{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005727286,0.0009497667,0.001095739,0.001102976,0.0004084068,0.000791363,0.0007057021,0.0006764902,0.01281598],"category_scores_gemma":[0.001249974,0.0003369377,0.000513799,0.002804019,0.0001879355,0.0005639733,0.0003590462,0.0006903777,0.008666652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007557051,"about_ca_system_score_gemma":0.001753515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002106606,"about_ca_topic_score_gemma":0.005199471,"domain_scores_codex":[0.9997646,0.00003361398,0.00002749743,0.00005320277,0.00008974232,0.00003124949],"domain_scores_gemma":[0.9997541,0.00007031881,0.00004870451,0.00004264835,0.000047749,0.00003632725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006163231,0.001223846,0.03830825,0.006782017,0.0006281367,0.002362407,0.0003384684,0.01172291,0.3348769,0.01300125,0.3376536,0.2469389],"study_design_scores_gemma":[0.001855735,0.001248749,0.03967021,0.0003940795,0.0007725164,0.001919489,0.0002624218,0.06488527,0.2177845,0.006281041,0.6647317,0.0001943204],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2644494,0.0102604,0.06125705,0.003278062,0.0004272068,0.001167321,0.5830409,0.03963303,0.03648664],"genre_scores_gemma":[0.2062472,0.006679134,0.1344179,0.0007424629,0.00004027956,0.0006644081,0.6393319,0.00102289,0.0108538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01281598,"threshold_uncertainty_score":0.04287374,"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."}}