{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001525322,0.0002435708,0.0003534733,0.0003742084,0.00180081,0.0001478464,0.0007322103,0.0001161645,0.0001011371],"category_scores_gemma":[0.0002699192,0.0002159871,0.0001746877,0.001153433,0.001581326,0.0006257856,0.0004469795,0.0002413559,0.00004791794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005882634,"about_ca_system_score_gemma":0.0008077566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002290915,"about_ca_topic_score_gemma":0.00003507173,"domain_scores_codex":[0.9974625,0.0002464044,0.0005614148,0.0008663978,0.0002234539,0.0006398239],"domain_scores_gemma":[0.9984068,0.001001482,0.0002324117,0.000124862,0.0002019374,0.00003248662],"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":[0.0002494019,0.0006625635,0.007411206,0.00004753213,0.0000872194,0.0000221746,0.002289962,0.2353711,0.7293979,0.01614596,0.005538827,0.002776208],"study_design_scores_gemma":[0.00383091,0.002202075,0.02020635,0.0004612786,0.00002842808,0.00009233126,0.01680526,0.3680657,0.4900731,0.0932046,0.003715825,0.001314211],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7983924,0.002440658,0.1871497,0.006008216,0.002410914,0.0005276896,0.0001393445,0.0001162932,0.002814827],"genre_scores_gemma":[0.9226106,0.000001867678,0.07617638,0.0009107292,0.00005554242,0.00004525784,0.0001102564,0.000009498764,0.00007986981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2393248,"threshold_uncertainty_score":0.9994987,"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."}}