{"id":"W2032729355","doi":"10.1002/minf.201000018","title":"Finding Inspiration in the Protein Data Bank to Chemically Antagonize Readers of the Histone Code","year":2010,"lang":"en","type":"article","venue":"Molecular Informatics","topic":"Click Chemistry and Applications","field":"Chemistry","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of New Brunswick; Structural Genomics Consortium; University of Toronto","funders":"Knut och Alice Wallenbergs Stiftelse; Natural Sciences and Engineering Research Council of Canada; Karolinska Institutet; Stiftelsen för Strategisk Forskning; Canadian Institutes of Health Research; Genome Canada; Ontario Genomics; Wellcome Trust; Ontario Genomics Institute; Ontario Innovation Trust","keywords":"Protein Data Bank (RCSB PDB); Protein Data Bank; Pharmacophore; Histone; Epigenetics; Chemistry; Cheminformatics; Computational biology; Affinities; Stereochemistry; Acetylation; Drug discovery; Biology; Biochemistry; Protein structure; Bioinformatics; DNA; Gene","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":[0.0002361171,0.00009659898,0.00009540751,0.00001652797,0.00007145051,0.0000312285,0.001142963,0.0001013729,0.00004510714],"category_scores_gemma":[0.0003529051,0.00006807483,0.00003327316,0.000232577,0.00007660242,0.00009546138,0.0002334562,0.0003714944,0.000007830799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002315223,"about_ca_system_score_gemma":0.00005866213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000015419,"about_ca_topic_score_gemma":0.00002702762,"domain_scores_codex":[0.9991056,0.000006360512,0.0004103096,0.00009324535,0.0002458966,0.0001385588],"domain_scores_gemma":[0.9983881,0.000037663,0.000171637,0.001333376,0.00003753704,0.00003176264],"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":[0.000003886539,0.00003828963,0.00008279562,0.0001014528,0.000004930578,4.791158e-7,0.001687096,0.00003667155,0.9965779,0.0004535874,0.0004559531,0.0005570201],"study_design_scores_gemma":[0.0001710063,0.000002642433,0.00006982914,0.00004503629,0.0000113751,0.000007096577,0.0006141944,0.001369995,0.9803843,0.0001075647,0.01712633,0.00009055837],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890746,0.000004576249,0.0008528043,0.001322595,0.00001428864,0.0002224665,0.00008401105,0.00001581443,0.008408838],"genre_scores_gemma":[0.9958594,8.044948e-7,0.003276838,0.000513701,0.00002348453,0.00005721705,0.0001693348,0.000008183861,0.00009099636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01667038,"threshold_uncertainty_score":0.2776011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02806135310030598,"score_gpt":0.2972882026075814,"score_spread":0.2692268495072754,"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."}}