Abstract
High-throughput combinatorial metal complex synthesis has emerged as a powerful tool for rapidly generating and screening diverse libraries of metal complexes, enabling accelerated discovery in fields such as catalysis, medicinal chemistry, and materials science. By systematically combining building blocks under mild and efficient conditions, researchers can explore broad chemical spaces, increasing the likelihood of identifying complexes with desired properties. This method streamlines hit identification and optimisation, especially when integrated with high-throughput screening and data-driven approaches like machine learning. Despite challenges such as scalability and purity control, recent advancements in automation and predictive modelling are enhancing the efficiency of combinatorial synthesis, opening new avenues for the development of metal-based catalysts, therapeutic agents, and functional materials.
Original language | English |
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Article number | e202420204 |
Number of pages | 15 |
Journal | Angewandte Chemie - International Edition |
Volume | 64 |
Issue number | 6 |
Early online date | 10 Jan 2025 |
DOIs | |
Publication status | Published - 3 Feb 2025 |
Bibliographical note
Publisher Copyright:© 2024 Wiley-VCH GmbH.
Keywords
- Combinatorial Synthesis
- High-Throughput
- Machine Learning
- Metal Complexes