{"id":"W4412846640","doi":"10.1101/2025.07.29.667383","title":"Click. Screen. Degrade. A Miniaturized D2B Workflow for rapid PROTAC Discovery","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":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Genentech; Deutsche Krebshilfe; Deutschen Konsortium für Translationale Krebsforschung; Ontario Genomics Institute; European Federation of Pharmaceutical Industries and Associations; Structural Genomics Consortium; Merck KGaA; Ontario Genomics; Genome Canada; McGill University; Bayer; Pfizer; Deutsche Forschungsgemeinschaft; Deutsches Krebsforschungszentrum; Bristol-Myers Squibb","keywords":"Workflow; Computer science; Computational biology; Database; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002383616,0.002408872,0.001712944,0.00275121,0.00070129,0.001666934,0.0014011,0.001336923,0.04501368],"category_scores_gemma":[0.00163245,0.00179117,0.001386163,0.001122521,0.0004889678,0.001113912,0.001604163,0.002270015,0.05325205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006584335,"about_ca_system_score_gemma":0.001179208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00120082,"about_ca_topic_score_gemma":0.001892517,"domain_scores_codex":[0.9982044,0.0001766095,0.0001552368,0.0005704931,0.0006792013,0.0002140338],"domain_scores_gemma":[0.9991477,0.0003218371,0.00006825521,0.0002302581,0.000105671,0.0001262865],"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":[0.003002097,0.0002619693,0.001403688,0.001539761,0.000261435,0.0009981403,0.000168724,0.0007456565,0.7786425,0.004068288,0.0917169,0.1171908],"study_design_scores_gemma":[0.0006211788,0.0003174575,0.002766236,0.0001001675,0.00007430591,0.001616264,0.00002947843,0.007518565,0.7067059,0.002842352,0.2772146,0.0001935767],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03075934,0.003340968,0.5616692,0.001113577,0.0006087146,0.001889553,0.09424363,0.2827491,0.02362589],"genre_scores_gemma":[0.1060714,0.003362385,0.6715164,0.001910827,0.0002456183,0.007169934,0.1189414,0.03883697,0.0519451],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04501368,"threshold_uncertainty_score":0.1505857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01222588678868859,"score_gpt":0.2344290961484673,"score_spread":0.2222032093597787,"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."}}