Technical implications of new IUPAC elements in cheminformatics

Mayfield, John W.; Sayle, Roger A.

doi:10.1186/s13321-017-0196-0

Cited by 7 publications

(9 citation statements)

References 6 publications

(5 reference statements)

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“…280,281 Additionally, Pistachio represents a commercially available data set, based on USPTO data, electronic lab notebook (ELN) data, and information obtained from journals or other patent literature containing 13.3 million reactions. 282 Most recently, the open reaction database was created to build a standard format and open-access location for reaction data, which represents a large shift in fair data accessibility. 283 Figure 18 illustrates the information overlap between several of these reaction databases.…”

Section: Reaction Databasesmentioning

confidence: 99%

A Brief Introduction to Chemical Reaction Optimization

et al. 2023

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From the start of a synthetic chemist’s training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist to develop practical skills and some chemical intuition. This procedure is often kept long into a researcher’s career, as new recipes are developed based on similar reaction protocols, and intuition-guided deviations are conducted through learning from failed experiments. However, when attempting to understand chemical systems of interest, it has been shown that model-based, algorithm-based, and miniaturized high-throughput techniques outperform human chemical intuition and achieve reaction optimization in a much more time- and material-efficient manner; this is covered in detail in this paper. As many synthetic chemists are not exposed to these techniques in undergraduate teaching, this leads to a disproportionate number of scientists that wish to optimize their reactions but are unable to use these methodologies or are simply unaware of their existence. This review highlights the basics, and the cutting-edge, of modern chemical reaction optimization as well as its relation to process scale-up and can thereby serve as a reference for inspired scientists for each of these techniques, detailing several of their respective applications.

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Section: Reaction Databasesmentioning

confidence: 99%

A Brief Introduction to Chemical Reaction Optimization

et al. 2023

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“…Achieving reliable activation energy prediction is an integral step toward the complete prediction of kinetics. Machine learning, particularly deep learning, has recently emerged as a promising data-driven approach for reaction outcome prediction − and for use in organic retrosynthetic analysis. − These methods leverage massive data sets of organic reactions, such as Reaxys and Pistachio . However, the methods operate on qualitative data that indicate only the major reaction product and mostly lack any information regarding reaction rates.…”

mentioning

confidence: 99%

Deep Learning of Activation Energies

Grambow

Pattanaik

Green

2020

J. Phys. Chem. Lett.

154

189

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Quantitative predictions of reaction properties, such as activation energy, have been limited due to a lack of available training data. Such predictions would be useful for computer-assisted reaction mechanism generation and organic synthesis planning. We develop a template-free deep learning model to predict the activation energy given reactant and product graphs and train the model on a new, diverse data set of gas-phase quantum chemistry reactions. We demonstrate that our model achieves accurate predictions and agrees with an intuitive understanding of chemical reactivity. With the continued generation of quantitative chemical reaction data and the development of methods that leverage such data, we expect many more methods for reactivity prediction to become available in the near future.

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“…However, this could not be avoided at this stage, where the lack of stoichiometry is a general problem for other popular open-source organic reaction databases, such as USPTO 41 and Pistachio. 42…”

Section: Methodsmentioning

confidence: 99%