{"id":"W4407350032","doi":"10.1021/acschembio.4c00812","title":"Workflow for E3 Ligase Ligand Validation for PROTAC Development","year":2025,"lang":"en","type":"article","venue":"ACS Chemical Biology","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Structural Genomics Consortium; LOEWE-Zentrum für Translationale Medizin und Pharmakologie; Deutschen Konsortium für Translationale Krebsforschung; Fraunhofer-Gesellschaft; Innovative Medicines Initiative; Technische Universität München; Bundesministerium für Bildung und Forschung; Deutsche Krebshilfe; Hessisches Ministerium für Wissenschaft und Kunst; Deutsche Forschungsgemeinschaft; Deutsches Krebsforschungszentrum","keywords":"Workflow; Ubiquitin ligase; DNA ligase; Ligand (biochemistry); Computer science; Computational biology; Business; Process management; Chemistry; Ubiquitin; Biology; Database; Biochemistry; Receptor; DNA","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001510558,0.000120316,0.0001310891,0.00003410664,0.0000708513,0.00001300669,0.000144972,0.0002621143,0.000003228945],"category_scores_gemma":[0.0004317616,0.0001083123,0.00006733498,0.00006540535,0.00005440889,0.000002503939,0.00007241609,0.00004452005,0.000003492145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000193761,"about_ca_system_score_gemma":0.0001102441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.96506e-7,"about_ca_topic_score_gemma":6.952816e-7,"domain_scores_codex":[0.9991868,0.00001492945,0.0002297032,0.0003272382,0.00002805167,0.0002133266],"domain_scores_gemma":[0.9995813,0.00003945237,0.00005811256,0.0001680166,0.0001104863,0.00004261527],"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":[0.0001663621,0.00004318362,0.0001754509,0.0000309472,0.00003807394,3.537307e-8,0.000007222227,4.254147e-7,0.9663092,0.0008000588,0.007245163,0.02518381],"study_design_scores_gemma":[0.0006282249,0.00007096941,0.00001187947,0.00001093857,0.000007546535,5.921389e-7,0.00000355762,0.000007107905,0.72879,0.001305031,0.2690693,0.00009479866],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9688462,0.0003126796,0.02796242,0.001054921,0.0002309676,0.001196217,0.00002830955,0.0000248292,0.0003434404],"genre_scores_gemma":[0.9763737,0.00002167404,0.01784193,0.0008813184,0.0002879273,0.001317564,0.001812009,0.00001395762,0.001449909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2618241,"threshold_uncertainty_score":0.4416846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01221062706261001,"score_gpt":0.2842726104764001,"score_spread":0.2720619834137901,"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."}}