Jansen Stef A H, Vantomme Ghislaine, Meijer E W
Institute for Complex Molecular Systems and Laboratory of Macromolecular and Organic Chemistry, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands.
School of Chemistry and RNA Institute, UNSW, Sydney, Australia.
Angew Chem Int Ed Engl. 2025 Sep 15;64(38):e202509122. doi: 10.1002/anie.202509122. Epub 2025 Aug 22.
Inspired by the dynamic assembly of fibrillar proteins in biology, research on supramolecular polymers has progressed rapidly toward the development of synthetic multicomponent systems. In this review, we highlight recent advances in the study of supramolecular polymers in solution, with an emphasis on how combined computational and experimental approaches deepen our understanding of these systems. In particular, these studies have elucidated the mechanisms of protein aggregation and provided insights into the characteristics of synthetic systems. We discuss the classification of these polymers and systems, highlighting how their different interaction modes and microstructures give rise to diverse structural and functional properties. In addition, we outline the emerging role of machine learning as a powerful tool to navigate the inherent complexity of these systems, thereby enhancing strategies for rational design and characterization. This review highlights how computational approaches, from traditional modeling to emerging machine learning techniques, enable the experimental characterization and understanding of supramolecular polymer chemistry.
受生物学中纤维状蛋白质动态组装的启发，超分子聚合物的研究朝着合成多组分体系的发展迅速推进。在本综述中，我们重点介绍溶液中超分子聚合物研究的最新进展，着重阐述计算方法与实验方法相结合如何加深我们对这些体系的理解。特别是，这些研究阐明了蛋白质聚集的机制，并为合成体系的特性提供了见解。我们讨论了这些聚合物和体系的分类，强调了它们不同的相互作用模式和微观结构如何产生多样的结构和功能特性。此外，我们概述了机器学习作为一种强大工具在应对这些体系固有复杂性方面所发挥的新兴作用，从而增强了合理设计和表征的策略。本综述强调了从传统建模到新兴机器学习技术的计算方法如何实现超分子聚合物化学的实验表征和理解。
