{"id":"W2767448403","doi":"10.1042/ebc20170051","title":"The SGC beyond structural genomics: redefining the role of 3D structures by coupling genomic stratification with fragment-based discovery","year":2017,"lang":"en","type":"review","venue":"Essays in Biochemistry","topic":"Click Chemistry and Applications","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Ontario Ministry of Economic Development and Innovation; Medical Research Council; Ministero dello Sviluppo Economico; International Seafood Sustainability Foundation; Novartis Pharma; Wellcome Trust; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Genome Canada; Fundação de Amparo à Pesquisa do Estado de São Paulo; Diamond Light Source; Pfizer","keywords":"Drug discovery; Genomics; Data science; Computational biology; Structural genomics; Identification (biology); Crowdsourcing; Computer science; Biology; Genome; Bioinformatics; Genetics; Gene; World Wide Web; Protein structure","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02029243,0.001049168,0.001066552,0.001752376,0.001208585,0.004925443,0.003174952,0.002422616,0.005318291],"category_scores_gemma":[0.01906187,0.0006311409,0.001073606,0.002068399,0.01016225,0.007471268,0.008789842,0.006017623,0.002141014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003489099,"about_ca_system_score_gemma":0.007558024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005541663,"about_ca_topic_score_gemma":0.004463087,"domain_scores_codex":[0.9938719,0.002513364,0.0001495602,0.0007341176,0.002270516,0.0004605354],"domain_scores_gemma":[0.9866253,0.004996636,0.0006114287,0.004579169,0.001632977,0.001554467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003913247,0.0001858346,0.002748437,0.0005484082,0.00007034153,0.0002156922,0.001717434,0.01260104,0.03413774,0.6909123,0.03082535,0.2256462],"study_design_scores_gemma":[0.00027082,0.00068185,0.002418964,0.0004188803,0.00005694764,0.0004650488,0.000621945,0.05676781,0.0427344,0.5430336,0.352315,0.0002147451],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0322177,0.003978339,0.907278,0.02958317,0.0009486308,0.000331106,0.0009289869,0.003912175,0.02082188],"genre_scores_gemma":[0.1587818,0.004924737,0.8208238,0.004724408,0.0003849156,0.0005331598,0.001489303,0.001574422,0.006763425],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.02029243,"threshold_uncertainty_score":0.1073179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01959746097161683,"score_gpt":0.2900239664706653,"score_spread":0.2704265054990485,"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."}}