{"id":"W4317651666","doi":"10.1186/s12866-023-02756-6","title":"Genomic landscape of the emerging XDR Salmonella Typhi for mining druggable targets clpP, hisH, folP and gpmI and screening of novel TCM inhibitors, molecular docking and simulation analyses","year":2023,"lang":"en","type":"article","venue":"BMC Microbiology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Penn Center for Musculoskeletal Disorders; Directorate for Biological Sciences; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Alliance de recherche numérique du Canada; Universidade Estadual de Campinas; Centro Nacional de Processamento de Alto Desempenho em São Paulo; University of Karachi","keywords":"Druggability; Biology; Salmonella typhi; Computational biology; Docking (animal); Genome; Homology modeling; Gene; Genetics; Biochemistry; Enzyme","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":[],"consensus_categories":[],"category_scores_codex":[0.0005628968,0.0001165064,0.0002408604,0.0001794634,0.0001043578,0.0000260719,0.0001793986,0.00007701739,7.9605e-7],"category_scores_gemma":[0.0001802912,0.00009937816,0.00005464795,0.0003024922,0.00008607457,0.00009980522,0.0004727373,0.00006251744,1.421244e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008454424,"about_ca_system_score_gemma":0.00004395962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001668314,"about_ca_topic_score_gemma":0.000008086083,"domain_scores_codex":[0.9990098,0.0001318231,0.0002977667,0.0003294293,0.00004952861,0.000181582],"domain_scores_gemma":[0.9984695,0.001038402,0.0002246692,0.0001689612,0.00007237772,0.00002603053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001895132,0.000009886196,0.01231831,0.00007836166,0.00004270023,4.383137e-7,0.0006613426,0.5100784,0.4742438,0.0004759267,0.00004272323,0.002029119],"study_design_scores_gemma":[0.00064086,0.00004140054,0.0149991,0.00005883554,0.00003453246,0.00001238094,0.0001308115,0.9665532,0.01660684,0.0006093945,0.0001812411,0.0001314385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.670224,0.001152455,0.3283008,0.00005880597,0.00008850824,0.0001389996,0.00001511554,0.00001497082,0.000006371363],"genre_scores_gemma":[0.9111663,0.00001453231,0.08872259,0.00003391028,0.00002669173,0.00000423574,0.00001716451,0.000009358321,0.000005280403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.457637,"threshold_uncertainty_score":0.4052523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05446994136537289,"score_gpt":0.3383271778713511,"score_spread":0.2838572365059783,"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."}}