Energy Transfer Catalysis: A Highway to Molecular Complexity

Introduction

The development of novel synthetic methodologies is one of the most essential chemical research areas since the access to organic molecules is the foundation for many applied sciences (e.g. medicinal chemistry, materials science). In recent years, the construction of increasingly complex molecular scaffolds has gained significance, with a particular need for conformationally restricted, three-dimensional architectures.

Challenges in Synthesis

However, the synthesis of such molecular frameworks remains exceptionally challenging, limiting their application in other research branches. Consequently, revealing novel strategies to convert simple feedstock chemicals into complex building blocks has a beneficial impact on society as a whole.

Objectives of HighEnT

In HighEnT, we will disclose ground-breaking methodologies augmenting the synthetic toolbox of organic chemists focusing on expanding the chemical space to discover pharmacologically relevant structural motifs.

Key Strategies

The key to success is the creative and innovative utilization of the unique triplet excited state reactivity enabled by visible light-mediated EnT catalysis, providing a platform for unconventional retrosynthetic disconnections. Based on our broad expertise in this field, we will investigate diverse areas of EnT catalysis including:

1. Non-classical cycloadditions
2. Dearomative cycloadditions
3. σ-bond cleavage processes

In each domain, longstanding challenges will be solved with respect to product motifs, chemical space expansion, and mechanistic understanding.

Integration of Catalysis Techniques

Furthermore, we envision the merger of N-heterocyclic carbene (NHC) organocatalysis with EnT catalysis, opening otherwise locked reaction pathways.

Prediction Platform Development

Finally, to guide our product- and mechanism-oriented reaction discovery, we will develop and apply a novel prediction platform based on the interconnection of quantum chemical calculations and machine learning. We aim to provide easily accessible tools and statistical analyses that give new insights and impetus for reaction design.