{"id":"W7116926340","doi":"10.1021/acs.jcim.5c01992","title":"Design of Highly Potent Antibiofilm, Antimicrobial Peptides Using Explainable Artificial Intelligence","year":2025,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity; University of British Columbia","funders":"Canadian Institutes of Health Research; University of British Columbia; Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche; Université de Strasbourg","keywords":"Rational design; Pipeline (software); Antimicrobial peptides; Peptide; Limiting; Autoencoder; Chemical space","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":[0.0003197162,0.0001068847,0.0002807135,0.000208878,0.00008293625,0.00003759841,0.0001091675,0.0001376778,0.000008790939],"category_scores_gemma":[0.00006921973,0.00009080968,0.0000818876,0.00009965758,0.0001137902,0.0006629137,0.00005741336,0.0002142481,0.000002597296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002977525,"about_ca_system_score_gemma":0.0001095401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002673246,"about_ca_topic_score_gemma":9.70811e-8,"domain_scores_codex":[0.9989274,0.00003108989,0.000786446,0.0000583257,0.00003780233,0.0001589077],"domain_scores_gemma":[0.9992445,0.00005444451,0.0003616159,0.00006129438,0.000261152,0.00001706399],"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":[0.0002796711,0.00004008288,0.000007282063,0.00008263815,0.00005870759,5.389531e-7,0.0003538548,0.03941246,0.9531011,0.001061731,0.0001882879,0.005413649],"study_design_scores_gemma":[0.0002102241,0.00004401058,6.787415e-7,0.0003091606,0.00004510473,0.00007321193,0.0008239651,0.05747633,0.9400723,0.0007837319,0.00007352745,0.00008777598],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5553205,0.0001729704,0.4441143,0.000108366,0.0001528954,0.00005480181,0.000003436218,0.000005611528,0.00006708095],"genre_scores_gemma":[0.9907054,0.0001649862,0.008918246,0.0001653181,0.00002102075,2.247837e-7,0.000006309356,0.000003222545,0.00001525067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4353849,"threshold_uncertainty_score":0.3703111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04042281080612997,"score_gpt":0.2690691269145881,"score_spread":0.2286463161084581,"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."}}