{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009467201,0.0002524774,0.0003653111,0.0005533284,0.0002329226,0.0003311924,0.0001817729,0.0002512759,0.002058478],"category_scores_gemma":[0.0002050497,0.0001268431,0.0004103982,0.0009477229,0.0000826582,0.000133378,0.0001851235,0.0001809203,0.000197157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000274529,"about_ca_system_score_gemma":0.0003098282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003115176,"about_ca_topic_score_gemma":0.003392036,"domain_scores_codex":[0.9999639,0.000006101795,0.000002486092,0.00001089698,0.000007533555,0.000009129051],"domain_scores_gemma":[0.9999512,0.00001616256,0.00001279552,0.000002391843,0.000008255183,0.000009165129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00215846,0.0005053301,0.1882467,0.00133488,0.000369889,0.004121498,0.0007254054,0.2782984,0.4438777,0.006074774,0.003968165,0.07031882],"study_design_scores_gemma":[0.0001311067,0.001595997,0.300959,0.00008403546,0.000416497,0.002156312,0.001303089,0.6466457,0.03061083,0.004803776,0.01120979,0.00008396417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911442,0.000620398,0.003756015,0.0001381398,0.000003162265,0.00002881265,0.002695328,0.0001198908,0.001494062],"genre_scores_gemma":[0.9828833,0.0007828186,0.007754414,0.00004668581,0.000002892611,0.00004479586,0.00766388,0.00002053593,0.000800705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003115176,"threshold_uncertainty_score":0.006886303,"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."}}