{"id":"W4409623886","doi":"10.1101/2025.04.14.648778","title":"Characterization of PROTAC specificity and endogenous protein interactomes using ProtacID","year":2025,"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; Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research; Genentech; European Federation of Pharmaceutical Industries and Associations; University of Toronto; Ontario Genomics Institute; Merck KGaA; Mitacs; Ontario Genomics; Genome Canada; McGill University; Bayer; Pfizer; Bristol-Myers Squibb","keywords":"Endogeny; Characterization (materials science); Protein–protein interaction; Computational biology; Chemistry; Computer science; Biology; Biochemistry; Nanotechnology; Materials science","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":[0.0004575734,0.0004121173,0.0003398139,0.0003895881,0.000419898,0.0006517044,0.0003763013,0.0003760068,0.001802869],"category_scores_gemma":[0.0003438767,0.0002629285,0.0003133762,0.0002176901,0.0003499463,0.0003872239,0.0004673135,0.0008374486,0.001312134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003780616,"about_ca_system_score_gemma":0.0002359807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006448978,"about_ca_topic_score_gemma":0.001429355,"domain_scores_codex":[0.9996479,0.00005934386,0.00002443447,0.00008872906,0.0001311321,0.00004857192],"domain_scores_gemma":[0.9995549,0.0001119825,0.00008623343,0.00009380747,0.00007960553,0.00007336292],"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":[0.0000530896,0.000004805578,0.0001027201,0.00002333717,0.000003651264,0.00003450809,0.00001566943,0.0000271939,0.9991346,0.00006259606,0.00002047272,0.0005172557],"study_design_scores_gemma":[0.000004059242,0.00002989847,0.001339967,0.000003040331,0.000004605141,0.0002774165,0.00001588326,0.0005436124,0.9950536,0.0000251334,0.002698854,0.000003970216],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9154097,0.002532675,0.07584477,0.0003147363,0.00004971768,0.0001527975,0.001499722,0.0004823752,0.003713384],"genre_scores_gemma":[0.9167764,0.001420941,0.0626828,0.0002290174,0.00001958528,0.0001490761,0.005784076,0.0004262172,0.01251191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001802869,"threshold_uncertainty_score":0.006031156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01788500567973451,"score_gpt":0.2253855389338255,"score_spread":0.207500533254091,"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."}}