{"id":"W4412110320","doi":"10.1021/acs.jcim.5c00530","title":"Deep Learning in Antimicrobial Peptide Prediction","year":2025,"lang":"en","type":"review","venue":"Journal of Chemical Information and Modeling","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Fundo para o Desenvolvimento das Ciências e da Tecnologia; National Natural Science Foundation of China","keywords":"Interpretability; Deep learning; Artificial intelligence; Computer science; Machine learning; Field (mathematics); Perspective (graphical); Data science; Antimicrobial peptides; Antimicrobial; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006826934,0.0007705957,0.0009392889,0.000865981,0.0001554757,0.001044661,0.0007995169,0.001020026,0.001923283],"category_scores_gemma":[0.00127995,0.0003392298,0.0007730835,0.001197497,0.0003555723,0.001129864,0.000686214,0.001652375,0.0009938338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005791385,"about_ca_system_score_gemma":0.0008631607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001985868,"about_ca_topic_score_gemma":0.001814928,"domain_scores_codex":[0.9997991,0.00004573407,0.00001843617,0.00004675174,0.00006877516,0.00002119902],"domain_scores_gemma":[0.9996753,0.0001848759,0.00003095957,0.00001307099,0.00007960632,0.00001623502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007348361,0.0000970066,0.001008566,0.005656031,0.0002565347,0.0001367307,0.00004791348,0.03412678,0.003147139,0.02518525,0.01804547,0.912219],"study_design_scores_gemma":[0.0000701153,0.0004675858,0.002888579,0.00614992,0.0006448648,0.001058442,0.0001104892,0.2070889,0.01501767,0.08912813,0.6771904,0.0001848961],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003951988,0.9350647,0.05206533,0.002108,0.0006128856,0.00003518088,0.0003215559,0.0002301489,0.005610096],"genre_scores_gemma":[0.04368489,0.9282741,0.02227484,0.0008453054,0.0006296252,0.00006420909,0.0005180331,0.0000374304,0.003671624],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.001985868,"threshold_uncertainty_score":0.006434083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02001041243644887,"score_gpt":0.2701305976309453,"score_spread":0.2501201851944964,"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."}}