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A Structure-Based Platform for Predicting Chemical Reactivity

preprint
submitted on 15.10.2019, 13:50 and posted on 21.10.2019, 16:40 by Frederik Sandfort, Felix Strieth-Kalthoff, Marius Kühnemund, Christian Beecks, Frank Glorius
Despite their enormous potential, machine learning methods have only found limited application in predicting reaction outcomes, as current models are often highly complex and, most importantly, are not transferrable to different problem sets. Herein, we present the direct utilization of Lewis structures in a machine learning platform for diverse applications in organic chemistry. Therefore, an input based on multiple fingerprint features (MFF) as a universal molecular representation was developed and used for problem sets of increasing complexity: First, molecular properties across a diverse array of molecules could be predicted accurately. Next, reaction outcomes such as stereoselectivities and yields were predicted for experimental data sets that were previously evaluated using (complex) problem-oriented descriptor models. As a final application, a systematic high-throughput data set showed good correlation when using the MFF model, which suggests that this approach is general and ready for immediate adoption by chemists.

Funding

Fonds der Chemischen Industrie

Deutsche Forschungsgemeinschaft (SPP2102)

Deutsche Forschungsgemeinschaft (Leibniz Award)

History

Email Address of Submitting Author

glorius@uni-muenster.de

Institution

Westfaelische Wilhelms-Universitaet Muenster

Country

Germany

ORCID For Submitting Author

0000-0002-0648-956X

Declaration of Conflict of Interest

The authors declare no conflict of interest.

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