{"id":"W2801639712","doi":"10.1002/cmdc.201800161","title":"Fragment‐Based Phenotypic Lead Discovery: Cell‐Based Assay to Target Leishmaniasis","year":2018,"lang":"en","type":"article","venue":"ChemMedChem","topic":"Research on Leishmaniasis Studies","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Phenotypic screening; Amastigote; Drug discovery; Axenic; Leishmania; Computational biology; Small molecule; Biology; Chemical library; Phenotype; Lead compound; Fragment (logic); Leishmaniasis; In vitro; Bioinformatics; Biochemistry; Genetics; Computer science; 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006906224,0.0004833995,0.0007414046,0.0003024059,0.0002898393,0.0001231569,0.0004493862,0.0001954201,0.001080111],"category_scores_gemma":[0.0009081138,0.0004212759,0.0002732622,0.0008160633,0.0004344134,0.0002133227,0.0002295408,0.0004408565,0.001505851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004901206,"about_ca_system_score_gemma":0.0004535824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005304383,"about_ca_topic_score_gemma":0.00003563612,"domain_scores_codex":[0.9962738,0.00009481256,0.0004716167,0.0009090689,0.001138788,0.001111962],"domain_scores_gemma":[0.9973594,0.0002975289,0.0001262099,0.00114085,0.000502845,0.0005731444],"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":[0.002000135,0.002546836,0.03955808,0.001111551,0.0004358104,0.0003793253,0.001232157,0.0000404192,0.556509,0.0002581141,0.3882943,0.00763429],"study_design_scores_gemma":[0.002688332,0.0007313495,0.007391003,0.0001411187,0.00008995549,0.000005726624,0.0002584398,0.0009811295,0.9174382,0.00006675345,0.06978417,0.0004238229],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7828413,0.0008616646,0.03149503,0.04520519,0.0007620397,0.002551529,0.000100377,0.0006794525,0.1355035],"genre_scores_gemma":[0.9666889,0.000009595725,0.01538025,0.00647,0.0009454688,0.0002331987,0.00008619952,0.0001180051,0.01006834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3609292,"threshold_uncertainty_score":0.999833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03271862383978037,"score_gpt":0.3051362606192915,"score_spread":0.2724176367795111,"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."}}