Design of self-assembly dipeptide hydrogels and machine learning via their chemical features
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- Fei Li
- Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet, Hong Kong;
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- Jinsong Han
- Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet, Hong Kong;
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- Tian Cao
- Department of Computer Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599;
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- William Lam
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong;
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- Baoer Fan
- South China Advanced Institute for Soft Matter Science and Technology, South China University of Technology, Guangzhou 510640, China
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- Wen Tang
- South China Advanced Institute for Soft Matter Science and Technology, South China University of Technology, Guangzhou 510640, China
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- Sijie Chen
- Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet, Hong Kong;
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- Kin Lam Fok
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong;
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- Linxian Li
- Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet, Hong Kong;
説明
<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>
収録刊行物
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- Proceedings of the National Academy of Sciences
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Proceedings of the National Academy of Sciences 116 (23), 11259-11264, 2019-05-20
Proceedings of the National Academy of Sciences
