{"id":"W2090953687","doi":"10.1021/ci900219u","title":"Evaluation of Virtual Screening as a Tool for Chemical Genetic Applications","year":2009,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium; University of Toronto","funders":"Wellcome Trust","keywords":"Virtual screening; In silico; Computational biology; Biology; Gene; Function (biology); Acetylation; Genetics; Bioinformatics; Drug discovery","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":[0.0005489115,0.00006532411,0.0001025966,0.00005476621,0.00002328126,0.0000198402,0.00007274335,0.00008993372,0.000003904287],"category_scores_gemma":[0.0003048262,0.00005961192,0.00007434691,0.0000437036,0.00001795598,0.00003991515,0.00001455971,0.00006151252,4.110724e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001333974,"about_ca_system_score_gemma":0.00009841842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.384068e-7,"about_ca_topic_score_gemma":2.483997e-8,"domain_scores_codex":[0.9990783,0.00001352756,0.0004972604,0.00005449596,0.0002822846,0.00007406834],"domain_scores_gemma":[0.9988268,0.000007979304,0.0002567068,0.00007207951,0.0007835845,0.00005283929],"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":[0.0001141477,0.00002724464,0.000005377148,0.00001061544,0.00002170508,1.636906e-8,0.00007068717,0.005443253,0.791261,0.0002690997,0.0001697715,0.2026071],"study_design_scores_gemma":[0.001480459,0.0002116218,0.000008262709,0.00003283972,0.00007278191,0.00003309618,0.0001045893,0.240884,0.7535204,0.001330853,0.002214738,0.0001063106],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6302447,0.0001701008,0.3691883,0.0001101874,0.00001446384,0.00016404,0.000002805131,0.000002008362,0.0001033333],"genre_scores_gemma":[0.9841307,0.00004567544,0.01535426,0.0002706534,0.000142503,0.00001203271,0.00003836467,0.000002926882,0.000002852635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.353886,"threshold_uncertainty_score":0.2430904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02470413877491048,"score_gpt":0.3010721495933626,"score_spread":0.2763680108184521,"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."}}