{"id":"W2165531467","doi":"10.1002/prot.24428","title":"Docking, scoring, and affinity prediction in CAPRI","year":2013,"lang":"en","type":"article","venue":"Proteins Structure Function and Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":249,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Canada Research Chairs; Hospital for Sick Children; University of Toronto","funders":"Agence Nationale de la Recherche","keywords":"Docking (animal); Computer science; Artificial intelligence; Computational biology; 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":[0.03636152,0.004898326,0.002923714,0.004333925,0.002288609,0.003652553,0.00660814,0.003335418,0.004747727],"category_scores_gemma":[0.03720323,0.001073812,0.001587051,0.003822215,0.001597171,0.002773353,0.00634913,0.003465715,0.002811166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003154808,"about_ca_system_score_gemma":0.003142802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01444196,"about_ca_topic_score_gemma":0.01334915,"domain_scores_codex":[0.9632831,0.01889701,0.001560231,0.002996719,0.01128148,0.001981493],"domain_scores_gemma":[0.9680277,0.01511226,0.001425991,0.00562117,0.00829778,0.001515137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007432668,0.003817461,0.0394757,0.002680029,0.001766838,0.0006917485,0.001150021,0.3389241,0.03025606,0.01022231,0.07322519,0.4903579],"study_design_scores_gemma":[0.0006980529,0.002017998,0.01161612,0.0001351922,0.0001582347,0.0005377404,0.0004723209,0.935212,0.02581974,0.004985218,0.01807791,0.000269631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.582352,0.009613418,0.2972987,0.002525018,0.001069168,0.003446257,0.007097053,0.05363904,0.04295933],"genre_scores_gemma":[0.6556678,0.001092184,0.3158965,0.0008708078,0.0001567809,0.001118794,0.0185433,0.00205047,0.004603324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03636152,"threshold_uncertainty_score":0.1923004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004166134472791118,"score_gpt":0.1880848308742308,"score_spread":0.1839186964014396,"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."}}