{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001468112,0.001025138,0.0007020587,0.0006313602,0.0002885941,0.0006439749,0.0007662044,0.0004407018,0.001383975],"category_scores_gemma":[0.000869273,0.0004049925,0.0005188675,0.0004626893,0.0004859985,0.0002942768,0.0006806225,0.0009769092,0.001586966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000316082,"about_ca_system_score_gemma":0.0003612745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004704516,"about_ca_topic_score_gemma":0.0006458318,"domain_scores_codex":[0.9985805,0.0003877562,0.0000784514,0.0002672553,0.0005536397,0.0001324136],"domain_scores_gemma":[0.9994777,0.0001383382,0.0001140417,0.0001036672,0.0001219153,0.00004446318],"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":[0.0001998055,0.00007687264,0.0009428091,0.0001171018,0.00005997373,0.0001689766,0.0000366916,0.0004526105,0.9760756,0.0009583569,0.001259857,0.01965128],"study_design_scores_gemma":[0.0000246031,0.000130636,0.0006700031,0.000005066272,0.0000189611,0.0005135529,0.00001456087,0.003839134,0.9884875,0.0001325819,0.006147502,0.00001578733],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5991092,0.005154845,0.3728755,0.001199377,0.0002918766,0.0006528285,0.002903151,0.003977951,0.01383532],"genre_scores_gemma":[0.7605321,0.002799125,0.2214527,0.000941636,0.0001003498,0.0002897448,0.002248971,0.0002918377,0.01134346],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001468112,"threshold_uncertainty_score":0.007764161,"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."}}