The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Design of self-assembly dipeptide hydrogels and machine learning via their chemical features
Open accessAbstract
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.
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“However, their application to soft materials remains challenging because of complex, multiscale structure–property relationships4–6.”
From Data-driven de novo design of super-adhesive hydrogels (2025) · cites this paper as [4]
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Cited by
Sorted by citations
Data-driven de novo design of super-adhesive hydrogels
Liao, H., Hu, S., Yang, H., Wang, L., Tanaka, S., Takigawa, I., Li, W., Fan, H., & Gong, J. P. (2025) · Nature · doi:10.1038/s41586-025-09269-4 · Cited by 169
“However, their application to soft materials remains challenging because of complex, multiscale structure–property relationships 4–6 .”
Accelerating the prediction and discovery of peptide hydrogels with human-in-the-loop
Xu, T., Wang, J., Zhao, S., Chen, D., Zhang, H., Fang, Y., Kong, N., Zhou, Z., Li, W., & Wang, H. (2023) · Nature Communications · doi:10.1038/s41467-023-39648-2 · Cited by 132
Metadata
- Authors
- Li, F., Han, J., Cao, T., Lam, W., Fan, B., Tang, W., Chen, S., Fok, K. L., & Li, L.
- Affiliations
- Karolinska Institutet (SE)
- University of North Carolina at Chapel Hill (US)
- Chinese University of Hong Kong (HK)
- South China University of Technology (CN)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2019-05-20
- Volume
- 116
- Issue
- 23
- Pages
- 11259-11264
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC6561259
- OpenAlex
- W2945892680
- Cited by
- 198
- References
- 3
- Citations by year
- 2026: 33 · 2025: 39 · 2024: 32 · 2023: 36 · 2022: 24 · 2021: 16 · 2020: 14 · 2019: 4
- FWCI
- 4.11
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (81771639)
- Research Grants Council, University Grants Committee (14127316, 14129316)
- Topics
- Physical Sciences › Materials Science › Biomaterials › Supramolecular Self-Assembly in Materials
- Physical Sciences › Materials Science › Biomaterials › Hydrogels: synthesis, properties, applications
- Physical Sciences › Materials Science › Biomaterials › Silk-based biomaterials and applications
- Keywords
- peptide self-assembly, QSPR, chemical features, machine learning prediction, hydrogel stiffness, nanofiber diameter, crosslink density, cell proliferation, biocompatibility
- MeSH terms
- Machine Learning, Biocompatible Materials, Dipeptides, Humans, Hydrogels, Cell Proliferation, Nanofibers
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- CoChat. (2026). Citation check: Design of self-assembly dipeptide hydrogels and machine learning via their chemical features. https://app.cochat.ai/papers/10.1073/pnas.1903376116
- The paper
- Li, F., Han, J., Cao, T., Lam, W., Fan, B., Tang, W., Chen, S., Fok, K. L., & Li, L. (2019). Design of self-assembly dipeptide hydrogels and machine learning via their chemical features. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1903376116
