{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006643127,0.00009445935,0.0001519214,0.0001946355,0.00005325591,0.0001663118,0.0002668302,0.00005078565,0.000003524166],"category_scores_gemma":[0.0001031803,0.00008596301,0.0000695131,0.000182246,0.00001472605,0.003286648,0.0001294625,0.0001712788,0.000001009351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004442618,"about_ca_system_score_gemma":0.0001073708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009240296,"about_ca_topic_score_gemma":4.493205e-8,"domain_scores_codex":[0.9988177,0.00003775047,0.0006434132,0.00006684591,0.0002981997,0.0001360487],"domain_scores_gemma":[0.9990582,0.00003713099,0.0003410272,0.00008418054,0.0003811531,0.000098306],"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":[0.00003270691,0.00001750822,0.0000223542,0.00002144703,0.0000325786,0.000007727814,0.003675585,0.9162467,0.04444133,0.01270626,0.000006992317,0.02278879],"study_design_scores_gemma":[0.0003172847,0.00001479327,0.00000358311,0.00004852947,0.000009981648,0.000197413,0.00005070123,0.9475692,0.04712153,0.004536081,0.00003285195,0.00009807424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.472908,0.00005686853,0.5267264,0.00002436196,0.00006912234,0.00001859739,1.506198e-7,0.000009701074,0.0001867554],"genre_scores_gemma":[0.6933801,0.00000923992,0.3062128,0.000369551,0.0000244679,2.594809e-7,6.876174e-7,0.000002774749,1.282066e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2205136,"threshold_uncertainty_score":0.350547,"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."}}