{"id":"W3214668463","doi":"10.1002/prot.26280","title":"Functional antibody characterization via direct structural analysis and information‐driven protein–protein docking","year":2021,"lang":"en","type":"article","venue":"Proteins Structure Function and Bioinformatics","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis","funders":"","keywords":"In silico; Computational biology; Docking (animal); Antibody; Mutagenesis; In vitro; Chemistry; Vascular endothelial growth factor; Kinase insert domain receptor; Cell biology; VEGF receptors; Biology; Mutant; Vascular endothelial growth factor A; Cancer research; Biochemistry; Immunology; Medicine","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":[0.0003525186,0.0008064251,0.0007224121,0.000369608,0.0004519915,0.0004407737,0.0008644345,0.0006130832,0.002721608],"category_scores_gemma":[0.000645385,0.0003329193,0.0006574935,0.0003426519,0.0003115786,0.0004908431,0.0002724311,0.0005507872,0.0004444946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001116944,"about_ca_system_score_gemma":0.001425287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008251599,"about_ca_topic_score_gemma":0.006761439,"domain_scores_codex":[0.9998789,0.00002243928,0.000005715326,0.00001570662,0.00004854841,0.00002875759],"domain_scores_gemma":[0.9997938,0.00007798551,0.00002396822,0.00002339034,0.00006485957,0.00001596568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009205707,0.0001197634,0.001253732,0.00009673644,0.00002596668,0.0001278737,0.0000464905,0.9674145,0.01938519,0.004644382,0.0005299583,0.006263431],"study_design_scores_gemma":[0.00001950418,0.0000347723,0.0002171293,0.000002974946,0.000006010067,0.00001758366,0.00001158541,0.9935674,0.004894421,0.0007441244,0.0004789382,0.000005556114],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.748781,0.0004446675,0.2290315,0.0005141044,0.00004861363,0.0003095676,0.001739707,0.001156466,0.01797434],"genre_scores_gemma":[0.9511108,0.0003442189,0.04571323,0.00007166212,0.00000713697,0.0002111331,0.001029672,0.00013796,0.001374192],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.008251599,"threshold_uncertainty_score":0.01640713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009092878569124028,"score_gpt":0.2478879446607479,"score_spread":0.2387950660916239,"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."}}