{"id":"W4317869953","doi":"10.1093/bioinformatics/btad055","title":"Binding peptide generation for MHC Class I proteins with deep reinforcement learning","year":2023,"lang":"en","type":"article","venue":"Bioinformatics","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"National Science Foundation","keywords":"Reinforcement learning; Computational biology; Class (philosophy); Computer science; Peptide; MHC class I; Artificial intelligence; Major histocompatibility complex; Chemistry; Biology; Biochemistry; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003063124,0.0001878137,0.0001461326,0.0001047185,0.0002792106,0.0001051514,0.000156799,0.0001156671,0.000006282022],"category_scores_gemma":[0.00008487181,0.000150584,0.00007814975,0.000196195,0.00002455164,0.0000293918,0.0001000764,0.00008749111,0.00006083121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002511455,"about_ca_system_score_gemma":0.00006710521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002593455,"about_ca_topic_score_gemma":0.000009065636,"domain_scores_codex":[0.9989235,0.000009945677,0.0004098655,0.0001278783,0.0001742164,0.0003545755],"domain_scores_gemma":[0.9993312,0.0000101453,0.0002145673,0.0002710347,0.0001093295,0.00006369597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006295279,0.0001288551,0.002531892,0.002107647,0.0008708158,0.000003231642,0.006869422,0.4478828,0.4509792,0.002814489,0.04500154,0.04018058],"study_design_scores_gemma":[0.001179973,0.0009819478,0.00009527464,0.00003401355,0.00003157749,0.00001320486,0.001977226,0.8788764,0.05225659,0.000007867951,0.06418567,0.0003602312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7335621,0.00006335753,0.2587278,0.0002085471,0.0001974364,0.001807422,0.00001323648,0.000144539,0.005275625],"genre_scores_gemma":[0.9502312,0.0002212574,0.03900662,0.0002366612,0.0004008526,0.0004400567,0.004077171,0.00006443078,0.005321734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4309936,"threshold_uncertainty_score":0.6140639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02448110864336796,"score_gpt":0.2395257250399956,"score_spread":0.2150446163966277,"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."}}