{"id":"W2804209870","doi":"10.1002/cmdc.201800166","title":"Structure‐Based Design of a Eukaryote‐Selective Antiprotozoal Fluorinated Aminoglycoside","year":2018,"lang":"en","type":"article","venue":"ChemMedChem","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Japan Society for the Promotion of Science; Fonds de recherche du Québec – Nature et technologies; Ichiro Kanehara Foundation for the Promotion of Medical Sciences and Medical Care; Japan Science Society; Kurata Memorial Hitachi Science and Technology Foundation; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Medical Research Council","keywords":"Antiprotozoal; Eukaryote; Ribosome; Ribosomal RNA; Biology; Aminoglycoside; RNA; Biochemistry; Chemistry; Stereochemistry; Computational biology; Antibiotics; 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":[0.00009187886,0.0003029558,0.0002550125,0.0001374972,0.0001964071,0.000319189,0.0003054034,0.0003771163,0.0006551922],"category_scores_gemma":[0.00007140288,0.000191696,0.0002168379,0.0001364707,0.0001545341,0.0001619307,0.0001801121,0.00038461,0.0003041243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005149239,"about_ca_system_score_gemma":0.0005054306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009720659,"about_ca_topic_score_gemma":0.001086968,"domain_scores_codex":[0.9999392,0.00000844384,0.000004757462,0.00001157218,0.00001958955,0.0000164348],"domain_scores_gemma":[0.9999673,0.000002050041,0.00000967688,0.000002357648,0.000007117919,0.00001147763],"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":[0.0001438097,0.00008869386,0.0001495328,0.00007735882,0.000008926839,0.0001315768,0.00002647458,0.004795369,0.989312,0.0009364638,0.0001218977,0.004207956],"study_design_scores_gemma":[0.0001877296,0.001817951,0.001190821,0.00002094296,0.00003452562,0.0003014287,0.00004733331,0.01376025,0.9682073,0.0002030344,0.0142038,0.00002487686],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802066,0.001568755,0.01145255,0.0002204224,0.00004947814,0.0002867372,0.0003436138,0.0001232079,0.005748697],"genre_scores_gemma":[0.9777898,0.001759795,0.01761915,0.00009296817,0.000008271295,0.0001161196,0.0004647858,0.00001824244,0.002130833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009720659,"threshold_uncertainty_score":0.003736079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01772863788062315,"score_gpt":0.2493616746258466,"score_spread":0.2316330367452235,"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."}}