The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
How to explore chemical space using algorithms and automation
Open accessAbstract
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.Automation can help in performing routine tasks quickly and consistently. Algorithms facilitate the searching of current knowledge. Combining the two could lead to a chemically intelligent approach to the discovery of not only new molecules but also novel and unpredictable reactivity.
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Deep learning for molecular design—a review of the state of the art
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Machine Learning for Catalysis Informatics: Recent Applications and Prospects
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Metadata
- Authors
- Gromski, P. S., Henson, A. B., Granda, J. M., & Cronin, L.
- Venue
- Nature Reviews Chemistry
- Published
- 2019-01-15
- Volume
- 3
- Issue
- 2
- Pages
- 119-128
- ISSN
- 2397-3358 (ISSN-L)
- OpenAlex
- W2908837618
- Cited by
- 245
- References
- 13
- FWCI
- 1.66
- Type
- journal article
- Language
- en
- Open access
- green
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