{"id":"W4401070200","doi":"10.1021/acs.analchem.4c01001","title":"Quantitative Hydrogen–Deuterium Exchange Mass Spectrometry for Simultaneous Structural Characterization and Affinity Indexing of Single Target Drug Candidate Libraries","year":2024,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; Structural Genomics Consortium; University of Toronto; York University","funders":"Structural Genomics Consortium","keywords":"Chemistry; Hydrogen–deuterium exchange; Mass spectrometry; Deuterium; Drug discovery; Surface plasmon resonance; Small molecule; Dissociation (chemistry); Characterization (materials science); Molecule; Allosteric regulation; Combinatorial chemistry; Computational chemistry; Chromatography; Nanotechnology; Physical chemistry; Biochemistry; Organic chemistry; Nanoparticle","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":[0.001059047,0.0005852501,0.0003185196,0.0006932159,0.0002475043,0.0004302787,0.0003599008,0.0002799586,0.001057337],"category_scores_gemma":[0.0008934678,0.0001840889,0.0001971937,0.0005093431,0.0003067267,0.0003637149,0.0003617316,0.0004508671,0.0003414956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003954926,"about_ca_system_score_gemma":0.0003484403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004611247,"about_ca_topic_score_gemma":0.001337146,"domain_scores_codex":[0.9995061,0.00008910806,0.00002403804,0.0001125593,0.0002350086,0.00003314748],"domain_scores_gemma":[0.9996517,0.0001552362,0.00006615787,0.00004511294,0.00006020956,0.00002164295],"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":[0.00008375786,0.00003023876,0.0008656958,0.0000711604,0.00002176454,0.00001723416,0.00002057397,0.0004898596,0.9818068,0.0002400487,0.0001339973,0.01621884],"study_design_scores_gemma":[0.00001054353,0.0001583959,0.0026829,0.000004028816,0.00001918239,0.0001036146,0.00001966238,0.008583181,0.986488,0.0001647822,0.001754438,0.00001116961],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6944861,0.005130622,0.2913327,0.000281378,0.00008176321,0.0002900339,0.001762795,0.001185901,0.005448633],"genre_scores_gemma":[0.8654464,0.00179043,0.1287223,0.0002624724,0.00002228975,0.0001518545,0.0008481095,0.00006076911,0.002695284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001059047,"threshold_uncertainty_score":0.00560081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01419245771546023,"score_gpt":0.2660510078279071,"score_spread":0.2518585501124468,"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."}}