{"id":"W4408884143","doi":"10.1093/bioinformatics/btaf129","title":"AI-augmented physics-based docking for antibody-antigen complex prediction","year":2025,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Ste. Anne's Hospital; National Research Council Canada","funders":"","keywords":"Docking (animal); Computer science; Computational biology; Macromolecular docking; Epitope; Artificial intelligence; Antigen; Biology; Protein structure","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":[0.0007414306,0.000997351,0.0009938319,0.000632101,0.0005739516,0.0009436529,0.001286196,0.0009908358,0.003586456],"category_scores_gemma":[0.001896981,0.000368472,0.0008721664,0.0006781006,0.0005665689,0.0009014897,0.00132599,0.001505859,0.0007961713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008489196,"about_ca_system_score_gemma":0.001325963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008545191,"about_ca_topic_score_gemma":0.005829033,"domain_scores_codex":[0.9996412,0.0001094842,0.00001324473,0.00004987327,0.0001441537,0.00004208475],"domain_scores_gemma":[0.999302,0.0003400548,0.00005918567,0.00009037846,0.0001298413,0.00007847238],"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":[0.00007520642,0.00004706768,0.0007987169,0.00005261778,0.00004905171,0.00004917241,0.00002316318,0.9846998,0.001968318,0.002141193,0.000902499,0.009193146],"study_design_scores_gemma":[0.000007671811,0.000012003,0.0001040151,0.000002681181,0.000003622784,0.000008887862,0.000003443184,0.997538,0.0006338638,0.001442256,0.0002381928,0.000005383476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2841001,0.001471273,0.6874452,0.001282864,0.0002257875,0.0001482749,0.0008006386,0.00662838,0.0178975],"genre_scores_gemma":[0.8622877,0.0005220418,0.1326967,0.0003349516,0.00005669951,0.0001333257,0.001171548,0.0003447417,0.002452273],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008545191,"threshold_uncertainty_score":0.0169909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0501294460679583,"score_gpt":0.3828606144434619,"score_spread":0.3327311683755035,"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."}}