Investigation of Arenes and Heteroarenes Nitration supported by High-Throughput Experimentation and Machine Learning

Access to the nitro functional group is a very common and longstanding transformation of interest in many fields of chemistry. However, the robustness and specificity of this transformation can remain challenging, particularly in the case of heteroarene nitration. From this observation, a large investigation was initiated to screen nitration conditions on various arenes and heteroarenes. The systematical and diverse study of both nitrating agents and activating reagents was conducted using high-throughput experimentation, to afford high quantity and high quality data generation. General trends have been identified and correlated to the electronic property of the heteroarene, notably the difficult nitration of electron-poor heteroarenes was highlighted. Original combinations of reagents were found to perform well in nitration reactions. The obtained data were also used to design a predictive tool relying on machine learning in order to provide the best nitration reaction conditions depending on the targeted substrate. The limited predictive efficiency obtained pointed out the importance of the diversification and the chemically relevant encoding of the data set.