{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003599876,0.0007506706,0.001091519,0.001162735,0.001030705,0.001188227,0.00242278,0.001341151,0.007742294],"category_scores_gemma":[0.02658394,0.0003941929,0.0004688134,0.002005624,0.0006784885,0.002148007,0.001371327,0.003295613,0.003833973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001720888,"about_ca_system_score_gemma":0.0007361051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009080232,"about_ca_topic_score_gemma":0.002091858,"domain_scores_codex":[0.9952621,0.001995089,0.0002770563,0.000295749,0.001945515,0.000224394],"domain_scores_gemma":[0.989971,0.006038859,0.0002776709,0.001949824,0.001432405,0.000330228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001173645,0.0002931889,0.001397545,0.0006300472,0.00008856854,0.0005426148,0.00009247586,0.1029396,0.01106223,0.1406052,0.5136006,0.2275743],"study_design_scores_gemma":[0.0004647976,0.0006115485,0.00104141,0.0002582494,0.00003777519,0.001133608,0.00006756173,0.5697289,0.0246559,0.1731133,0.2287758,0.0001110715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09509341,0.01544316,0.7156565,0.0436281,0.009958838,0.001026067,0.01531366,0.01823249,0.08564784],"genre_scores_gemma":[0.4823011,0.006779753,0.436671,0.01617797,0.003065903,0.004050455,0.0288814,0.004305425,0.01776698],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007742294,"threshold_uncertainty_score":0.02590054,"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."}}