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An algorithmic framework for synthetic cost-aware decision making in molecular design

Abstract

Small molecules exhibiting desirable property profiles are often discovered through an iterative process of designing, synthesizing and testing sets of molecules. The selection of molecules to synthesize from all possible candidates is a complex decision-making process that typically relies on expert chemist intuition. Here we propose a quantitative decision-making framework, SPARROW, that prioritizes molecules for evaluation by balancing expected information gain and synthetic cost. SPARROW integrates molecular design, property prediction and retrosynthetic planning to balance the utility of testing a molecule with the cost of batch synthesis. We demonstrate, through three case studies, that the developed algorithm captures the non-additive costs inherent to batch synthesis, leverages common reaction steps and intermediates, and scales to hundreds of molecules.

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Fig. 1: Overview of SPARROW and its role within the molecular design cycle.
Fig. 2: SPARROW’s problem formulation.
Fig. 3: Demonstration of SPARROW’s ability to balance cost and reward on a 14-member candidate library of putative ASCT2 inhibitors.
Fig. 4: Results of SPARROW applied to an autonomous molecular design cycle from ref. 33.
Fig. 5: SPARROW’s proposed routes for case 2 with λ = [3, 1, 1].
Fig. 6: Example set of synthetic routes selected by SPARROW for case 3 using λ = [30, 1, 5].

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Data availability

SMILES and rewards used for all case studies32,33,37 can be found at github.com/coleygroup/sparrow/tree/main/examples. All results can be reproduced using included configuration files in the same repository52. Source data are provided with this paper.

Code availability

SPARROW is open source and can be found at github.com/coleygroup/sparrow (ref. 52). All code and retrosynthetic routes from ASKCOS used to generate the described results can be found at github.com/coleygroup/sparrow/tree/main/examples. Full candidate sets with configuration files are included in this repository both for reproducibility and as examples for use of SPARROW.

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Acknowledgements

This work was supported by the DARPA Accelerated Molecular Discovery program (contract no. HR00111920025) and the Office of Naval Research (grant no. N00014-21-1-2195). J.C.F. received additional support from the National Science Foundation Graduate Research Fellowship (grant no. 2141064). We are grateful to M. Stenta, M. Pasquini, D. Jimenez and T. Ziegler for participating in discussions that guided the development of SPARROW. We are also grateful to M. A. McDonald, B. Koscher, R. Canty and the remaining authors of ref. 33 for providing the candidate set for case 2. Finally, we thank B. Mahjour and A. Zhang for providing insight into the validity of reactions and conditions proposed by retrosynthetic software.

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Authors and Affiliations

  1. Department of Chemical Engineering, MIT, Cambridge, MA, USA

    Jenna C. Fromer & Connor W. Coley

  2. Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA

    Connor W. Coley

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  1. Jenna C. Fromer
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Contributions

C.W.C. and J.C.F. conceptualized the project, validated the method, analyzed results and wrote the paper. J.C.F. curated the data and wrote the software. C.W.C. supervised the work.

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Correspondence to
Connor W. Coley.

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Nature Computational Science thanks Mingyue Zheng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team.

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Supplementary information

Supplementary Information

Supplementary Fig. 1 and Tables 1–4.

Supplementary Data 1

Starting material prices from the ChemSpace API in October 2023 and March 2024 used in the first case study, plotted in Supplementary Fig. 1a.

Supplementary Data 2

Starting material prices from the ChemSpace API in October 2023 and March 2024 used in the second case study, plotted in Supplementary Fig. 1b.

Source data

Source Data Fig. 3

Numerical source data; reaction SMILES, scores and conditions

Source Data Fig. 4

Numerical source data for a–d

Source Data Fig. 5

Reaction SMILES, scores and conditions

Source Data Fig. 6

Reaction SMILES, scores and conditions

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Fromer, J.C., Coley, C.W. An algorithmic framework for synthetic cost-aware decision making in molecular design.
Nat Comput Sci (2024). https://doi.org/10.1038/s43588-024-00639-y

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  • Received: 20 December 2023

  • Accepted: 07 May 2024

  • Published: 17 June 2024

  • DOI: https://doi.org/10.1038/s43588-024-00639-y

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