{"id":"W3093258324","doi":"10.1101/2020.10.14.340034","title":"A high throughput RNA displacement assay for screening SARS-CoV-2 nsp10-nsp16 complex towards developing therapeutics for COVID-19","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bacteriophages and microbial interactions","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium; University of Toronto","funders":"Genentech; University of Toronto; Ontario Genomics Institute; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Mitacs; Ontario Genomics; Genome Canada; Fundação de Amparo à Pesquisa do Estado de São Paulo; McGill University; Pfizer","keywords":"RNA; Chemistry; Messenger RNA; Isothermal titration calorimetry; Virtual screening; Methyltransferase; Virology; Biology; Biochemistry; Drug discovery; DNA; Gene","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":[0.001284487,0.001797207,0.001201179,0.0007920446,0.0004664141,0.0004934547,0.001108385,0.001140905,0.002831938],"category_scores_gemma":[0.0006923708,0.0006099237,0.000601342,0.0007946959,0.0003836255,0.0004698665,0.0006536978,0.001310832,0.001734809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005037868,"about_ca_system_score_gemma":0.0003635438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007826329,"about_ca_topic_score_gemma":0.001500594,"domain_scores_codex":[0.9979827,0.000706965,0.0001123248,0.0002890987,0.0007435161,0.0001653603],"domain_scores_gemma":[0.9995964,0.0001532541,0.00005345323,0.00004432032,0.0001086788,0.00004386145],"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":[0.00005156262,0.0001173311,0.0001156323,0.00006105079,0.00001225596,0.00001567053,0.00002494081,0.0001680762,0.997664,0.00006551942,0.00009475817,0.001609171],"study_design_scores_gemma":[0.00001280754,0.0003585237,0.0005112328,0.000005649613,0.00001665765,0.00005597872,0.00001810613,0.002418888,0.9954935,0.00002695115,0.001072353,0.000009360961],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7216579,0.002755403,0.2576395,0.0005177086,0.0002083754,0.001876513,0.004824415,0.00207551,0.008444633],"genre_scores_gemma":[0.6219217,0.002866437,0.3354222,0.0002513975,0.00008552947,0.002158463,0.008926585,0.0002380572,0.02812959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002831938,"threshold_uncertainty_score":0.009473741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09198876421487516,"score_gpt":0.3170181415963668,"score_spread":0.2250293773814916,"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."}}