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Computer-designed repurposing of chemical wastes into drugs

Nature volume 604, pages 668–676 (2022)Cite this article

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Abstract

As the chemical industry continues to produce considerable quantities of waste chemicals1,2, it is essential to devise ‘circular chemistry’3,4,5,6,7,8 schemes to productively back-convert at least a portion of these unwanted materials into useful products. Despite substantial progress in the degradation of some classes of harmful chemicals9, work on ‘closing the circle’—transforming waste substrates into valuable products—remains fragmented and focused on well known areas10,11,12,13,14,15. Comprehensive analyses of which valuable products are synthesizable from diverse chemical wastes are difficult because even small sets of waste substrates can, within few steps, generate millions of putative products, each synthesizable by multiple routes forming densely connected networks. Tracing all such syntheses and selecting those that also meet criteria of process and ‘green’ chemistries is, arguably, beyond the cognition of human chemists. Here we show how computers equipped with broad synthetic knowledge can help address this challenge. Using the forward-synthesis Allchemy platform16, we generate giant synthetic networks emanating from approximately 200 waste chemicals recycled on commercial scales, retrieve from these networks tens of thousands of routes leading to approximately 300 important drugs and agrochemicals, and algorithmically rank these syntheses according to the accepted metrics of sustainable chemistry17,18,19. Several of these routes we validate by experiment, including an industrially realistic demonstration on a ‘pharmacy on demand’ flow-chemistry platform20. Wide adoption of computerized waste-to-valuable algorithms can accelerate productive reuse of chemicals that would otherwise incur storage or disposal costs, or even pose environmental hazards.

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Fig. 1: Examples of small molecules recycled from various types of industrial chemical waste.
Fig. 2: Generation and properties of ‘forward’ synthetic networks.
Fig. 3: Examples of highly ranked syntheses of more advanced drugs starting from waste substrates and few simple, auxiliary molecules used frequently in organic synthesis.
Fig. 4: Experimental validation of selected, computer-designed pathways in laboratory scale syntheses.
Fig. 5: Syntheses of COVID-19 intensive care unit medications or their intermediates performed on an automated, modular ODP platform.

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Data availability

All data in support of the findings of this study are available within the Article and its Supplementary Information. Syntheses of all drugs and selected most interesting agrochemicals identified in large-scale network searches are available for analysis and re-ranking at https://wasteresults.allchemy.net. User manuals are available in Supplementary Information section 2.

Code availability

The interactive Allchemy web application is freely available for academic users at https://waste.allchemy.net (given server capacity, to five concurrent academic users on a rolling basis and two-week slots). Reaction rules and the source code of Allchemy are proprietary.

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Acknowledgements

Development of the medicinal chemistry modules within the Allchemy platform (by A.W., R.R., S.S., M.M. and B.A.G.) has been supported by internal funds of Allchemy, Inc. D.K. and R.O. gratefully acknowledge funding from the National Science Centre, Poland (award 2016/23/B/ST5/03307) which supported the laboratory-scale syntheses described in this paper. Analysis of pathways and writing of the paper by B.A.G. was supported by the Institute for Basic Science, Korea (project code IBS-R020-D1). Development of the manufacturing processes for COVID-19 medications was supported by DARPA & CARES Act grant (HR0011-16-2-0029). The views, opinions and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the US Government.

Author information

Authors and Affiliations

  1. Allchemy, Highland, IN, USA

    Agnieszka Wołos, Rafał Roszak, Sara Szymkuć, Martyna Moskal & Bartosz A. Grzybowski

  2. Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland

    Agnieszka Wołos, Dominik Koszelewski, Ryszard Ostaszewski & Bartosz A. Grzybowski

  3. On Demand Pharmaceuticals, Rockville, MD, USA

    Brenden T. Herrera, Josef M. Maier, Gordon Brezicki, Jonathon Samuel, Justin A. M. Lummiss, D. Tyler McQuade & Luke Rogers

  4. Center for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan, Republic of Korea

    Bartosz A. Grzybowski

  5. Department of Chemistry, Ulsan Institute of Science and Technology (UNIST), Ulsan, Republic of Korea

    Bartosz A. Grzybowski

Authors
  1. Agnieszka Wołos
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Contributions

A.W., R.R., S.S., M.M. and B.A.G. designed and developed the Allchemy platform and performed the analyses and calculations described in the paper. D.K. performed the syntheses described in Fig. 4, with supervision from R.O. B.T.H. and J.S. developed the process for cisatracurium intermediate; G.B., J.M.M. and B.T.H. developed the process for propofol; and J.A.M.L., D.T.M. and L.R. developed the process for midazolam; all these are described in Fig. 5. B.A.G. conceived and supervised the research and wrote the paper with help from other authors.

Corresponding author

Correspondence to Bartosz A. Grzybowski.

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Competing interests

A.W., R.R., S.S., M.M. and B.A.G. are consultants and/or stakeholders of Allchemy, Inc. Allchemy software is the property of Allchemy, Inc., USA. On Demand Pharmaceuticals (ODP) is planning to file for US Food and Drug Administration (FDA) approvals on the flow processes described in this study. All queries about access options to Allchemy, including academic collaborations, should be sent to saraszymkuc@allchemy.net. On Demand Pharmaceuticals inquiries can be sent to lrogers@ondemandpharma.com.

