{"id":"W2607129213","doi":"10.3389/fchem.2017.00025","title":"Screening and Optimizing Antimicrobial Peptides by Using SPOT-Synthesis","year":2017,"lang":"en","type":"review","venue":"Frontiers in Chemistry","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft; Institute of Infection and Immunity; Karlsruhe Institute of Technology","keywords":"Antimicrobial; Antimicrobial peptides; Peptide; Bacteria; Antibiotics; Pathogenic bacteria; Antibacterial peptide; Microbiology; Chemistry; Biochemistry; Biology; Combinatorial chemistry; Antibacterial activity","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":[0.0006080406,0.001394394,0.001064979,0.001455494,0.0001942445,0.0007439577,0.0006843138,0.0008283664,0.001267989],"category_scores_gemma":[0.0005254228,0.0004035565,0.0006179578,0.001396498,0.0003575301,0.000917327,0.0006206914,0.001041124,0.001660211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003769732,"about_ca_system_score_gemma":0.0003709426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001737871,"about_ca_topic_score_gemma":0.0003555674,"domain_scores_codex":[0.9995384,0.00005738382,0.0000400287,0.00009922915,0.0002175054,0.00004747496],"domain_scores_gemma":[0.9998374,0.00004392544,0.00003714745,0.00001260126,0.00005728253,0.00001148176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008927142,0.0002441207,0.0003609976,0.006565106,0.0000750178,0.0003151994,0.00007604976,0.002023922,0.6406195,0.002675891,0.002363378,0.3445916],"study_design_scores_gemma":[0.00003946704,0.0008728455,0.001300822,0.0004656691,0.0001572676,0.00151144,0.00009252374,0.001572981,0.8094994,0.001329891,0.1830768,0.00008089266],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0774637,0.8093163,0.09057876,0.000716073,0.0007417628,0.0006849493,0.0008596934,0.000517806,0.01912088],"genre_scores_gemma":[0.130116,0.7350664,0.120783,0.0007181795,0.0002628349,0.0007834397,0.001227235,0.0001433396,0.01089965],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.001455494,"threshold_uncertainty_score":0.004241884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0471197023181891,"score_gpt":0.291685121762484,"score_spread":0.2445654194442949,"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."}}