{"id":"W4392200710","doi":"10.1101/2024.02.22.581480","title":"ProT-Diff: A Modularized and Efficient Approach to De Novo Generation of Antimicrobial Peptide Sequences through Integration of Protein Language Model and Diffusion Model","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"MD Precision (Canada)","funders":"","keywords":"Antimicrobial; In silico; Antimicrobial peptides; Computational biology; Peptide; Biology; Biochemistry; Chemistry; Microbiology; Gene","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":[0.000642677,0.0007560736,0.0005020177,0.0003499897,0.0002497899,0.000527059,0.001271553,0.0008984905,0.00234917],"category_scores_gemma":[0.0008981366,0.0004829033,0.0008391285,0.0002423303,0.0004800416,0.0006199261,0.001198255,0.00155954,0.0009717363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005726787,"about_ca_system_score_gemma":0.0007073292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0010068,"about_ca_topic_score_gemma":0.001256585,"domain_scores_codex":[0.9997545,0.00005058089,0.00001506811,0.00006627922,0.00008427089,0.00002939527],"domain_scores_gemma":[0.9997037,0.000137433,0.00003655876,0.00004242346,0.00004438019,0.00003543826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002948426,0.0002496838,0.001752629,0.0002970965,0.0001496695,0.000577918,0.0001107664,0.5833208,0.2275412,0.02322048,0.004618437,0.1578665],"study_design_scores_gemma":[0.00002069666,0.00004569759,0.00005384451,0.000003816587,0.000007482556,0.00005652594,0.000004701714,0.9763605,0.01976445,0.002059649,0.00161404,0.000008684222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02584083,0.000146908,0.9681483,0.0001511747,0.00006360579,0.00006368968,0.0001280046,0.004258674,0.001198831],"genre_scores_gemma":[0.3138274,0.0002337385,0.6807033,0.0002258994,0.00004659369,0.0002243224,0.0006098965,0.001127185,0.003001659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00234917,"threshold_uncertainty_score":0.007858753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02064549246004135,"score_gpt":0.2280862341571026,"score_spread":0.2074407416970612,"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."}}