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A deep generative model enables automated structure elucidation of novel psychoactive substances

Nature Machine Intelligence volume 3, pages 973–984 (2021)Cite this article

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A preprint version of the article is available at ChemRxiv.

Abstract

Over the past decade, the illicit drug market has been reshaped by the proliferation of clandestinely produced designer drugs. These agents, referred to as new psychoactive substances (NPSs), are designed to mimic the physiological actions of better-known drugs of abuse while skirting drug control laws. The public health burden of NPS abuse obliges toxicological, police and customs laboratories to screen for them in law enforcement seizures and biological samples. However, the identification of emerging NPSs is challenging due to the chemical diversity of these substances and the fleeting nature of their appearance on the illicit market. Here we present DarkNPS, a deep learning-enabled approach to automatically elucidate the structures of unidentified designer drugs using only mass spectrometric data. Our method employs a deep generative model to learn a statistical probability distribution over unobserved structures, which we term the structural prior. We show that the structural prior allows DarkNPS to elucidate the exact chemical structure of an unidentified NPS with an accuracy of 51% and a top-10 accuracy of 86%. Our generative approach has the potential to enable de novo structure elucidation for other types of small molecules that are routinely analysed by mass spectrometry.

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Fig. 1: A deep generative model of novel psychoactive substances.
Fig. 2: Generated molecules closely resemble known designer drugs.
Fig. 3: Sampling frequency defines a structural prior over unseen molecules.
Fig. 4: Automated structure elucidation of unidentified NPSs.
Fig. 5: Application of the structural prior to the designer dissociative deoxymethoxetamine.
Fig. 6: High-confidence structure elucidation using MS/MS.

Data availability

Owing to the sensitivity of the data and the potential for misuse, HighResNPS and the databases of generated molecules and MS/MS spectra described here are not available to the public for unrestricted download. However, the data have been uploaded to the NPS Data Hub59 (https://nps-datahub.com/) and will be made available to all qualified researchers in the field upon request. A demonstration dataset of 2,000 SMILES strings for drug-like small molecules sampled at random from the ChEMBL database is provided at http://github.com/skinnider/NPS-generation to demonstrate the functionality of the code.

Code availability

Code used to train and evaluate chemical language models is available from GitHub at http://github.com/skinnider/NPS-generation (https://doi.org/10.5281/zenodo.5136669).

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Acknowledgements

This work was supported by funding from Genome Canada, Genome British Columbia and Genome Alberta (project 284MBO), the National Institutes of Health (NIH), National Institute of Environmental Health Sciences grant no. U2CES030170 and computational resources provided by WestGrid, Compute Canada and Advanced Research Computing at the University of British Columbia. M.A.S. acknowledges support from a CIHR Vanier Canada Graduate Scholarship, a Roman M. Babicki Fellowship in Medical Research, a Borealis AI Graduate Fellowship, a Walter C. Sumner Memorial Fellowship and a Vancouver Coastal Health–CIHR–UBC MD/PhD Studentship.

Author information

Authors and Affiliations

  1. Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada

    Michael A. Skinnider & Leonard J. Foster

  2. Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada

    Fei Wang, Russell Greiner & David S. Wishart

  3. Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada

    Fei Wang & Russell Greiner

  4. Section of Forensic Chemistry, Department of Forensic Medicine, University of Copenhagen, Copenhagen, Denmark

    Daniel Pasin & Petur W. Dalsgaard

  5. Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, British Columbia, Canada

    Leonard J. Foster

  6. Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada

    David S. Wishart

  7. Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada

    David S. Wishart

  8. Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada

    David S. Wishart

  9. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

    David S. Wishart

Authors
  1. Michael A. Skinnider
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Contributions

All authors contributed to study design. M.A.S. and F.W. performed experiments. P.W.D. supervised the analysis of DXME at RKA. All authors contributed to data analysis. M.A.S. wrote the manuscript. All authors edited the manuscript.

Corresponding authors

Correspondence to Michael A. Skinnider or David S. Wishart.

