{"id":"W4391565056","doi":"10.1128/aac.01210-23","title":"Repurposing screen identifies novel candidates for broad-spectrum coronavirus antivirals and druggable host targets","year":2024,"lang":"en","type":"article","venue":"Antimicrobial Agents and Chemotherapy","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; Helmholtz-Alberta Initiative; Deutsche Forschungsgemeinschaft; Knut och Alice Wallenbergs Stiftelse; Bundesministerium für Bildung und Forschung; European Commission; Niedersächsisches Ministerium für Wissenschaft und Kultur; Deutsches Zentrum für Infektionsforschung","keywords":"Druggability; Drug repositioning; Repurposing; Biology; Drug discovery; Coronavirus; Farnesyltransferase; Computational biology; Virology; Gene; Genetics; Prenylation; Drug; Coronavirus disease 2019 (COVID-19); Bioinformatics; Pharmacology; Biochemistry; Infectious disease (medical specialty); Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001530086,0.0005916807,0.0006315596,0.0006835864,0.0001550243,0.0002635804,0.0002548812,0.0002966,0.004010453],"category_scores_gemma":[0.0001660858,0.0001494355,0.0004575169,0.0003859041,0.0001219582,0.000187226,0.0002443852,0.0004059114,0.0008333641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002263914,"about_ca_system_score_gemma":0.0002669817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003843694,"about_ca_topic_score_gemma":0.001064835,"domain_scores_codex":[0.9998884,0.00001451319,0.0000091255,0.00002468485,0.00003631461,0.00002682],"domain_scores_gemma":[0.9999071,0.00001941198,0.0000161659,0.00001075536,0.00002412176,0.00002242198],"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":[0.0003572745,0.0001953944,0.0008976435,0.0001312468,0.00002895905,0.0001887488,0.00001297547,0.000429692,0.9829795,0.0001136197,0.0002934984,0.0143715],"study_design_scores_gemma":[0.0002677297,0.006012851,0.008358322,0.0000258635,0.0002035114,0.001126721,0.00004649933,0.002418238,0.9700689,0.0001107413,0.01133578,0.0000248021],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9818165,0.00398011,0.005131358,0.0002122668,0.00005169177,0.0005609337,0.002426064,0.0004475249,0.005373475],"genre_scores_gemma":[0.9761055,0.002633605,0.008344236,0.0003018547,0.00002450433,0.000218309,0.004448177,0.00004864595,0.007875131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004010453,"threshold_uncertainty_score":0.01341623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03534776844650133,"score_gpt":0.3404281759965299,"score_spread":0.3050804075500286,"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."}}