{"id":"W4393074594","doi":"10.1016/j.ebiom.2024.105073","title":"High-throughput screening of small-molecules libraries identified antibacterials against clinically relevant multidrug-resistant A. baumannii and K. pneumoniae","year":2024,"lang":"en","type":"article","venue":"EBioMedicine","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Drugs for Neglected Diseases initiative; Canton de Genève; European Commission; Bundesministerium für Bildung und Forschung; Public Health Agency; Public Health Agency of Canada; Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie; Wellcome Trust","keywords":"Acinetobacter baumannii; Klebsiella pneumoniae; Antibiotics; Multiple drug resistance; Drug resistance; Microbiology; Drug; Multi drug resistant; Biology; Medicine; Bacteria; Gene; Pharmacology; Escherichia coli; Genetics","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":[0.0007342499,0.0009233279,0.0009208254,0.001109348,0.0004472005,0.0009368343,0.0005360952,0.0006127417,0.002820882],"category_scores_gemma":[0.0009436111,0.0002595452,0.0006887387,0.00108825,0.0003947509,0.0004468852,0.0005276197,0.0006760213,0.001276944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005483621,"about_ca_system_score_gemma":0.0009177073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006814211,"about_ca_topic_score_gemma":0.002329092,"domain_scores_codex":[0.9991997,0.0001310318,0.00006994334,0.0001459801,0.0003601745,0.00009323783],"domain_scores_gemma":[0.9994339,0.0002105505,0.00008894201,0.0000594161,0.0001340708,0.00007298651],"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":[0.0004107511,0.0006170658,0.00205807,0.0003754127,0.00007727502,0.000177151,0.00004422558,0.001538968,0.9761813,0.0001755794,0.0005546626,0.01778949],"study_design_scores_gemma":[0.000152465,0.004623204,0.01368283,0.00005162771,0.0002712032,0.000763475,0.00006951258,0.003676163,0.9710267,0.000182338,0.005452903,0.00004764648],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9593595,0.004286664,0.02043852,0.000496859,0.0000750507,0.00146137,0.006738758,0.001039193,0.006104044],"genre_scores_gemma":[0.9450648,0.004826513,0.03446579,0.0003854851,0.00005880106,0.0007344832,0.009276964,0.0001120267,0.005075076],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002820882,"threshold_uncertainty_score":0.009436786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02874300681253746,"score_gpt":0.3082191690923003,"score_spread":0.2794761622797629,"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."}}