"study_design_scores_gemma":[0.000676721,0.0002098395,0.000115199,0.01068526,0.000186445,0.00003651788,0.0004951327,0.00002350492,0.001639019,0.0004144425,0.9849944,0.0005235228],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001567476,0.9947985,0.0006986827,0.0003713037,0.0003700709,0.0004212595,0.0004578838,0.000003774766,0.00131098],"genre_scores_gemma":[0.004472506,0.9861794,0.0001022211,0.0001622697,0.0004455243,0.00004493287,0.005085995,0.00002874724,0.003478408],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.928872,"threshold_uncertainty_score":0.9999872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03355777438327915,"score_gpt":0.3248021662832254,"score_spread":0.2912443918999462,"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."}}