Machine Learning-guided Prediction of Ionizable Amphiphilic Janus Dendrimers for mRNA Nanomedicine

Wan-Ting Cheng; Heng-Li Zheng; Peng-Yu Zhu; Ji-Na Hao; Da-Peng Zhang; Yong-Sheng Li

doi:10.1007/s10118-026-3563-3

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Machine Learning-guided Prediction of Ionizable Amphiphilic Janus Dendrimers for mRNA Nanomedicine

RESEARCH ARTICLE | Updated：2026-03-26

- Machine Learning-guided Prediction of Ionizable Amphiphilic Janus Dendrimers for mRNA Nanomedicine
- Machine Learning-guided Prediction of Ionizable Amphiphilic Janus Dendrimers for mRNA Nanomedicine
- Chinese Journal of Polymer Science 2026年44卷第4期页码：1154-1164
- Affiliations：
  
  Lab of Low-Dimensional Materials Chemistry, Key Laboratory for Ultraﬁne Materials of Ministry of Education, Frontier Science Center of the Materials Biology and Dynamic Chemistry, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
- Author bio：
  
  dapengzhang@ecust.edu.cn (D.P.Z.)
  ysli@ecust.edu.cn (Y.S.L.)
- Funds：
  
  This work was ﬁnancially supported by the National Key Research and Development Program of China (No. 2024YFF0508600), the National Natural Science Foundation of China (Nos. 22305081, 32571562 and 52572303), Leading Talents in Shanghai in 2018, Shanghai Sailing Program (No. 23YF1408600), 111 project (No. B14018), and Fundamental Research Funds for the Central Universities of China.;The authors declare no interest conflict.Electronic supplementary information (ESI) is available free of charge in the online version of this article at http://doi.org/10.1007/s10118-026-3563-3.Data supporting the findings of this study are available from the corresponding author upon reasonable request.
- DOI：10.1007/s10118-026-3563-3
  中图分类号：
- 收稿：2025-12-03，
  
  录用：2026-01-06，
  
  网络首发：2026-03-05，
  
  纸质出版：2026-04-05
- Accepted：
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Cheng, W. T.; Zheng, H. L.; Zhu, P. Y.; Hao, J. N.; Zhang, D. P.; Li, Y. S. Machine learning-guided prediction of ionizable amphiphilic Janus dendrimers for mRNA nanomedicine. Chinese J. Polym. Sci. 2026, 44, 1154–1164

Wan-Ting Cheng, Heng-Li Zheng, Peng-Yu Zhu, et al. Machine Learning-guided Prediction of Ionizable Amphiphilic Janus Dendrimers for mRNA Nanomedicine[J]. Chinese Journal of Polymer Science, 2026, 44(4): 1154-1164.
Cheng, W. T.; Zheng, H. L.; Zhu, P. Y.; Hao, J. N.; Zhang, D. P.; Li, Y. S. Machine learning-guided prediction of ionizable amphiphilic Janus dendrimers for mRNA nanomedicine. Chinese J. Polym. Sci. 2026, 44, 1154–1164 DOI： 10.1007/s10118-026-3563-3.

Wan-Ting Cheng, Heng-Li Zheng, Peng-Yu Zhu, et al. Machine Learning-guided Prediction of Ionizable Amphiphilic Janus Dendrimers for mRNA Nanomedicine[J]. Chinese Journal of Polymer Science, 2026, 44(4): 1154-1164. DOI： 10.1007/s10118-026-3563-3.

摘要

A tailored machine-learning (ML) framework was developed to investigate the relationship of molecular structures of ionizable amphiphilic Janus dendrimers (IAJDs) and their mRNA delivery effects. Count-based fingerprints and ChemBERTa embeddings improve accuracy and interpretability

revealing key motifs and enabling ML-guided design and optimization of IAJDs for mRNA delivery.

Abstract

The efficient and safe delivery of messenger RNA (mRNA) therapeutics remains a critical challenge for clinical translation

driving the need for advanced carrier design. Ionizable amphiphilic Janus dendrimers (IAJDs) represent a promising class of carriers; however

their structural complexity and limited available datasets hinder systematic exploration and optimization. In this study

we established a tailored machine-learning framework to investigate the structure-function relationships of IAJDs under a constrained data regime (

=231). Convention

al molecular fingerprints were found to be suboptimal for representing these macromolecules

motivating the adoption of count-based descriptors and systematic ablation analyses to disentangle the contributions of the substructural features. These experiments identified key functional motifs underlying transfection performance and provided interpretable insights into the IAJD design principles. Complementing these handcrafted descriptors

we further applied deep learning-based molecular embeddings

which captured higher-order chemical semantics and significantly improved predictive accuracy. Collectively

these advances demonstrate that both refined fingerprinting and representation learning approaches can overcome data limitations

enabling the reliable prediction of IAJD activity while offering mechanistic interpretability. This study illustrates the potential of data-driven strategies as hypothesis-generation and prioritization tools for the design of next-generation mRNA delivery systems.

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references

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