{"id":"W2088388996","doi":"10.1021/ci100374f","title":"Virtual Decoy Sets for Molecular Docking Benchmarks","year":2011,"lang":"en","type":"letter","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Decoy; Virtual screening; Computer science; Benchmark (surveying); Docking (animal); Directory; Artificial intelligence; Drug discovery; Chemistry; Bioinformatics; Biology; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006323616,0.0001818693,0.0003095734,0.0002745151,0.000043676,0.0002583443,0.0005237977,0.0002974084,0.000005478086],"category_scores_gemma":[0.0002089333,0.0001629491,0.0002005346,0.00008947207,0.00001919464,0.002038185,0.0001643492,0.0007835645,0.000001224494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006282482,"about_ca_system_score_gemma":0.0001968404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001728004,"about_ca_topic_score_gemma":1.914621e-8,"domain_scores_codex":[0.9983168,0.00004076532,0.0008927399,0.0001155304,0.0004304867,0.0002037116],"domain_scores_gemma":[0.9983582,0.0002336251,0.0006561961,0.0001483669,0.0005199944,0.00008355711],"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":[0.0001004947,0.0000382683,0.000001515446,0.0004840214,0.0002784801,0.00004737158,0.003613709,0.5631731,0.0007116554,0.01248247,0.2439887,0.1750801],"study_design_scores_gemma":[0.0003594499,0.00004422475,1.488151e-7,0.0001220486,0.00002509371,0.0001568425,0.00001446562,0.9502882,0.001173373,0.01768173,0.02994765,0.0001867443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009279004,0.0001065302,0.9638929,0.0258896,0.0004038698,0.0001087896,0.000005823891,0.00001267359,0.0003008423],"genre_scores_gemma":[0.07998797,0.00007146872,0.5739408,0.3445343,0.001218761,0.00001470694,0.0001973261,0.00002816766,0.000006474519],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.389952,"threshold_uncertainty_score":0.6644872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03779593393573778,"score_gpt":0.3067115313897497,"score_spread":0.2689155974540119,"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."}}