{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001770318,0.001268928,0.002088967,0.003024069,0.0004046437,0.001842681,0.001698614,0.001732813,0.005204794],"category_scores_gemma":[0.001647195,0.000312275,0.0006286722,0.00364434,0.001563117,0.002694369,0.001309491,0.003709954,0.002263408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00177126,"about_ca_system_score_gemma":0.00159513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001609726,"about_ca_topic_score_gemma":0.003488878,"domain_scores_codex":[0.9994196,0.0001812575,0.00005013648,0.000072192,0.0002381685,0.00003873804],"domain_scores_gemma":[0.998578,0.001061263,0.00009664362,0.00004293011,0.0001636742,0.00005748136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006519314,0.00006983359,0.0001291332,0.02025054,0.000169,0.0001489847,0.0000643983,0.0004769644,0.0007657445,0.02146321,0.02276287,0.9336342],"study_design_scores_gemma":[0.00002649778,0.00007084279,0.000562896,0.007502676,0.0001453695,0.0006703152,0.00006516952,0.0002175093,0.0003288401,0.01735255,0.9730293,0.00002798515],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002901267,0.9979964,0.0002986658,0.0004764208,0.0001962868,0.000002571516,0.000006123101,0.000006169754,0.0009883012],"genre_scores_gemma":[0.0004934014,0.9979444,0.0004779108,0.0004289058,0.0002955133,0.000005474939,0.00001396723,0.00000154263,0.000338915],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005204794,"threshold_uncertainty_score":0.01741177,"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."}}