{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007256973,0.001059954,0.001235471,0.0008189065,0.0004622683,0.0007431128,0.0006364711,0.0007729318,0.001783227],"category_scores_gemma":[0.0006711553,0.0004221862,0.0005170622,0.001166512,0.0002204169,0.0004785141,0.0008397524,0.0009546343,0.001685711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003562628,"about_ca_system_score_gemma":0.0003262261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004459556,"about_ca_topic_score_gemma":0.0007679892,"domain_scores_codex":[0.9991762,0.0001819074,0.00004206157,0.0001274052,0.0003735751,0.00009884906],"domain_scores_gemma":[0.9997447,0.0001004385,0.00002610766,0.00002461037,0.00007888863,0.00002522924],"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":[0.0001229347,0.0001938374,0.000261172,0.0001759369,0.00003049116,0.00008593675,0.00003455177,0.000796482,0.9860633,0.0001549345,0.0005259106,0.01155447],"study_design_scores_gemma":[0.00003095775,0.0006100544,0.0007836937,0.00001357043,0.00004371746,0.0002036465,0.00003388475,0.002876103,0.9906879,0.0001415811,0.004551791,0.00002317125],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7358938,0.01220785,0.2272259,0.000843031,0.0001539959,0.001926314,0.004911863,0.002713686,0.01412358],"genre_scores_gemma":[0.7704053,0.01343812,0.1907486,0.0004593071,0.00008132492,0.002674996,0.006221211,0.0003001958,0.01567081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001783227,"threshold_uncertainty_score":0.005965471,"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."}}