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Peer review information Nature Machine Intelligence thanks Claude Guillou, Stefan Kuhn and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Model selection and hyperparameter optimization.

a, Jensen-Shannon distance between the distribution of Murcko scaffolds in the training set and generated molecules, for recurrent neural network-based models trained on the HighResNPS database after varying degrees of non-canonical SMILES enumeration. b, Jensen-Shannon distance between the natural product-likeness scores of the training set and generated molecules, for recurrent neural network-based models trained on the HighResNPS database after varying degrees of non-canonical SMILES enumeration. c, Jensen-Shannon distance between the proportion of stereocenters in the training set and generated molecules, for recurrent neural network-based models trained on the HighResNPS database after varying degrees of non-canonical SMILES enumeration. d, Factor loadings onto the first principal component in a principal component analysis of recurrent neural network-based models trained on the HighResNPS database after varying degrees of non-canonical SMILES enumeration.

Extended Data Fig. 2 Physicochemical properties and EMCDDA drug categorizations of generated molecules.

a, Calculated octanol-water partition coefficients (LogP) of known NPSs and generated molecules. b, Topological complexities of known NPSs and generated molecules. c, Natural product-likeness scores of known NPSs and generated molecules. d, Synthetic accessibility scores of known NPSs and generated molecules. e, UMAP visualization of known NPSs and an equal number of generated molecules sampled at random from the trained generative model, with the known NPSs colored by their EMCDDA drug categorizations. f, Log-odds ratios of EMCDDA drug category frequencies among generated molecules, as compared to the training set. *, p < 0.05; ***, p < 0.001.

Extended Data Fig. 3 Sampling frequencies of known and generated molecules.

a, Distribution of sampling frequencies within a sample of 1 billion SMILES strings from the trained generative model, with known NPSs from the training set shown in red. b, Jensen-Shannon distance between the molecular weights of generated molecules and the set of known NPSs, for molecules generated with progressively increasing frequencies. c, Jensen-Shannon distance between the quantitative estimate of drug-likeness (QED) score of generated molecules and the set of known NPSs, for molecules generated with progressively increasing frequencies. d, Jensen-Shannon distance between the proportion of carbons that are sp3-hybridized in generated molecules and the set of known NPSs, for molecules generated with progressively increasing frequencies. e, Jensen-Shannon distance between the partition coefficients of generated molecules and the set of known NPSs, for molecules generated with progressively increasing frequencies. f, Jensen-Shannon distance between the topological complexities of generated molecules and the set of known NPSs, for molecules generated with progressively increasing frequencies. g, Jensen-Shannon distance between the natural product-likeness scores of generated molecules and the set of known NPSs, for molecules generated with progressively increasing frequencies. h, Jensen-Shannon distance between the synthetic accessibility scores of generated molecules and the set of known NPSs, for molecules generated with progressively increasing frequencies. i, Jensen-Shannon distance between the proportion of stereocenters in generated molecules and the set of known NPSs, for molecules generated with progressively increasing frequencies.

Extended Data Fig. 4 Examples of molecules from the held-out set that were not generated by DarkNPS.

Chemical structures of an illustrative subset of the 18 molecules in the held-out set that were never produced by the generative model in a sample of 1 billion SMILES strings, and their nearest neighbors among structures that were generated by the model. Many of these molecules either are not designer drugs at all (for example, clozapine, citicoline, nalmefene, 2,2-dibromo-1-phenylhexan-2-one), or had a very closely related molecule appear in the model output.

Extended Data Fig. 5 Examples of molecules from the held-out set that were correctly anticipated by DarkNPS.

a, Frequency with which each of the 194 molecules in the held-out set were sampled from the generative model. b, Chemical structures, left, and sampling frequencies, right, for an illustrative subset of molecules in the held-out set that were correctly anticipated by the generated molecule. The molecules were selected from across the spectrum of sampling frequency in order to illustrate some of the major chemotypes captured by the generative model.

Extended Data Fig. 6 Benchmarking the structural prior using continuous molecular embeddings.

a, Median Euclidean distance between CDDD embeddings of held-out NPSs and generated molecules matching their exact masses ( ± 10 ppm), arranged in descending order by sampling frequency (“most frequent”), ascending order by sampling frequency (“least frequent”), or a random sample of molecules with matching exact masses from PubChem. Error bars show the interquartile range. b, Distribution of Euclidean distances between the CDDD embeddings of held-out NPSs and generated molecules matching their exact masses ( ± 10 ppm), taking either the single most frequently sampled generated molecule, the single least frequently sampled generated molecule, or a random molecule with a matching exact mass from PubChem.

