{"id":"W3207417464","doi":"10.1242/dmm.049145","title":"An anti-tuberculosis compound screen using a zebrafish infection model identifies an aspartyl-tRNA synthetase inhibitor","year":2021,"lang":"en","type":"article","venue":"Disease Models & Mechanisms","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Infection and Immunity; Innovative Medicines Initiative; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; European Commission; Rijksuniversiteit Groningen; Amsterdam University Medical Centers; European Federation of Pharmaceutical Industries and Associations","keywords":"Zebrafish; Mycobacterium tuberculosis; In vivo; Drug discovery; Tuberculosis; Biology; Docking (animal); Mutant; Computational biology; Phenotypic screening; Mycobacterium marinum; Microbiology; Genetics; Biochemistry; Phenotype; Medicine; Gene","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":[0.000188897,0.0008383184,0.0006248766,0.0005835921,0.0002398755,0.0002679116,0.0004300755,0.0004608149,0.001954762],"category_scores_gemma":[0.0001366703,0.0002149195,0.000438843,0.0003495184,0.0002329932,0.0002059838,0.0002468979,0.000572174,0.0005215885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005490532,"about_ca_system_score_gemma":0.0006322528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002688372,"about_ca_topic_score_gemma":0.007770092,"domain_scores_codex":[0.9998534,0.00001903498,0.000008659955,0.00003191331,0.00005856884,0.0000284308],"domain_scores_gemma":[0.9999291,0.00001196574,0.0000189416,0.000006464272,0.00001457199,0.00001896335],"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":[0.00007213597,0.00003480087,0.0001657585,0.00004350159,0.000008618568,0.00007656173,0.00000623524,0.0002066727,0.9979194,0.00003164645,0.00005625149,0.001378507],"study_design_scores_gemma":[0.00005002731,0.002408497,0.003169242,0.00001152471,0.00006198864,0.000317362,0.0000211706,0.000985898,0.989846,0.00003751349,0.003076772,0.00001408915],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9806444,0.003933888,0.008384699,0.0003033923,0.00004934969,0.0002450165,0.002370608,0.0002865514,0.003781986],"genre_scores_gemma":[0.9713809,0.004962501,0.01194134,0.0001303762,0.000009092636,0.0001466185,0.002989672,0.00004898553,0.008390324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002688372,"threshold_uncertainty_score":0.006539345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02821021328767874,"score_gpt":0.2727264609128473,"score_spread":0.2445162476251686,"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."}}