{"id":"W4387959257","doi":"10.1007/s40747-023-01237-7","title":"Primary sequence based protein–protein interaction binder generation with transformers","year":2023,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; National Research Council Canada; University of Ottawa","funders":"","keywords":"Transformer; Computer science; Protein design; Artificial intelligence; Machine learning; Protein sequencing; Computational biology; Protein structure; Peptide sequence; Engineering; Biology; Voltage","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":[0.0003703059,0.0002410147,0.0003585586,0.0003053835,0.0001866839,0.00007670182,0.0001288853,0.00008821565,0.0003241178],"category_scores_gemma":[0.00002172186,0.0001733107,0.0001222383,0.0005480358,0.0001218493,0.0001262502,0.00002371238,0.0002919194,0.0006888059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000252132,"about_ca_system_score_gemma":0.0002199255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005569484,"about_ca_topic_score_gemma":0.00005478695,"domain_scores_codex":[0.997833,0.000126126,0.000437538,0.0004034141,0.0007637384,0.000436147],"domain_scores_gemma":[0.9991497,0.00004942225,0.00009531257,0.0002537601,0.0002415344,0.0002103129],"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":[0.00107846,0.0001650254,0.001278007,0.0015489,0.0001793234,0.0002184011,0.0002647034,0.00104463,0.9804841,0.0007765568,0.003761675,0.009200245],"study_design_scores_gemma":[0.002223372,0.003175247,0.005547353,0.00299886,0.0001149933,0.0006117902,0.001355958,0.3736772,0.3611995,0.00005614807,0.2479862,0.001053444],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9670798,0.0001798863,0.01985781,0.00216863,0.0003079277,0.003193351,0.00004486624,0.0002777074,0.006890076],"genre_scores_gemma":[0.9870349,0.00002472042,0.0003682172,0.0002082264,0.0004065383,0.0004133469,0.0007781183,0.00004119543,0.01072471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6192846,"threshold_uncertainty_score":0.8853436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2132068556284793,"score_gpt":0.3629646822055819,"score_spread":0.1497578265771025,"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."}}