Predictive Model for Site-Selective Aryl and Heteroaryl C–H Functionalization via Organic Photoredox Catalysis

Abstract

Direct C–H functionalization of aromatic compounds is a useful synthetic strategy that has garnered much attention because of its application to pharmaceuticals, agrochemicals, and late-stage functionalization reactions on complex molecules. On the basis of previous methods disclosed by our lab, we sought to develop a predictive model for site selectivity and extend this aryl functionalization chemistry to a selected set of heteroaromatic systems commonly used in the pharmaceutical industry. Using electron density calculations, we were able to predict the site selectivity of direct C–H functionalization in a number of heterocycles and identify general trends observed across heterocycle classes.

C−H Functionalization—Prediction of Selectivity in Iridium(I)‐Catalyzed Hydrogen Isotope Exchange Competition Reactions

“…Establishing convenient prediction models will be extremely beneficial. [36] We consider that with the help of improved computer-aided tools the prediction of late-stage functionalization of complex molecules can be significantly improved by taking this competition principle into account. Together with the understanding of directing-group-enhancing transformations recently described by Dai, Yu, and co-workers [37] this research could lead to a much broader use of C À H functionalization in lead optimization in the future.…”

Divergent Late‐Stage (Hetero)aryl C−H Amination by the Pyridinium Radical Cation

“…One‐electron oxidation of the arene to an arene radical cation with subsequent nucleophilic attack by a nitrogen nucleophile has been established through both electrochemical and photo‐oxidation . However, in both cases, the arene must be sufficiently electron‐rich for the oxidation to occur, which again limits the substrate scope to electron‐rich arenes …”

Machine Learning for Organic Synthesis: Are Robots Replacing Chemists?

“…The development of quantum‐chemical approaches has already opened some opportunities in this direction, and in many cases, the outcomes of experiments can be efficiently modeled in silico 1, 2, 3, 4, 5, 6. The advent of artificial intelligence (AI) algorithms to automatize, improve, and generalize predictions is gaining importance in this field, and several recent studies have been published in this area.…”

“…The ability to predict the outcome of complex chemical transformations has been al ong-standing challenge for chemists.The development of quantum-chemical approaches has already opened some opportunities in this direction, and in many cases,the outcomes of experiments can be efficiently modeled in silico. [1][2][3][4][5][6] Theadvent of artificial intelligence (AI) algorithms to automatize,improve,and generalize predictions is gaining importance in this field, and several recent studies have been published in this area. Fore xample,i n2 016, Aspuru-Guzik and co-workers reported their attempt to apply neural networks to basic reactions of alkenes and alkyl halides,a nd they were able to identify the correct reaction type for the majority of aset of textbook problems.…”