{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004032543,0.0004597287,0.0003847124,0.0001182331,0.0004463746,0.0003221647,0.0003068994,0.0002908961,0.0000716073],"category_scores_gemma":[0.00005561686,0.0005006589,0.0003015254,0.0001787067,0.00006419233,0.000142716,0.0001993481,0.0001092481,0.00001200526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004245591,"about_ca_system_score_gemma":0.0003072796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001415614,"about_ca_topic_score_gemma":0.0001016116,"domain_scores_codex":[0.9970102,0.0003892339,0.0004300907,0.001131298,0.0004920877,0.0005470585],"domain_scores_gemma":[0.9976526,0.0000108796,0.000164664,0.001196422,0.0003135889,0.0006618782],"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":[0.0001375896,0.0004978447,0.00002970565,0.00004633405,0.00009987941,0.00005775789,0.00006074865,0.07289553,0.9219993,0.00353421,0.00004726261,0.0005938468],"study_design_scores_gemma":[0.0003298867,0.0001048597,0.00003315132,0.00005494728,0.0001935873,0.00002103104,0.00006399505,0.3307654,0.637278,0.03065109,0.00002934475,0.0004747198],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6192707,0.0003084667,0.3795131,0.00004565156,0.0001908718,0.0002736252,0.0002710487,0.00006851641,0.00005803454],"genre_scores_gemma":[0.9866792,0.0001419999,0.01097879,0.0005057086,0.0003037119,0.00008527815,0.001097879,0.0001181172,0.00008932296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3685343,"threshold_uncertainty_score":0.9997445,"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."}}