{"id":"W4417120895","doi":"10.1021/acschembio.5c00702","title":"Overcoming Ligand Discovery Challenges: Developing Peptide-Based Tracers for SPSB2","year":2025,"lang":"en","type":"article","venue":"ACS Chemical Biology","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Genentech; Innovative Medicines Initiative; Deutschen Konsortium für Translationale Krebsforschung; Ontario Genomics Institute; European Federation of Pharmaceutical Industries and Associations; Structural Genomics Consortium; Janssen Pharmaceuticals; Merck KGaA; Ontario Genomics; Genome Canada; Bristol-Myers Squibb Foundation; Pfizer; Deutsches Krebsforschungszentrum; Bristol-Myers Squibb; Bayer; Diamond Light Source; Takeda Canada; McGill University; Boehringer Ingelheim","keywords":"Degron; Ubiquitin ligase; Proteolysis; Drug discovery; Ligand (biochemistry); Peptide; Plasma protein binding; DNA ligase; Confocal microscopy","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":[0.0001421527,0.0001591131,0.0001826361,0.00004854524,0.0000611924,0.00001826248,0.0001964547,0.0003106013,0.000002340615],"category_scores_gemma":[0.0003851499,0.0001458103,0.0001065893,0.00006823412,0.0001120738,0.000005811883,0.00008704319,0.0000858266,0.000002738489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003509872,"about_ca_system_score_gemma":0.0001516563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003828627,"about_ca_topic_score_gemma":0.000005487866,"domain_scores_codex":[0.9990246,0.00002943516,0.0002304693,0.0004097432,0.0000378459,0.0002678488],"domain_scores_gemma":[0.9995313,0.0000764217,0.00006315816,0.0002319172,0.00005696001,0.00004029168],"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":[0.0001268153,0.0000268813,0.0002631034,0.00004744149,0.00004176957,2.380868e-7,0.000007798939,0.000001618625,0.9868646,0.00500276,0.002672246,0.004944769],"study_design_scores_gemma":[0.0007605491,0.0000702613,0.00005373031,0.00003263285,0.00001072938,9.684569e-7,0.00002809452,0.00001734748,0.859879,0.001098284,0.1378874,0.0001610344],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9588065,0.002544678,0.03227075,0.004184332,0.0003264051,0.0003864484,0.00003117372,0.00003275076,0.001416925],"genre_scores_gemma":[0.993502,0.0002820095,0.003342692,0.001790088,0.0002081578,0.0001030101,0.0003324016,0.00001404743,0.000425611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1352152,"threshold_uncertainty_score":0.5945971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01966360601027635,"score_gpt":0.2814646283140008,"score_spread":0.2618010223037245,"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."}}