{"id":"W4312213284","doi":"10.1101/2022.12.26.521961","title":"Enhanced antibody-antigen structure prediction from molecular docking using AlphaFold2","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Alliance de recherche numérique du Canada; Compute Canada","keywords":"Docking (animal); Decoy; False positive paradox; Computer science; Computational biology; True positive rate; Artificial intelligence; Machine learning; Biology; Biochemistry; Medicine","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":[0.002418415,0.001260858,0.001261283,0.001683289,0.0004117884,0.001031153,0.0008478525,0.0007119848,0.002305261],"category_scores_gemma":[0.003282924,0.0003002944,0.0008695708,0.0008433103,0.0002810731,0.001057387,0.001212882,0.0006848373,0.0004825106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007499473,"about_ca_system_score_gemma":0.0009043507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003960024,"about_ca_topic_score_gemma":0.002762616,"domain_scores_codex":[0.9993523,0.0001825355,0.00004442861,0.0001091112,0.0002425092,0.00006911491],"domain_scores_gemma":[0.9983783,0.0005458929,0.0001749685,0.0002607008,0.0004877449,0.0001524547],"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":[0.001925766,0.000554585,0.04807267,0.000434854,0.0005138718,0.0003638948,0.0001412711,0.6957207,0.06714258,0.003231883,0.00827912,0.1736189],"study_design_scores_gemma":[0.00001972667,0.0001043371,0.002094231,0.000005746545,0.00001395039,0.00004326084,0.000009337879,0.9883506,0.00841055,0.0005317109,0.0003979514,0.00001849163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7289015,0.0006689464,0.2514431,0.00030959,0.0001195329,0.0001340127,0.001506128,0.01301709,0.00390007],"genre_scores_gemma":[0.9147538,0.0001440963,0.08137365,0.00007659953,0.000018535,0.00007142217,0.00236277,0.000237893,0.0009612793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003960024,"threshold_uncertainty_score":0.01278996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02006722221520141,"score_gpt":0.2886777463070007,"score_spread":0.2686105240917993,"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."}}