Design of self-assembly dipeptide hydrogels and machine learning via their chemical features

Significance Hydrogels maintain great potential for biomedical applications. However, predicting whether a chemical can form a hydrogel simply based on its chemical structure remains challenging. In this study, we developed a combinational approach to obtain a structurally diverse hydrogel library with over 2,000 peptides as a training dataset for machine learning. We calculated their chemical features, including topological and physicochemical properties, and utilized machine learning methods to predict the self-assembly behavior. Hydrogels that are self-assembled by peptides have attracted great interest for biomedical applications. However, the link between chemical structures of peptides and their corresponding hydrogel properties is still unclear. Here, we showed a combinational approach to generate a structurally diverse hydrogel library with more than 2,000 peptides and evaluated their corresponding properties. We used a quantitative structure–property relationship to calculate their chemical features reflecting the topological and physicochemical properties, and applied machine learning to predict the self-assembly behavior. We observed that the stiffness of hydrogels is correlated with the diameter and cross-linking degree of the nanofiber. Importantly, we demonstrated that the hydrogels support cell proliferation in culture, suggesting the biocompatibility of the hydrogel. The combinatorial hydrogel library and the machine learning approach we developed linked the chemical structures with their self-assembly behavior and can accelerate the design of novel peptide structures for biomedical use.