{"id":"W3186573005","doi":"10.1007/s12539-021-00461-4","title":"Target-Based In Silico Screening for Phytoactive Compounds Targeting SARS-CoV-2","year":2021,"lang":"en","type":"article","venue":"Interdisciplinary Sciences Computational Life Sciences","topic":"Toxin Mechanisms and Immunotoxins","field":"Immunology and Microbiology","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; University of Guelph; Canadian Institute for Advanced Research","funders":"","keywords":"Druggability; In silico; Coronavirus; Protease; Lipinski's rule of five; Virology; Medicine; Vero cell; Pneumonia; Drug repositioning; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Drug discovery; Ritonavir; Pharmacology; Biology; Drug; Computational biology; Coronavirus disease 2019 (COVID-19); Bioinformatics; Disease; Virus; Enzyme; Viral load; Gene; Infectious disease (medical specialty); Genetics; Biochemistry","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":[0.0002685243,0.0009622945,0.001119433,0.0004203668,0.0002732927,0.0006399234,0.0005997727,0.0005702866,0.003816105],"category_scores_gemma":[0.0003744134,0.0003719639,0.001051274,0.0003566144,0.0001269268,0.0002478463,0.0002926521,0.0004580771,0.0007935344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004254135,"about_ca_system_score_gemma":0.0005852222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002145118,"about_ca_topic_score_gemma":0.003909879,"domain_scores_codex":[0.9998703,0.00002661236,0.000007528656,0.00003091816,0.00004250813,0.00002206446],"domain_scores_gemma":[0.9998937,0.00005433077,0.00001499945,0.00000617121,0.0000219322,0.000008851348],"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":[0.002265913,0.001428525,0.01030603,0.001917855,0.0007248058,0.000916485,0.00009000048,0.5473648,0.3957504,0.001843152,0.00236395,0.03502801],"study_design_scores_gemma":[0.0003599263,0.00283917,0.002815419,0.0000387014,0.0006981393,0.0003358307,0.00008503698,0.8549547,0.1329175,0.0005892371,0.004326381,0.00003994165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9553939,0.001706192,0.02802232,0.0002209904,0.00005247892,0.0002697976,0.004246557,0.001531713,0.008556025],"genre_scores_gemma":[0.9673011,0.00134646,0.02383697,0.0001567308,0.000009539763,0.0001815465,0.004408282,0.00009159347,0.00266777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003816105,"threshold_uncertainty_score":0.01276612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04784682614767079,"score_gpt":0.3390140320351006,"score_spread":0.2911672058874298,"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."}}