{"id":"W4318704876","doi":"10.32920/21982913","title":"Multiscale interactome analysis coupled with off‑target drug predictions reveals drug repurposing candidates for human coronavirus disease","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; Hospital for Sick Children; Vector Institute; University of Toronto; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Drug repositioning; Interactome; Coronavirus; Drug; Repurposing; In silico; Drug discovery; Computational biology; Biology; Drug target; Drug development; Coronavirus disease 2019 (COVID-19); Virology; Antiviral drug; Disease; Infectious disease (medical specialty); Pharmacology; Medicine; Bioinformatics; Gene; 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":[0.0002907903,0.0004230405,0.0005388694,0.0005233687,0.0001547281,0.0003875543,0.0002913695,0.0004081841,0.0008920609],"category_scores_gemma":[0.0008349638,0.0001911083,0.0006026777,0.0002712361,0.0002562912,0.0003989665,0.0003390838,0.0003482246,0.00007053386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006770762,"about_ca_system_score_gemma":0.0006859541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00615266,"about_ca_topic_score_gemma":0.007774968,"domain_scores_codex":[0.9999131,0.00002792251,0.000003588541,0.00001993715,0.00002035171,0.0000150816],"domain_scores_gemma":[0.9998282,0.0000903863,0.00003223312,0.00001640533,0.00001662843,0.0000161271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001077446,0.00008511937,0.004396049,0.00006457952,0.0001160594,0.00008369908,0.00001173038,0.9617661,0.008968556,0.003822058,0.0007295238,0.0198488],"study_design_scores_gemma":[0.000003570616,0.0000128274,0.0005110197,8.441547e-7,0.00000891961,0.000004571101,0.000001880851,0.9977215,0.0005270355,0.001123265,0.00008301833,0.000001505038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.88498,0.0007579107,0.1076078,0.0009257119,0.00003246308,0.00004438777,0.0005315825,0.0006633273,0.004456861],"genre_scores_gemma":[0.9834778,0.0001958778,0.01524333,0.00007616908,0.0000119693,0.00001897423,0.0003925081,0.00002050953,0.0005630065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00615266,"threshold_uncertainty_score":0.01223367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05654311852971072,"score_gpt":0.3724187440506735,"score_spread":0.3158756255209628,"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."}}