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How to explore chemical space using algorithms and automation

Nature Reviews Chemistry volume 3, pages 119–128 (2019)Cite this article

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Abstract

Although extending the reactivity of a given class of molecules is relatively straightforward, the discovery of genuinely new reactivity and the molecules that result is a wholly more challenging problem. If new reactions can be considered unpredictable using current chemical knowledge, then we suggest that they are not merely new but also novel. Such a classification, however, requires an expert judge to have access to all current chemical knowledge or risks a lack of information being interpreted as unpredictability. Here, we describe how searching chemical space using automation and algorithms improves the probability of discovery. The former enables routine chemical tasks to be performed more quickly and consistently, while the latter uses algorithms to facilitate the searching of chemical knowledge databases. Experimental systems can also be developed to discover novel molecules, reactions and mechanisms by augmenting the intuition of the human expert. In order to find new chemical laws, we must seek to question current assumptions and biases. Accomplishing that involves using two areas of algorithmic approaches: algorithms to perform searches, and more general machine learning and statistical modelling algorithms to predict the chemistry under investigation. We propose that such a chemical intelligence approach is already being used and that, in the not-too-distant future, the automated chemical reactor systems controlled by these algorithms and monitored by a sensor array will be capable of navigating and searching chemical space more quickly, efficiently and, importantly, without bias. This approach promises to yield not only new molecules but also unpredictable and thus novel reactivity.

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Fig. 1: Searching chemical space.
Fig. 2: Creation of databases and the extraction of data.
Fig. 3: Searching for new reactivity, methods or properties.
Fig. 4: Optimizing reaction conditions.
Fig. 5: A projected 3D search space.
Fig. 6: A flow chart to assist in a strict definition of validity, newness and novelty.

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Acknowledgements

The authors gratefully acknowledge financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) (grant nos EP/H024107/1, EP/I033459/1, EP/J00135X/1, EP/J015156/1, EP/K021966/1, EP/K023004/1, EP/K038885/1, EP/L015668/1 and EP/L023652/1) and the European Research Council (ERC) (project 670467 SMART-POM).

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Nature Reviews Chemistry thanks M. Waller and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Authors and Affiliations

  1. WestCHEM, School of Chemistry, University of Glasgow, Glasgow, UK

    Piotr S. Gromski, Alon B. Henson, Jarosław M. Granda & Leroy Cronin

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  1. Piotr S. Gromski
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  2. Alon B. Henson
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  3. Jarosław M. Granda
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  4. Leroy Cronin
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Contributions

P.S.G. and A.B.H. contributed equally to the article. L.C. conceived the framework and developed the novelty algorithm presented here; L.C., A.B.H., P.S.G. and J.M.G. performed the literature review; and all the authors wrote the article. The authors thank N. A. B. Johnson for the artistic depiction used in the graphical abstract.

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Correspondence to Leroy Cronin.

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Gromski, P.S., Henson, A.B., Granda, J.M. et al. How to explore chemical space using algorithms and automation. Nat Rev Chem 3, 119–128 (2019). https://doi.org/10.1038/s41570-018-0066-y

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