{"id":"W4389297057","doi":"10.1021/acs.analchem.3c02413","title":"Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances","year":2023,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Genome Alberta; Eesti Teadusagentuur; Genome British Columbia; Alberta Machine Intelligence Institute; Canadian Institutes of Health Research; Alliance de recherche numérique du Canada; Genome Canada","keywords":"Chemistry; Synthetic cannabinoids; Workflow; Identification (biology); Phencyclidine; Database; Computer science","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":[0.0007039828,0.001054265,0.0004919591,0.0008737131,0.0003187253,0.0006963746,0.0009093878,0.0009624587,0.002431027],"category_scores_gemma":[0.001510708,0.000386604,0.0006737255,0.0004970041,0.0003779875,0.001017704,0.0008833324,0.001336987,0.001341679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006910958,"about_ca_system_score_gemma":0.0009372659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005730692,"about_ca_topic_score_gemma":0.007150324,"domain_scores_codex":[0.9997099,0.0000412247,0.00001322584,0.0001093303,0.00008212212,0.00004417857],"domain_scores_gemma":[0.9995394,0.0001963418,0.00006774678,0.00005723821,0.0001011289,0.00003810066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001330483,0.00100282,0.01482533,0.0004020019,0.0002927503,0.0005457749,0.0001007294,0.3562,0.1106121,0.002522707,0.01864538,0.4935199],"study_design_scores_gemma":[0.00001115674,0.00003532099,0.0007492773,0.000007127626,0.00001159345,0.00002748274,0.000007233755,0.9875298,0.009480085,0.001145615,0.0009873012,0.000008105495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3424791,0.002917915,0.6128973,0.001706555,0.0002349703,0.0001385401,0.003595503,0.02811605,0.007914146],"genre_scores_gemma":[0.7900543,0.001158622,0.1952523,0.0007447421,0.0001044097,0.0001282641,0.005919256,0.0003965359,0.006241641],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005730692,"threshold_uncertainty_score":0.01139468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01264933388163547,"score_gpt":0.2698373789368256,"score_spread":0.2571880450551901,"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."}}