{"id":"W4390784145","doi":"10.48550/arxiv.2401.04453","title":"High throughput screening for LC3/GABARAP binders utilizing fluorescence polarization assay","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"GABA and Rice Research","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Genentech; Deutschen Konsortium für Translationale Krebsforschung; Ontario Genomics Institute; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Ontario Genomics; Genome Canada; McGill University; Bayer; Pfizer; Deutsche Forschungsgemeinschaft; Deutsches Krebsforschungszentrum; Bristol-Myers Squibb","keywords":"ATG8; Autophagy; Small molecule; Chemistry; Peptide; Fluorescence; Fluorescence anisotropy; High-throughput screening; Molecule; Combinatorial chemistry; Biophysics; Nanotechnology; Biochemistry; Biology; Materials science; Organic chemistry; Physics","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":[0.0004584484,0.0002885287,0.0002822017,0.00007411234,0.0004058789,0.0001986079,0.0006862427,0.0004219796,0.0001226693],"category_scores_gemma":[0.00007989783,0.0001440695,0.0003070483,0.0007418576,0.0001235759,0.00014971,0.001001216,0.0006686487,0.00006463283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001089734,"about_ca_system_score_gemma":0.00005532259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001188179,"about_ca_topic_score_gemma":0.0002848702,"domain_scores_codex":[0.9979844,0.0001407104,0.0001842151,0.001014868,0.0001553782,0.0005203895],"domain_scores_gemma":[0.9990595,0.0003182477,0.0001205981,0.0001588048,0.0001788196,0.000164043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000885889,0.0007575239,0.03533509,0.001931319,0.001348089,0.0008053093,0.0008494786,0.04770721,0.4058712,0.3507418,0.00674413,0.1470229],"study_design_scores_gemma":[0.001907659,0.001188741,0.1539585,0.002388941,0.001072046,0.00002335408,0.007175586,0.5899464,0.01050029,0.2185083,0.008815972,0.004514182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990213,0.0001858108,0.005969209,0.0009422444,0.0005872705,0.0007599695,0.0002030086,0.0002795905,0.0008599277],"genre_scores_gemma":[0.9963195,0.0001976681,0.0004774809,0.00004263834,0.0003079507,0.000002776001,0.0002902545,0.000004649239,0.002357023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5422392,"threshold_uncertainty_score":0.5874984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1424851452933526,"score_gpt":0.2270427501698546,"score_spread":0.08455760487650196,"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."}}