{"id":"W50463116","doi":"10.1201/9780203967294-7","title":"Drug-target discovery in silico: using the web to identify novel molecular targets for drug action","year":2018,"lang":"en","type":"article","venue":"","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"In silico; Computational biology; Drug discovery; Drug; Drug action; Drug target; Action (physics); Computer science; Biology; Bioinformatics; Pharmacology; Genetics; Gene; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00315281,0.001770989,0.001653572,0.003095899,0.0005341382,0.003207669,0.001864176,0.001787259,0.01182193],"category_scores_gemma":[0.006319228,0.0009461273,0.001751981,0.001731868,0.00083432,0.002895156,0.001905191,0.002409094,0.008027566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004770753,"about_ca_system_score_gemma":0.001321696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001020107,"about_ca_topic_score_gemma":0.001449454,"domain_scores_codex":[0.9989907,0.0004630736,0.00008863691,0.0001343322,0.0002852328,0.00003802883],"domain_scores_gemma":[0.9964773,0.002482198,0.0001760651,0.0005014935,0.0001874722,0.0001754746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002637226,0.001936514,0.01048403,0.007524106,0.002405104,0.001924136,0.0005367227,0.09178717,0.04575806,0.07584782,0.1463582,0.612801],"study_design_scores_gemma":[0.001209766,0.0004508211,0.002351157,0.0009203954,0.0006868932,0.001467398,0.0002166185,0.4359941,0.04115213,0.1723299,0.3429121,0.0003087191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01648943,0.006844318,0.7720658,0.005741621,0.0006689201,0.0006967938,0.01208661,0.1551365,0.03027003],"genre_scores_gemma":[0.1231475,0.01047502,0.8280353,0.003042981,0.0003151482,0.001237202,0.01995381,0.006238707,0.007554269],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01182193,"threshold_uncertainty_score":0.03954828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04889217085464192,"score_gpt":0.3838699163935759,"score_spread":0.334977745538934,"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."}}