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

ABSTRACT: 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 prolife

SUBMITTER: Li F 

PROVIDER: S-EPMC6561259 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature