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Predicting outcomes of catalytic reactions using machine learning
[article]
2019
arXiv
pre-print
Predicting the outcome of a chemical reaction using efficient computational models can be used to develop high-throughput screening techniques. This can significantly reduce the number of experiments needed to be performed in a huge search space, which saves time, effort and expense. Recently, machine learning methods have been bolstering conventional structure-activity relationships used to advance understanding of chemical reactions. We have developed a model to predict the products of
arXiv:1908.10953v1
fatcat:h2esnehd4raqjeg6gahvmuxnrm