{"id":"W4406727664","doi":"10.1101/2025.01.17.633682","title":"Enantioselective Protein Affinity Selection Mass Spectrometry (E-ASMS)","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":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; The Scarborough Hospital; Structural Genomics Consortium; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; School of Medicine, Emory University; National Institutes of Health; Emory University; University of Toronto; European Federation of Pharmaceutical Industries and Associations; Bristol-Myers Squibb; McGill University; Deutsche Krebshilfe; Genentech; Bayer; Winship Cancer Institute; Pfizer","keywords":"Enantioselective synthesis; Mass spectrometry; Selection (genetic algorithm); Chemistry; Chromatography; Computer science; Biochemistry; Artificial intelligence; Catalysis","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005250897,0.0005975608,0.0004487807,0.0003709123,0.0002267673,0.0001708018,0.0005398921,0.0009753182,0.00005162479],"category_scores_gemma":[0.0005268416,0.0006675432,0.0002483779,0.0007367212,0.0001020509,0.00001515917,0.0004472731,0.0008486136,0.00003666721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002524232,"about_ca_system_score_gemma":0.001167551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004570932,"about_ca_topic_score_gemma":0.000007238358,"domain_scores_codex":[0.9970514,0.0002555113,0.0004945265,0.001288674,0.00034113,0.0005687758],"domain_scores_gemma":[0.9978014,0.00001392946,0.0004022979,0.0009662597,0.000621917,0.0001942307],"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":[0.00007280098,0.0001290779,0.001435028,0.0001856204,0.000202019,0.000004118609,0.000001406488,0.00002569196,0.9962285,0.0005064079,0.001206537,0.00000279542],"study_design_scores_gemma":[0.0004369225,0.0001585803,0.006288141,0.0002034226,0.00005971863,1.64096e-8,0.00000144401,0.00008653742,0.9808908,0.00001003021,0.01117707,0.0006873509],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9672375,0.001096174,0.02732104,0.0002536667,0.001207611,0.001963222,0.0003209717,0.0003454825,0.0002543003],"genre_scores_gemma":[0.9830458,0.0001872259,0.01495598,0.0001873492,0.0008806431,0.0004465505,0.000003838406,0.00007980461,0.000212843],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01580824,"threshold_uncertainty_score":0.9995776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007663598990833283,"score_gpt":0.2181561148071208,"score_spread":0.2104925158162876,"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."}}