{"id":"W3004422815","doi":"10.1101/2020.02.06.937730","title":"The Druggable Genome as Seen from the Protein Data Bank","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium; University of Toronto","funders":"Novartis Pharma; Fundação de Amparo à Pesquisa do Estado de São Paulo; Ontario Genomics Institute; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Genome Canada; Ontario Genomics; Natural Sciences and Engineering Research Council of Canada; Pfizer","keywords":"Druggability; Human proteome project; Genome; Drug discovery; Computational biology; Human genome; Proteome; Small molecule; Protein Data Bank; Biology; Protein Data Bank (RCSB PDB); Gene; Genetics; Bioinformatics; Protein structure; Proteomics; Biochemistry","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":[0.0004042673,0.0004277723,0.0003935244,0.003250373,0.0002820048,0.001165119,0.000232705,0.0004339646,0.004650027],"category_scores_gemma":[0.001363911,0.0001369081,0.0002522086,0.007834207,0.0001926663,0.0005884551,0.000355907,0.0003873727,0.002596854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005635848,"about_ca_system_score_gemma":0.0009901433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003522126,"about_ca_topic_score_gemma":0.002451994,"domain_scores_codex":[0.9997025,0.00006006168,0.0000283698,0.00007849104,0.000101346,0.00002926659],"domain_scores_gemma":[0.9994689,0.0001847496,0.0001231534,0.00005138243,0.0001153649,0.00005648749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002327436,0.0001657177,0.1159743,0.00667703,0.0005628819,0.003037958,0.001504322,0.009272715,0.172483,0.0358809,0.1711419,0.4809718],"study_design_scores_gemma":[0.00006681227,0.000165052,0.1527356,0.0008318518,0.000294895,0.002092243,0.00072668,0.00432024,0.02144858,0.01064886,0.8066035,0.00006576728],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3430353,0.06098407,0.01442175,0.007878323,0.0001455323,0.0001283278,0.5261576,0.003587597,0.04366145],"genre_scores_gemma":[0.3357314,0.03042113,0.04619934,0.001198762,0.00007998276,0.00009208971,0.5798678,0.0005541045,0.005855417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004650027,"threshold_uncertainty_score":0.01555592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01926651892534853,"score_gpt":0.2274216732159796,"score_spread":0.2081551542906311,"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."}}