{"id":"W2270302232","doi":"10.1021/acs.jcim.5b00278","title":"Evaluation of the Wilma-SIE Virtual Screening Method in Community Structure–Activity Resource 2013 and 2014 Blind Challenges","year":2015,"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":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Virtual screening; Docking (animal); Computer science; Computational biology; Artificial intelligence; Chemistry; Machine learning; Data mining; Biology; Stereochemistry; Pharmacophore; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.007497802,0.00007306316,0.0001610434,0.0001314378,0.00004385874,0.00006060551,0.0002552683,0.00005843742,5.924541e-7],"category_scores_gemma":[0.0007428571,0.00005361108,0.00003323791,0.0001152595,0.00003129637,0.00167686,0.0002281307,0.0003944113,7.683695e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005463963,"about_ca_system_score_gemma":0.0001500989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004270975,"about_ca_topic_score_gemma":0.00000519979,"domain_scores_codex":[0.9979746,0.0007534771,0.0004317601,0.00005072234,0.0007127498,0.00007665013],"domain_scores_gemma":[0.9985028,0.0003257313,0.0003871558,0.0001364727,0.0005765132,0.00007136558],"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":[0.00004743465,0.00001563987,0.00001560151,0.00001294062,0.00001165421,4.146401e-8,0.004901988,0.7903349,0.0008876399,0.0007115504,0.00003170653,0.2030288],"study_design_scores_gemma":[0.001009625,0.0000321409,0.0003054668,0.00005858166,0.00001592015,0.0000331487,0.0006127118,0.9837577,0.002456324,0.01162107,0.00004298939,0.00005431141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.539765,0.000212517,0.459261,0.0006114434,0.00003323545,0.00004707267,6.399065e-7,0.000002170618,0.00006697979],"genre_scores_gemma":[0.9422247,0.00002704818,0.05764332,0.00008033373,0.00002126633,6.165648e-7,6.910307e-7,0.000001792321,2.482085e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4024597,"threshold_uncertainty_score":0.2598604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1553333044916533,"score_gpt":0.375606206298661,"score_spread":0.2202729018070077,"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."}}