{"id":"W3019239233","doi":"10.1096/fasebj.2020.34.s1.02411","title":"Assessing and improving the performance of consensus docking strategies using the DockBox package","year":2020,"lang":"en","type":"article","venue":"The FASEB Journal","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Docking (animal); Virtual screening; Computer science; Protein–ligand docking; Machine learning; Drug discovery; Artificial intelligence; Bioinformatics; 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":[0.004469284,0.001322495,0.001388088,0.001520251,0.0005316191,0.001103242,0.002208208,0.0008284418,0.006751542],"category_scores_gemma":[0.01158083,0.0004456755,0.0007095281,0.001124545,0.0002885718,0.001237905,0.001652656,0.001230604,0.001696591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006715565,"about_ca_system_score_gemma":0.001270238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004769241,"about_ca_topic_score_gemma":0.002918553,"domain_scores_codex":[0.9978883,0.0007518409,0.0001404024,0.0002160894,0.0008211955,0.0001822132],"domain_scores_gemma":[0.9956077,0.002619698,0.0001995385,0.0004935738,0.0009418822,0.0001376044],"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":[0.001176561,0.0005327839,0.007094107,0.0004907739,0.0002504862,0.0001988238,0.0001306186,0.699586,0.01757077,0.009795232,0.0225035,0.2406704],"study_design_scores_gemma":[0.00006781472,0.0001460201,0.0008682117,0.00001470452,0.00002140415,0.00004124291,0.0000231873,0.9841424,0.01097744,0.001648141,0.002007107,0.00004233845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.185899,0.0006982511,0.7692056,0.0004171687,0.0001385529,0.0003913331,0.002780102,0.0316249,0.008845099],"genre_scores_gemma":[0.4546355,0.0005164935,0.5357555,0.0001079561,0.00002301624,0.0005275479,0.003791882,0.002886703,0.001755367],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006751542,"threshold_uncertainty_score":0.02363616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06569433269866362,"score_gpt":0.3245146706805098,"score_spread":0.2588203379818462,"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."}}