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Exploring Machine Learning Tools for the Prediction of the Stability of New Togni-type Reagents

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In the context of the prediction of the (in-)stability of chemical compounds using machine learning tools, we are often confronted with a basic issue: Whereas much information is available on stable (existing) compounds, little is known about compounds that might well exist, but that have not yet been successfully synthesized, or compounds that are inherently unstable (kinetically and thermodynamically). In the search for Togni-type reagents, many of them kinetically instable, the stability of the prospects can be assessed based on the transition state for the conversion to their non-hypervalent inactive isomer. In earlier work, we determined the barriers of conversion for over one-hundred reagents, still not enough information to train a tool such as a vector support machine. Here, instead, we focus on the early intermediate structures expressed along the isomerization pathway, i.e. transition state searches are replaced by finding (local) minima. Based on an array of 382 Togni-type reagents whose behaviour was known in advance, we show that it is possible to have the machine predict the intermediate form expressed. The approach introduced here can be used to make predictions on the stability and possibly also the reactivity of Togni-type reagents in general.
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Document Type: Research Article

Affiliations: 1: Department of Systems and Mathematical Science, Nanzan University, Nagoya, Japan;, Email: [email protected] 2: Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland;, Email: [email protected]

Publication date: December 1, 2019

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  • International Journal for Chemistry and Official Membership Journal of the Swiss Chemical Society (SCS) and its Divisions

    CHIMIA, a scientific journal for chemistry in the broadest sense, is published 10 times a year and covers the interests of a wide and diverse readership. Contributions from all fields of chemistry and related areas are considered for publication in the form of Review Articles and Notes. A characteristic feature of CHIMIA are the thematic issues, each devoted to an area of great current significance.

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