{"id":"W4386713035","doi":"10.1038/s41598-023-42090-5","title":"Enhanced antibody-antigen structure prediction from molecular docking using AlphaFold2","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; National Research Council Canada","funders":"Alliance de recherche numérique du Canada; Compute Canada","keywords":"Docking (animal); Decoy; False positive paradox; Computer science; Computational biology; Antigen; Machine learning; Artificial intelligence; Bioinformatics; Biology; Medicine; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002239732,0.001521309,0.001572481,0.001583906,0.0005657317,0.001178423,0.001140484,0.0009160889,0.003321763],"category_scores_gemma":[0.003599137,0.0003922418,0.001204886,0.0009477283,0.0003373985,0.001278502,0.001482012,0.0009072371,0.000730832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006393654,"about_ca_system_score_gemma":0.001271904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003141126,"about_ca_topic_score_gemma":0.003714188,"domain_scores_codex":[0.9993167,0.0001921054,0.00004559028,0.0001032203,0.0002594127,0.00008285091],"domain_scores_gemma":[0.9988342,0.0004103206,0.0001215829,0.0002101941,0.0003063614,0.0001173103],"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":[0.001694275,0.0006662692,0.04393666,0.0006860714,0.0006232407,0.0005886406,0.0002226283,0.6595218,0.07225057,0.006992667,0.01080708,0.2020101],"study_design_scores_gemma":[0.00004197974,0.0001720683,0.002069761,0.00001100802,0.0000292641,0.0001036798,0.00001827689,0.9839999,0.01124027,0.001357757,0.0009295737,0.00002643111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.547954,0.0007061515,0.4305107,0.000306459,0.0001177399,0.0001981552,0.001572842,0.01287014,0.005763886],"genre_scores_gemma":[0.8003908,0.0003339711,0.1919769,0.0001430007,0.00002845348,0.0001781778,0.004356384,0.0006308806,0.001961425],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003321763,"threshold_uncertainty_score":0.01184499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02976933467163302,"score_gpt":0.3453607176295351,"score_spread":0.3155913829579021,"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."}}