{"id":"W4250846407","doi":"10.26434/chemrxiv.14075408.v1","title":"The Structure-Based Design of SARS-CoV-2 Nsp14 Methyltransferase Ligands Yields Nanomolar Inhibitors","year":2021,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"RNA modifications and cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium; University of Toronto","funders":"European Regional Development Fund; Genentech; Akademie Věd České Republiky; European Federation of Pharmaceutical Industries and Associations; University of Toronto; Ministerstvo Zdravotnictví Ceské Republiky; Ontario Genomics Institute; Gilead Sciences; Merck KGaA; Genome Canada; Fundação de Amparo à Pesquisa do Estado de São Paulo; McGill University; Ontario Genomics; Pfizer","keywords":"Methyltransferase; Biology; RNA; Docking (animal); Biochemistry; Enzyme; Transferase; Chemistry; Methylation; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003020323,0.0002998929,0.0002945113,0.00003938983,0.0001328075,0.00008411621,0.0005058189,0.0005732843,0.00001326686],"category_scores_gemma":[0.00008864223,0.0002377221,0.0002837153,0.0001181585,0.0001832085,0.00000233662,0.0001497324,0.0003496683,8.995514e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000264838,"about_ca_system_score_gemma":0.0006146824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004869251,"about_ca_topic_score_gemma":0.00002701116,"domain_scores_codex":[0.998507,0.00009056765,0.0003982923,0.0005682243,0.0001922919,0.000243659],"domain_scores_gemma":[0.9983386,0.00004082427,0.0001858695,0.001128723,0.0002577625,0.00004816629],"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":[0.00007337811,0.00004320064,0.00007401244,0.00008726331,0.000143547,0.000002300637,0.00005784262,0.00115602,0.9962366,0.00001911039,0.001091685,0.001015095],"study_design_scores_gemma":[0.0002862717,0.00007455572,0.00005395524,0.00006562873,0.0000736926,0.000001993991,0.00005662864,0.0001840953,0.9953154,0.0001157978,0.003505749,0.0002662063],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9576492,0.006466847,0.03463023,0.0002980576,0.000497452,0.0003368338,0.00002875504,0.0000129958,0.00007967635],"genre_scores_gemma":[0.9966861,0.0009964438,0.001401903,0.0002083115,0.0002495383,0.00006622492,0.0002737031,0.00004404565,0.00007371535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03903696,"threshold_uncertainty_score":0.9694026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02854661430444767,"score_gpt":0.275573136832776,"score_spread":0.2470265225283283,"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."}}