Development and application of ML tools for energy transfer catalysed photocycloadditions

• Applicant: Professor Dr. Frank Glorius
• Subject Area: Organic Molecular Chemistry - Synthesis and Characterisation ; Methods in Artificial Intelligence and Machine Learning
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 561351695

EnT-Mediated cycloaddition reactions are pivotal tools in synthetic chemistry for constructing complex molecular scaffolds, e.g., biologically relevant C(sp3)-rich three-dimensional molecular structures. However, the reactivity and selectivity of substrates in EnT-catalysed reactions remain challenging to predict, given the limited mechanistic understanding and scarcity of experimental data. This proposal aims to systematically address these challenges through three main objectives: (i) the curation of a comprehensive and balanced dataset of EnT-catalysed reactions (ii) the development of physically relevant descriptors to accurately capture triplet state reactivity, and (iii) development of data-driven models for predicting selectivity. By leveraging these models, we will provide valuable mechanistic insights that enable the generalisation of selectivity trends across a diverse array of reaction conditions and substrates. The predictive models will be furnished into user-friendly tools that will equip synthetic chemists with a robust predictive framework that enables precise prediction of reaction outcomes. Successful completion of this project will broaden the applicability of EnT catalysis, facilitating its adoption in complex molecular synthesis and advancing innovations in drug discovery and materials science.

• DFG Programme: Priority Programmes
• Subproject of: SPP 2363: Utilization and Development of Machine Learning for Molecular Applications – Molecular Machine Learning
• International Connection: Switzerland
• Partner Organisation: Schweizerischer Nationalfonds (SNF)
• Cooperation Partner: Professor Dr. Kjell Jorner, Ph.D.