Investigation of arene and heteroarene nitration supported by high-throughput experimentation and machine learning

Abstract

Access to the nitro functional group is a widespread 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. Based on this observation, a comprehensive investigation was initiated to screen nitration conditions on various arenes and heteroarenes. A systematic 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 were identified and correlated with the electronic properties of the heteroarenes; 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 diversification and chemically relevant encoding of the data set.