{"id":"W2040202888","doi":"10.1021/ci200175h","title":"Normalizing Molecular Docking Rankings using Virtually Generated Decoys","year":2011,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium; University of Toronto","funders":"","keywords":"Decoy; Docking (animal); Virtual screening; Computer science; Artificial intelligence; Data mining; Machine learning; Drug discovery; Bioinformatics; Chemistry; 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":[0.00461765,0.001290394,0.001468431,0.002734605,0.0007170304,0.001837372,0.001628158,0.0005676738,0.002224424],"category_scores_gemma":[0.01835668,0.0004862496,0.001173293,0.0023859,0.001065801,0.001816139,0.001433584,0.001057833,0.001205045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001978318,"about_ca_system_score_gemma":0.001809924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003778336,"about_ca_topic_score_gemma":0.00488513,"domain_scores_codex":[0.9946741,0.001110314,0.0003943889,0.0008613461,0.002669518,0.0002904016],"domain_scores_gemma":[0.9919917,0.002012157,0.0008205015,0.002723381,0.002297944,0.0001544377],"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":[0.0004665914,0.0002608222,0.01735122,0.0003329797,0.0003541677,0.0001957487,0.0001664459,0.5726945,0.04787139,0.02137082,0.006204702,0.3327306],"study_design_scores_gemma":[0.00006509346,0.0005138483,0.01079647,0.00004254513,0.0001166417,0.0002764303,0.0001063616,0.8934209,0.07414386,0.01258401,0.007803769,0.0001300656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2214661,0.0006574062,0.7612513,0.0002422659,0.0001805265,0.0003811779,0.002167153,0.008086816,0.00556729],"genre_scores_gemma":[0.6214068,0.000790131,0.3596306,0.000243979,0.0000685874,0.000632452,0.01161374,0.001754914,0.003858711],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00461765,"threshold_uncertainty_score":0.02442074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06290050966544028,"score_gpt":0.2942137047721308,"score_spread":0.2313131951066905,"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."}}