{"id":"W4320170165","doi":"10.32920/21950375","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; Coronavirus disease 2019 (COVID-19); Drug target; Drug development; Biology; Virology; Disease; Infectious disease (medical specialty); Medicine; Bioinformatics; Pharmacology; 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":[0.0002873361,0.0004091403,0.0005215195,0.0005227345,0.0001433993,0.0003861866,0.0002524884,0.0003671378,0.0008809323],"category_scores_gemma":[0.0008553806,0.0001710496,0.0005668757,0.000273794,0.000243768,0.000367342,0.000336415,0.0003179262,0.00007319074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000593062,"about_ca_system_score_gemma":0.0006568256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005076264,"about_ca_topic_score_gemma":0.006771858,"domain_scores_codex":[0.9999052,0.00003052645,0.000004011553,0.00002087891,0.00002427222,0.00001500637],"domain_scores_gemma":[0.9998353,0.00008480183,0.0000318323,0.00001641356,0.00001623362,0.00001549601],"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":[0.0001431652,0.000112727,0.006976638,0.0000889775,0.0001550791,0.0001176226,0.0000158765,0.9437075,0.01645244,0.004743628,0.0009431863,0.02654316],"study_design_scores_gemma":[0.00000478393,0.00001706348,0.0008131286,0.000001111285,0.00001120241,0.000006228982,0.000002730732,0.9968388,0.0008652247,0.001319317,0.0001184261,0.00000198211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8996517,0.0007170312,0.09307323,0.0008853672,0.00003001186,0.00004174856,0.0005420732,0.0006425017,0.004416267],"genre_scores_gemma":[0.9823472,0.0002056977,0.01639211,0.0000729978,0.00001198616,0.00001821094,0.0003937211,0.00002175954,0.0005363445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005076264,"threshold_uncertainty_score":0.01009345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05620093117549998,"score_gpt":0.3718164274611975,"score_spread":0.3156154962856976,"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."}}