{"id":"W4389395132","doi":"10.21203/rs.3.rs-3560097/v1","title":"Predicting Monoclonal Antibody Binding Sequences from a Sparse Sampling of All Possible Sequences","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre for Movement Disorders","funders":"Arizona State University","keywords":"Monoclonal antibody; Sequence (biology); Computational biology; Epitope; Sampling (signal processing); Computer science; Binding site; Artificial intelligence; Chemistry; Algorithm; Biology; Antibody; Genetics; Biochemistry","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":[0.001002235,0.001024988,0.001438157,0.001661714,0.0003536528,0.000810852,0.001170317,0.001717004,0.002117559],"category_scores_gemma":[0.005329574,0.000850866,0.001126012,0.00121714,0.0006247432,0.001172427,0.0005859807,0.001606465,0.0008837669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003784555,"about_ca_system_score_gemma":0.0007049502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002502549,"about_ca_topic_score_gemma":0.003592196,"domain_scores_codex":[0.9994696,0.0001801115,0.00002617965,0.0001408031,0.0001173284,0.00006593199],"domain_scores_gemma":[0.9960117,0.003172753,0.0002201935,0.0002293853,0.0002385867,0.0001273047],"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":[0.002536555,0.0007429105,0.02093265,0.0008914157,0.0003802639,0.001133889,0.0002291243,0.613623,0.09586322,0.01186716,0.0157576,0.2360421],"study_design_scores_gemma":[0.00005487274,0.0001075782,0.001112378,0.00001428929,0.00005095355,0.0001801811,0.00002708648,0.9841883,0.003717932,0.009824373,0.0007117387,0.00001023392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3707793,0.002512249,0.6185865,0.001442356,0.0001335577,0.00009655776,0.003010282,0.001646764,0.001792424],"genre_scores_gemma":[0.8460588,0.00143072,0.1418098,0.0005346755,0.0003537242,0.0001552914,0.007337087,0.0001290331,0.002190795],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002502549,"threshold_uncertainty_score":0.007083893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2912694336747164,"score_gpt":0.4917020311591714,"score_spread":0.200432597484455,"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."}}