{"id":"W4308583764","doi":"10.1007/978-1-0716-2609-2_20","title":"Optimizing Antibody–Antigen Binding Affinities with the ADAPT Platform","year":2022,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Mutant; Binding affinities; Affinities; Affinity maturation; Robustness (evolution); Mutagenesis; Computational biology; Protein engineering; Antibody; Chemistry; Computer science; Biology; Genetics; Biochemistry; Receptor; Gene; Enzyme","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":[0.001249735,0.001084441,0.0006890285,0.0005485205,0.0004699185,0.001145236,0.0009876292,0.0008956732,0.005613769],"category_scores_gemma":[0.001506446,0.0007609737,0.0004771884,0.0004058513,0.0003280062,0.0007785711,0.001183259,0.001491352,0.002723885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006024155,"about_ca_system_score_gemma":0.0004853461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004632894,"about_ca_topic_score_gemma":0.001236549,"domain_scores_codex":[0.9989491,0.0001599411,0.00004916481,0.0001741356,0.0005046686,0.0001629963],"domain_scores_gemma":[0.9995185,0.0001983638,0.00004987966,0.00005928448,0.0001199656,0.00005394024],"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":[0.0001714785,0.0001436603,0.0003876343,0.0001153102,0.00003926787,0.00005377652,0.00004659996,0.001998491,0.9810919,0.0009908978,0.0007784487,0.01418254],"study_design_scores_gemma":[0.00002242892,0.000195949,0.0006243319,0.000009124661,0.0000302527,0.000117331,0.00002750017,0.008227215,0.9824449,0.0002987426,0.007973687,0.00002845051],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7803214,0.002244145,0.1993312,0.0004935567,0.0003474417,0.0004466517,0.00048714,0.002682488,0.013646],"genre_scores_gemma":[0.8104712,0.001577554,0.1675847,0.0008036618,0.00008206732,0.0005731564,0.0009366427,0.000726813,0.01724424],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005613769,"threshold_uncertainty_score":0.01877993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06338003356308367,"score_gpt":0.4336469648954304,"score_spread":0.3702669313323467,"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."}}