{"id":"W4398135254","doi":"10.3390/metabo14050290","title":"Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning","year":2024,"lang":"en","type":"article","venue":"Metabolites","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Center for Complementary and Integrative Health","keywords":"Chemical shift; Metabolomics; Proton NMR; Chemistry; Nuclear magnetic resonance spectroscopy; Carbon-13 NMR; NMR spectra database; Chemical space; Metabolite; Drug discovery; Spectral line; Chromatography; Organic chemistry; Physical chemistry","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.000233053,0.0001510606,0.0002877508,0.0001073938,0.00003171962,0.0000164395,0.0001167314,0.00009025465,0.00002239614],"category_scores_gemma":[0.0002019038,0.0001296224,0.0001502767,0.0002112614,0.00008556064,0.000004671743,0.0001368643,0.000108694,0.000001193164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005427513,"about_ca_system_score_gemma":0.00003717063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005401373,"about_ca_topic_score_gemma":0.000005088068,"domain_scores_codex":[0.9990614,0.00005498988,0.0003134568,0.000277261,0.0001126396,0.0001802566],"domain_scores_gemma":[0.9995799,0.00002476564,0.00009949091,0.0001732898,0.00008375986,0.00003878492],"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":[0.00002910483,0.0000320643,0.002897755,0.0001123373,0.0002212169,0.000001117235,0.00005543632,0.0001991837,0.9939504,0.001742291,0.00002061505,0.0007384311],"study_design_scores_gemma":[0.0001917071,0.00008211638,0.001682955,0.00003162671,0.0001453802,0.000005264454,0.00004118533,0.01023956,0.9737177,0.0002776373,0.0134702,0.0001146204],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9434755,0.05022928,0.005523072,0.00003237114,0.0001608809,0.00008644752,0.0001247795,0.00001884161,0.0003487941],"genre_scores_gemma":[0.9899681,0.002048974,0.007530876,0.00001291248,0.0001647248,0.000005698928,0.0001017101,0.00002340003,0.0001436421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04818031,"threshold_uncertainty_score":0.5285848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02116695543160135,"score_gpt":0.2560980431823404,"score_spread":0.234931087750739,"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."}}