{"id":"W4417428524","doi":"10.1038/s41467-025-67403-2","title":"Enantioselective protein affinity selection mass spectrometry (E-ASMS)","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Princess Margaret Cancer Centre; The Scarborough Hospital; Structural Genomics Consortium; University of Toronto","funders":"National Institute on Aging; Natural Sciences and Engineering Research Council of Canada; School of Medicine, Emory University; Emory University; European Federation of Pharmaceutical Industries and Associations; University of Toronto; National Institutes of Health; McGill University; Alexander S. Onassis Public Benefit Foundation; Government of Canada; Bristol-Myers Squibb; Bayer; Genentech; Deutsche Krebshilfe; Winship Cancer Institute; Pfizer","keywords":"Enantioselective synthesis; Mass spectrometry; High-throughput screening; Characterization (materials science); Selection (genetic algorithm); Plasma protein binding; Identification (biology); Target protein; Protein–protein interaction","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":[0.0014234,0.001218411,0.000725688,0.0007377857,0.0003516466,0.0006229678,0.0009244406,0.0005813091,0.001269227],"category_scores_gemma":[0.0008263832,0.0004332895,0.0006091775,0.0004958355,0.0005683435,0.000423342,0.0009300564,0.001162565,0.001584205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003570981,"about_ca_system_score_gemma":0.0004700701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005142937,"about_ca_topic_score_gemma":0.0008811887,"domain_scores_codex":[0.9986332,0.0002722772,0.00006859329,0.0002624792,0.0006306652,0.0001327592],"domain_scores_gemma":[0.9995127,0.0001168836,0.0001159649,0.00008701083,0.0001209385,0.00004658082],"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":[0.0002065279,0.0001220793,0.001368899,0.0002048973,0.0000872446,0.0002534222,0.00006106608,0.000625631,0.9585063,0.001809151,0.001893157,0.03486163],"study_design_scores_gemma":[0.00003317681,0.0002354236,0.0007901856,0.000008765529,0.00002855016,0.0007590029,0.0000230313,0.005363918,0.9818563,0.0002437254,0.01063079,0.00002718415],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4908103,0.008429846,0.4712583,0.001725532,0.0004474597,0.0009668839,0.002838034,0.005101327,0.01842235],"genre_scores_gemma":[0.7052161,0.005742865,0.2719769,0.00144064,0.0001776323,0.0004391212,0.002330771,0.0002770411,0.0123989],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0014234,"threshold_uncertainty_score":0.007527769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01011504776983701,"score_gpt":0.2964497663385385,"score_spread":0.2863347185687015,"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."}}