{"id":"W2072682248","doi":"10.1016/j.drudis.2006.03.009","title":"Consensus scoring for protein–ligand interactions","year":2006,"lang":"en","type":"review","venue":"Drug Discovery Today","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":234,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; Ontario Institute for Cancer Research","funders":"","keywords":"Virtual screening; Computer science; Task (project management); Machine learning; Artificial intelligence; Computational biology; Data mining; Drug discovery; Bioinformatics; Biology; Engineering","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008573459,0.0007828609,0.001646981,0.0005246149,0.0003297218,0.001169701,0.001681757,0.0001639049,0.000005125352],"category_scores_gemma":[0.0002781536,0.0006974056,0.001174303,0.0008312366,0.0001053084,0.001126522,0.0007021017,0.0005532252,0.0000879305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003976004,"about_ca_system_score_gemma":0.001169975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006761037,"about_ca_topic_score_gemma":0.00003753116,"domain_scores_codex":[0.9957957,0.0004959404,0.001172968,0.001349122,0.0005150758,0.0006712467],"domain_scores_gemma":[0.9957064,0.002000232,0.0007697555,0.001223511,0.0001606048,0.0001395432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008879938,0.0001940447,9.520053e-7,0.007333471,0.0002215607,0.00003491763,0.00008890195,0.001686754,0.000005522437,0.07650197,0.01171736,0.9022056],"study_design_scores_gemma":[0.0002408738,0.00002539276,0.000002002863,0.006235968,0.0001898961,0.00007409434,0.000007762069,0.003189306,0.00005163178,0.008169314,0.9810097,0.000804072],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00003014604,0.7085521,0.2842683,0.0002058332,0.0020129,0.002340421,0.0002928884,0.0002295935,0.002067773],"genre_scores_gemma":[0.0001356609,0.7887393,0.1689944,0.000201499,0.002350469,0.003839992,0.0008650193,0.0003424965,0.03453113],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9692923,"threshold_uncertainty_score":0.9998672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05769589549382061,"score_gpt":0.3672049981125308,"score_spread":0.3095091026187102,"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."}}