{"id":"W4206161097","doi":"10.1101/2022.01.13.475779","title":"Structure-Based Survey of the Human Proteome for Opportunities in Proximity Pharmacology","year":2022,"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":"Ontario Genomics Institute; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Ontario Genomics; Genome Canada; McGill University; Genentech; Bayer; Natural Sciences and Engineering Research Council of Canada; Pfizer; Bristol-Myers Squibb","keywords":"Drug discovery; Computational biology; Small molecule; Proteome; Ubiquitin ligase; Human proteome project; Ubiquitin; Biochemistry; Biology; DNA ligase; Acetyltransferases; Chemical biology; Target protein; Kinase; Enzyme; Chemistry; Proteomics; Acetylation; 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":[0.0001977527,0.0002998907,0.0002869263,0.0005616741,0.0001775707,0.0002516746,0.0001112442,0.0001868226,0.0009879138],"category_scores_gemma":[0.00015262,0.00008915997,0.0002888751,0.001082687,0.0001016365,0.0001218781,0.000179745,0.0001805563,0.000516257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001733303,"about_ca_system_score_gemma":0.0002386941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000627729,"about_ca_topic_score_gemma":0.0007617155,"domain_scores_codex":[0.9999136,0.00001490173,0.000006972679,0.00003061476,0.0000219163,0.00001199967],"domain_scores_gemma":[0.9999398,0.00001439638,0.0000154339,0.000007665615,0.0000119801,0.00001069652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001094874,0.00007336719,0.02254212,0.0006763689,0.0001760152,0.0008783712,0.0001542049,0.001495814,0.9349631,0.0006421017,0.003674464,0.03362922],"study_design_scores_gemma":[0.0001286012,0.0009307261,0.3538049,0.00009317443,0.0003822442,0.00610235,0.0006417118,0.01063182,0.5516483,0.002005088,0.07357732,0.00005374071],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"review","genre_scores_codex":[0.9732007,0.008122982,0.003757419,0.0003236613,0.00001934845,0.00002546019,0.01260786,0.0001950133,0.001747524],"genre_scores_gemma":[0.9387787,0.004757866,0.01686841,0.0001673948,0.00002645319,0.00003330975,0.03790567,0.00003822604,0.001423912],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.0009879138,"threshold_uncertainty_score":0.003304899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03771003358531715,"score_gpt":0.2688047598891675,"score_spread":0.2310947263038504,"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."}}