{"id":"W4404171705","doi":"10.1101/2024.11.06.622293","title":"AI-Augmented Physics-Based Docking for Antibody-Antigen Complex Prediction","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Docking (animal); Computer science; Computational biology; Physics; Biology; 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":[0.0008365585,0.0009908878,0.0009020341,0.0006199232,0.0005083995,0.0008836674,0.00113387,0.0008460174,0.003545154],"category_scores_gemma":[0.001769453,0.0003209047,0.0009547012,0.0005978594,0.0003952913,0.0007863418,0.001153584,0.001406145,0.0006670812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006910484,"about_ca_system_score_gemma":0.001199456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007785802,"about_ca_topic_score_gemma":0.00590901,"domain_scores_codex":[0.999645,0.0001140478,0.00001444598,0.00004590897,0.0001360932,0.00004452791],"domain_scores_gemma":[0.9992579,0.0003410261,0.0000540369,0.0001158557,0.0001486731,0.00008253634],"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":[0.00008706308,0.0000693913,0.00122561,0.00005347951,0.00006363611,0.00004928667,0.00002090656,0.9834261,0.002910837,0.001762032,0.0009826699,0.009349043],"study_design_scores_gemma":[0.000006006612,0.00001457326,0.0001291315,0.000001995605,0.000003027885,0.000006941004,0.000003236368,0.9981603,0.0007697473,0.0007169272,0.0001834231,0.000004726479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4072313,0.0009109601,0.5656006,0.00100137,0.0002217916,0.0001393423,0.0009404783,0.007752634,0.01620147],"genre_scores_gemma":[0.8978517,0.0002377696,0.09884294,0.0002270329,0.00003275251,0.00008704234,0.0009160553,0.0002740844,0.001530516],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007785802,"threshold_uncertainty_score":0.01548094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04156718800715047,"score_gpt":0.3227643981081819,"score_spread":0.2811972101010314,"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."}}