{"id":"W2252622713","doi":"10.1371/journal.pone.0135961","title":"In Vivo, In Vitro, and In Silico Characterization of Peptoids as Antimicrobial Agents","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of Allergy and Infectious Diseases; Canadian Institutes of Health Research; National Institutes of Health; Camille and Henry Dreyfus Foundation; Danmarks Frie Forskningsfond; Northwestern University; U.S. Department of Homeland Security","keywords":"Peptoid; In silico; Antimicrobial; Antimicrobial peptides; In vivo; Computational biology; In vitro; Antibiotics; Protease; Biology; Drug discovery; Chemistry; Microbiology; Peptide; Biochemistry; Enzyme; Biotechnology","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":[0.00009684057,0.0001031295,0.0002963257,0.0002046252,0.00001711861,0.000004732441,0.00007315139,0.0001457748,0.0003806745],"category_scores_gemma":[0.00003694256,0.00008711663,0.00002061386,0.0001070174,0.0001351929,0.0001966718,0.00006024697,0.0001044035,0.00004913994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003261994,"about_ca_system_score_gemma":0.00002141786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002529459,"about_ca_topic_score_gemma":0.00008686817,"domain_scores_codex":[0.999245,0.0000775332,0.0002605207,0.0001853298,0.00002099118,0.0002106085],"domain_scores_gemma":[0.9997542,0.00004419833,0.00008097039,0.0000955104,0.00001668869,0.000008442381],"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":[0.0003451626,0.0007465785,0.03035538,0.00004339997,0.00002502793,0.000004695656,0.0003294114,7.599986e-8,0.9677283,0.00002624147,0.00005086025,0.0003448824],"study_design_scores_gemma":[0.001421221,0.00004673141,0.05937557,0.000333149,0.000007125551,0.000003389954,0.00002729975,0.000001476181,0.9385616,0.00002586585,0.00009636942,0.0001002012],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990128,0.0000254532,0.000003197236,0.0004716618,0.00005114108,0.0001770849,0.00007122633,0.000008594023,0.0001788555],"genre_scores_gemma":[0.9976425,0.0002917017,0.00002849461,0.0001874003,0.00001327446,0.000006677571,0.0000224736,0.00001042951,0.001797019],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02916668,"threshold_uncertainty_score":0.4168119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01653174271063124,"score_gpt":0.2090497287866142,"score_spread":0.1925179860759829,"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."}}