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Design of self-assembly dipeptide hydrogels and machine learning via their chemical features
2019
Proceedings of the National Academy of Sciences of the United States of America
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
doi:10.1073/pnas.1903376116
pmid:31110004
pmcid:PMC6561259
fatcat:dv3tlgodp5c7fijbghreyoark4