{"id":"W4236044224","doi":"10.26434/chemrxiv.14644854","title":"A Deep Generative Model Enables Automated Structure Elucidation of Novel Psychoactive Substances","year":2021,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Forensic Toxicology and Drug Analysis","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of Alberta; University of British Columbia","funders":"National Institutes of Health; Genome Alberta; University of British Columbia; Genome British Columbia; Compute Canada; Canadian Institutes of Health Research; Genome Canada","keywords":"Generative grammar; Generative model; Enforcement; Drugs of abuse; Law enforcement; Illicit drug; Artificial intelligence; Identification (biology); Drug; Computer science; Designer drug; Drug discovery; Computational biology; Pharmacology; Chemistry; Medicine; Biology; Political science; Law; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003832614,0.000551197,0.0009697124,0.0002407798,0.0002238794,0.00003603901,0.0004702305,0.001919577,0.001093032],"category_scores_gemma":[0.0001522154,0.0005377187,0.0003746503,0.0004218583,0.0005818971,0.0001414294,0.000278112,0.001890058,0.000006339605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001531477,"about_ca_system_score_gemma":0.0004695767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002514856,"about_ca_topic_score_gemma":0.0003208651,"domain_scores_codex":[0.9973458,0.0003322567,0.0007140346,0.0008996411,0.0002456384,0.0004626301],"domain_scores_gemma":[0.9976603,0.0002434312,0.0007940535,0.0005478851,0.000575568,0.000178811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003743006,0.0007518146,0.001013079,0.0003036769,0.002851039,0.00002155379,0.005081871,0.6877893,0.2961709,0.001116108,0.003151415,0.001375051],"study_design_scores_gemma":[0.000923581,0.00002281576,0.0003055879,0.00004247887,0.000733863,0.000006646448,0.0004844268,0.6191966,0.3765672,0.001204773,0.0001565692,0.0003555008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9710193,0.003132078,0.02062785,0.0004722322,0.001450122,0.0004678561,0.0001824316,0.0002877169,0.002360469],"genre_scores_gemma":[0.9859489,0.0004806874,0.01111109,0.0006967643,0.0002319835,0.00008107349,0.0009999624,0.00004367463,0.0004058992],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08039629,"threshold_uncertainty_score":0.9998201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08643922890686774,"score_gpt":0.4089420961955051,"score_spread":0.3225028672886374,"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."}}