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Nature thanks Fabrice Gallou, Frank Roschanger and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Statistics of the numbers of synthetic pathways.

a, Distribution of the numbers of synthetic paths for 69 drugs obtained from 189 waste molecules (as in Extended Data Fig. 4) within 7 synthetic generations. b, Distribution of number of synthetic paths for 127 drugs obtained from 189 waste molecules and 1,000 auxiliary, popular reagents (as in Fig. 3) within 8 synthetic generations. c, Histogram plotting the average number of pathways for drug targets of given molecular weights (mass ranges without drugs are omitted for clarity). Note the logarithmic vertical scale.

Extended Data Fig. 2 Arcs tracing 52 syntheses of the allyl alcohol agrochemical.

For large number of syntheses, the arc representation becomes messy (for example, some arcs extend beyond the window view)—nevertheless, this viewing modality enables rapid assessment of key intermediates (that is, hub nodes shared between different arcs).

Extended Data Fig. 3 Ranking of pathways according to process criteria.

Allchemy identified 42 pathways, originating from 11 waste substrates and leading to acetaminophen. a, Screenshot shows the settings of the Cost function ranking these syntheses only for the overall length of synthesis (that is, assigning high cost for the execution of each step). b, The shortest route, but it entails Friedel–Crafts acylation using toxic and environmentally problematic DCM solvent (in red font) for which no ‘greener’ replacement is suggested. c, Another scoring scheme, this time assigning more penalty for problematic solvents and reagents (the checkboxes in the bottom assign even higher penalties, +200, to assure that any pathways violating these conditions will be at the very bottom of the rankings; here extremely toxic solvents are penalized). d, The top-ranking synthesis is one step longer but does not involve Friedel–Crafts acylation in DCM. Similar rankings are available for all syntheses generated by the web app (https://waste.allchemy.net) and for results of large-scale calculations from the text available at https://wasteresults.allchemy.net. For the use of these software programmes, please see the user manual in Supplementary Information section 2 and watch Supplementary Video 1.

Extended Data Fig. 4 High-ranking syntheses of nine drugs and four agrochemicals entailing three or more steps and starting solely from recycled ‘waste’ molecules.

The waste substrates are shown in red in the inner circle. Small stars indicate geographical locations (Europe, Asia, North America; see Fig. 1 and Supplementary Information section 1) at which companies producing and/or recycling these substrates are located. Larger stars next to some of the drugs and agrochemicals indicate that they can be synthesized from waste substrates available at the same geographical location. For instance, the drug hexoprenaline can be made from waste recycled solely in Europe (both of its substrates, muconic acid and 3,4-dihydroxyphenylglycol, are denoted by small light-blue stars) and is thus marked with a large light-blue star. The agrochemical triethanolamine is denoted by two large stars (light blue, Europe; orange, North America) because it can be made from the same-continent wastes either in Europe or in North America (one of the waste substrates, ethylene glycol, is recycled on all three continents and, accordingly, is denoted by three small stars; the other substrate, formaldehyde, is recycled at industrial scales only in Europe and North America and thus has two small stars). Synthetic pathways to different targets are differentiated by colours. Within each pathway, reaction arrows for steps already reported in the literature are coloured whereas those without literature precedent are in black. Hazardous substances30,31 are marked by yellow ellipses (for example, formaldehyde). Details of all syntheses shown in this figure as well as other routes of each target are available at https://wasteresults.allchemy.net. Note that all pathways are top-scoring with exception of that for dapsone for which the software also found a two-step route starting from chlorobenzene waste, sulfuric acid and ammonia; however, this pathway is already known and patented and is not shown.

Extended Data Fig. 5 Additional details of highly ranked syntheses of more advanced drugs starting from waste substrates and few simple, auxiliary molecules used frequently in organic synthesis.

The figure accompanies and extends Fig. 3. All colour-coding schemes are the same. Details of all syntheses shown in this figure as well as other routes of each target are available at https://wasteresults.allchemy.net. HNPhth, phthalimide.

Extended Data Fig. 6 Two syntheses of Cysview top-ranked by different Cost functions.

Route traced by blue arrows was scored for overall length—it is concise but entails several harmful reagents (in red font) and intermediates (allyl alcohol in a yellow ellipse). Path traced by violet arrows is less convergent but uses greener reaction conditions (for details, see text).

Supplementary information

Supplementary Information

This file contains Supplementary Figures S1–S67, Supplementary Tables S1–S11 and Supplementary References.

Supplementary Video 1

The video illustrates how to perform calculations and analyse results in the web application available at https://waste.allchemy.net. This web application is freely available to academic users as specified under Code availability. To register a new account, send an e-mail to admin@allchemy.net from your academic address.

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Wołos, A., Koszelewski, D., Roszak, R. et al. Computer-designed repurposing of chemical wastes into drugs. Nature 604, 668–676 (2022). https://doi.org/10.1038/s41586-022-04503-9

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