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Issue 17, 2019
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Selection of cost-effective yet chemically diverse pathways from the networks of computer-generated retrosynthetic plans

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Abstract

As the programs for computer-aided retrosynthetic design come of age, they are no longer identifying just one or few synthetic routes but a multitude of chemically plausible syntheses, together forming large, directed graphs of solutions. An important problem then emerges: how to select from these graphs and present to the user manageable numbers of top-scoring pathways that are cost-effective, promote convergent vs. linear solutions, and are chemically diverse so that they do not repeat only minor variations in the same chemical theme. This paper describes a family of reaction network algorithms that address this problem by (i) using recursive formulae to assign realistic prices to individual pathways and (ii) applying penalties to chemically similar strategies so that they are not dominating the top-scoring routes. Synthetic examples are provided to illustrate how these algorithms can be implemented – on the timescales of ∼1 s even for large graphs – to rapidly query the space of synthetic solutions under the scenarios of different reaction yields and/or costs associated with performing reaction operations on different scales.

Graphical abstract: Selection of cost-effective yet chemically diverse pathways from the networks of computer-generated retrosynthetic plans

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Publication details

The article was received on 16 Dec 2018, accepted on 24 Feb 2019 and first published on 01 Mar 2019


Article type: Edge Article
DOI: 10.1039/C8SC05611K
Chem. Sci., 2019,10, 4640-4651
  • Open access: Creative Commons BY-NC license
    All publication charges for this article have been paid for by the Royal Society of Chemistry

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    Selection of cost-effective yet chemically diverse pathways from the networks of computer-generated retrosynthetic plans

    T. Badowski, K. Molga and B. A. Grzybowski, Chem. Sci., 2019, 10, 4640
    DOI: 10.1039/C8SC05611K

    This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. Material from this article can be used in other publications provided that the correct acknowledgement is given with the reproduced material and it is not used for commercial purposes.

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