{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003063338,0.0002024274,0.0008932221,0.000437185,0.00005645219,0.00005036641,0.0001238083,0.0004376921,0.00001895511],"category_scores_gemma":[0.0001485722,0.0001642147,0.0002533196,0.0001216483,0.00004879183,0.0006410278,0.00007484342,0.001051187,0.00001091538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007407724,"about_ca_system_score_gemma":0.0001694111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008428001,"about_ca_topic_score_gemma":5.475488e-7,"domain_scores_codex":[0.9984364,0.0000664493,0.001197954,0.00008157214,0.00003632674,0.0001812804],"domain_scores_gemma":[0.9990956,0.00009981962,0.0006207003,0.00005711848,0.0001064235,0.00002037407],"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":[0.00006118038,0.00003648493,0.000003877485,0.007302549,0.0001499545,0.000001982002,0.0004003361,0.00192253,0.0007752906,0.00006595979,0.0009864828,0.9882933],"study_design_scores_gemma":[0.00145242,0.0000775752,3.561437e-7,0.03462193,0.0005298483,0.0008052351,0.0003702343,0.004480026,0.0005575978,0.00004664049,0.9566366,0.0004215179],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0009714023,0.9925048,0.005396549,0.00002989777,0.0004016883,0.0001525547,0.0000128084,0.00001925291,0.0005109838],"genre_scores_gemma":[0.002450384,0.9969296,0.0002363518,0.00006931352,0.00005788737,0.000002253109,0.0001376852,0.000007241568,0.0001092593],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9878718,"threshold_uncertainty_score":0.6696483,"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."}}