{"id":"W2128564217","doi":"10.1177/1087057105281173","title":"Experimental Screening of Dihydrofolate Reductase Yields a “Test Set” of 50,000 Small Molecules for a Computational Data-Mining and Docking Competition","year":2005,"lang":"en","type":"article","venue":"SLAS DISCOVERY","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"McMaster University","keywords":"Dihydrofolate reductase; Test set; Computer science; Computational biology; Set (abstract data type); High-throughput screening; Docking (animal); Data set; Training set; Small molecule; Escherichia coli; Data mining; Drug discovery; Chemistry; Biology; Machine learning; Biochemistry; Artificial intelligence; Enzyme; Medicine; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002151068,0.001342389,0.001196218,0.0008242304,0.001086791,0.0007151166,0.001840758,0.001413043,0.005520975],"category_scores_gemma":[0.003456622,0.0005077322,0.001247662,0.001413141,0.0008744264,0.0007394187,0.0006563413,0.001096806,0.001808612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007886788,"about_ca_system_score_gemma":0.001377037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006580551,"about_ca_topic_score_gemma":0.01213702,"domain_scores_codex":[0.9986287,0.0004208327,0.00007872742,0.0002750148,0.000445712,0.0001510819],"domain_scores_gemma":[0.9972948,0.001418965,0.000116984,0.000415993,0.0005823318,0.0001708688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01018631,0.02717326,0.05883361,0.002986536,0.002183106,0.002967806,0.000415892,0.3261305,0.3745778,0.00671252,0.05153438,0.1362983],"study_design_scores_gemma":[0.002748917,0.01407263,0.03085448,0.00005308012,0.0009208483,0.001276751,0.0004614996,0.3887968,0.5332785,0.002558857,0.02479542,0.0001822213],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831751,0.000753134,0.005258623,0.0003212799,0.00004768827,0.0001849024,0.004394314,0.000561779,0.005303234],"genre_scores_gemma":[0.9405262,0.0004845568,0.01969679,0.0002785305,0.00002010105,0.0002204247,0.0344339,0.000102105,0.004237287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006580551,"threshold_uncertainty_score":0.01846951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06302125943349392,"score_gpt":0.3321109729729829,"score_spread":0.269089713539489,"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."}}