{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005458557,0.0005353491,0.0005684494,0.0002489741,0.0001034898,0.0004321445,0.0002693754,0.0003109948,0.000911725],"category_scores_gemma":[0.0006714003,0.0001846269,0.0004809705,0.0002241559,0.0001904647,0.0002677586,0.0001969798,0.0005635563,0.0002020822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002201208,"about_ca_system_score_gemma":0.0002266474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005918352,"about_ca_topic_score_gemma":0.000532827,"domain_scores_codex":[0.9997718,0.00008895036,0.000015217,0.0000368388,0.00005912513,0.0000280807],"domain_scores_gemma":[0.9996384,0.0002223063,0.00006358767,0.00001753924,0.00003197468,0.0000261713],"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":[0.000352759,0.0002267887,0.00282522,0.0003191535,0.00005302629,0.0001246092,0.00003796339,0.1635361,0.8275821,0.0004305376,0.0001202947,0.004391545],"study_design_scores_gemma":[0.0000524897,0.003664288,0.003696052,0.00002669289,0.00008559287,0.0001333042,0.00006538963,0.365561,0.624522,0.000363558,0.001800392,0.00002918574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9758468,0.001742518,0.01996931,0.0001109264,0.00002035883,0.00006433838,0.0009357621,0.0000425831,0.001267445],"genre_scores_gemma":[0.9866725,0.001645667,0.01026749,0.00004714849,0.00001098975,0.00006366517,0.0007358963,0.00001573293,0.0005408888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000911725,"threshold_uncertainty_score":0.00304997,"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."}}