{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006020207,0.001010793,0.0007122318,0.0007933899,0.0003920373,0.001075873,0.001443619,0.001722201,0.002883194],"category_scores_gemma":[0.001919725,0.0009816127,0.001723656,0.0005338197,0.001137351,0.001134578,0.001460341,0.002695637,0.00101221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001383807,"about_ca_system_score_gemma":0.001140996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007204984,"about_ca_topic_score_gemma":0.01308175,"domain_scores_codex":[0.9997334,0.00005841132,0.000009718198,0.00009431277,0.00006625306,0.00003778973],"domain_scores_gemma":[0.9991717,0.0005715892,0.00006247,0.00008852743,0.00006260018,0.00004316367],"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":[0.0001528711,0.00008715751,0.002122573,0.0001108502,0.00009745589,0.000302269,0.0000843923,0.8779907,0.01028216,0.01759608,0.004250816,0.08692259],"study_design_scores_gemma":[0.000003433772,0.000006609414,0.00006222275,0.00000414563,0.000004938198,0.0000175469,0.000002263077,0.9938352,0.0008429583,0.00477452,0.0004434867,0.000002710238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04337223,0.0008002947,0.9465488,0.00101499,0.00008626564,0.00005167926,0.0009304956,0.003915648,0.003279648],"genre_scores_gemma":[0.7248796,0.0009032823,0.2594763,0.0009709116,0.0001468862,0.0001779504,0.003531635,0.000593465,0.009319982],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007204984,"threshold_uncertainty_score":0.0143261,"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."}}