Extended Data Fig. 7 Application of the structural prior to the synthetic cannabinoid ADB-HEXINACA.

Left, the chemical structure, molecular formula, and exact mass of ADB-HEXINACA. Middle, sampling frequencies of the 20 most frequently sampled molecules matching the exact mass of ADB-HEXINACA ( ± a 10 ppm window). An illustrative subset of the generated molecules, highlighted in red, are shown on the bottom.

Extended Data Fig. 8 Improved chemical similarity of automatically elucidated structures after MS/MS data integration.

a, Euclidean distances between CDDD embeddings for molecules in the held-out set of unidentified NPSs and the top-ranked structures suggested by CFM-ID alone, the structural prior alone, or the combination of the two. b, Improvements in automated structure elucidation of an unidentified NPS using tandem mass spectrometry. Left, the chemical structure of 3,4-MDMA methylene homologue, created by inserting a methylene spacer between the α-carbon and amine group in MDMA. Middle, the top-ranked molecule suggested by the structural prior and mirror plot comparing the observed tandem mass spectrum of 3,4-MDMA methylene homologue with the tandem mass spectrum predicted by CFM-ID. Right, the top-ranked molecule after integrating the structural prior with MS/MS evidence (top) and mirror plot comparing the observed and predicted tandem mass spectra.

Extended Data Fig. 9 Evaluation of DarkNPS against two additional baselines.

a–d, Comparison of DarkNPS to chemical database search against known NPSs from the training set. a, Top-1 accuracy with which the complete chemical structures of unidentified NPSs in the held-out set were correctly elucidated by the combination of the structural prior and CFM-ID, or chemical database search against the 1,753 known NPSs in the training set. Searching against the disjoint training set yields a top-1 accuracy of 0%, by definition. b, Top-k accuracy curve for structure elucidation of unidentified NPSs in the held-out set by the combination of the structural prior and CFM-ID, or chemical database search against the 1,753 known NPSs in the training set. Searching against the disjoint training set yields a top-1 accuracy of 0%, by definition. c, Tanimoto coefficients between the held-out set and the top-ranked structures suggested by the combination of the structural prior and CFM-ID, or chemical database search against the 1,753 known NPSs in the training set. Results are not shown for 30 held-out molecules whose masses did not match any molecule in the training set. d, As in c, but showing the Euclidean distances between CDDD embeddings. Results are not shown for 30 held-out molecules whose masses did not match any molecule in the training set. e–h, Comparison of DarkNPS to the AddCarbon model. e, Top-1 accuracy with which the complete chemical structures of unidentified NPSs in the held-out set were correctly elucidated by the combination of the structural prior and CFM-ID, or chemical database search against 34,358 molecules generated by the AddCarbon baseline. f, Top-k accuracy curve for structure elucidation of unidentified NPSs in the held-out set by the combination of the structural prior and CFM-ID, or chemical database search against 34,358 molecules generated by the AddCarbon baseline. g, Tanimoto coefficients between the held-out set and the top-ranked structures suggested by the combination of the structural prior and CFM-ID, or chemical database search against 34,358 molecules generated by the AddCarbon baseline. Results are not shown for 38 held-out molecules whose masses did not match any molecule generated by the AddCarbon model. h, As in g, but showing the Euclidean distances between CDDD embeddings. Results are not shown for 38 held-out molecules whose masses did not match any molecule generated by the AddCarbon model.

Extended Data Fig. 10 Robustness of the structural prior to sample size.

a, Correlation between molecular sampling frequency in the original sample of 1 billion SMILES strings and a second sample of 1 billion SMILES strings. Inset text shows the Pearson correlation. b, Proportion of NPSs in the held-out set that were generated after downsampling the original sample of 1 billion SMILES strings to between 1,000 and 300 million SMILES. Only marginal improvement is observed after approximately 100 million SMILES. c, Top-k accuracy of the structural prior in the held-out set after downsampling the original sample of 1 billion SMILES strings to between 1,000 and 300 million SMILES. Only marginal improvement is observed after approximately 100 million SMILES.

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Skinnider, M.A., Wang, F., Pasin, D. et al. A deep generative model enables automated structure elucidation of novel psychoactive substances. Nat Mach Intell 3, 973–984 (2021). https://doi.org/10.1038/s42256-021-00407-x

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