{"id":"W4212913059","doi":"10.1016/j.jbc.2022.101739","title":"Improved SARS-CoV-2 main protease high-throughput screening assay using a 5-carboxyfluorescein substrate","year":2022,"lang":"en","type":"article","venue":"Journal of Biological Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"High-throughput screening; Protease; Coronavirus; Drug discovery; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Substrate (aquarium); Computational biology; False positive paradox; Coronavirus disease 2019 (COVID-19); Chemistry; Enzyme; Biology; Virology; Biochemistry; Medicine; Computer science; Machine learning","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":[0.0009707854,0.001584705,0.001496851,0.0009171545,0.0004919488,0.000466032,0.00129971,0.001248747,0.00313109],"category_scores_gemma":[0.0005248632,0.0006623126,0.0009650619,0.0008540843,0.0002267595,0.0005024056,0.0005064853,0.001593903,0.002617097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005749927,"about_ca_system_score_gemma":0.0004168504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001314495,"about_ca_topic_score_gemma":0.003293468,"domain_scores_codex":[0.9984829,0.0003781251,0.0001215361,0.0002673345,0.0005922653,0.0001578077],"domain_scores_gemma":[0.9995561,0.0001026398,0.00005033834,0.0000660387,0.0001742357,0.00005073127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001167845,0.00008119686,0.000142554,0.0001441972,0.00002472442,0.00005174059,0.00001224874,0.00008788018,0.9957979,0.00006652924,0.0005187757,0.002955481],"study_design_scores_gemma":[0.00003318734,0.0002781836,0.001382623,0.00001368123,0.00004528025,0.0004604736,0.00001171824,0.001482611,0.9917829,0.00003319434,0.004449567,0.00002652709],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5745714,0.02132822,0.3597767,0.001686386,0.0009546049,0.001540894,0.0155121,0.006093028,0.0185366],"genre_scores_gemma":[0.6003339,0.0106465,0.331524,0.0006590626,0.0001533948,0.001756922,0.03257728,0.0003067891,0.02204231],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00313109,"threshold_uncertainty_score":0.01047456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07001068301784276,"score_gpt":0.3197684545971118,"score_spread":0.249757771579269,"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."}}