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

DOI Web Site Web Site 1 Citations
  • Fei Li
    Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet, Hong Kong;
  • Jinsong Han
    Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet, Hong Kong;
  • Tian Cao
    Department of Computer Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599;
  • William Lam
    School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong;
  • Baoer Fan
    South China Advanced Institute for Soft Matter Science and Technology, South China University of Technology, Guangzhou 510640, China
  • Wen Tang
    South China Advanced Institute for Soft Matter Science and Technology, South China University of Technology, Guangzhou 510640, China
  • Sijie Chen
    Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet, Hong Kong;
  • Kin Lam Fok
    School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong;
  • Linxian Li
    Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet, Hong Kong;

Description

<jats:title>Significance</jats:title> <jats:p>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.</jats:p>

Journal

Citations (1)*help

See more

Report a problem

Back to top