Evidence map›Paper›PMID 40665077›Full record

ReviewNano convergence2025

Machine learning techniques for lipid nanoparticle formulation.

Hao Li, Yayi Zhao, Chenjie Xu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Nano convergence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Machine Learning-Driven QSAR Modeling for pKInternational journal of molecular sciences · 2026
    Article
  6. Review
  7. Article
  8. Review
  9. Review
  10. Article
  11. Review
  12. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hao LiDepartment of Biomedical Engineering, College of Biomedicine, City University of Hong Kong, Tat Chee Ave, Kowloon, Hong Kong SAR, China.
Yayi ZhaoDepartment of Biomedical Engineering, College of Biomedicine, City University of Hong Kong, Tat Chee Ave, Kowloon, Hong Kong SAR, China.
Chenjie XuDepartment of Biomedical Engineering, College of Biomedicine, City University of Hong Kong, Tat Chee Ave, Kowloon, Hong Kong SAR, China. chenjie.xu@cityu.edu.hk.

Funding

National Science Fund for Distinguished Young Scholars T2425004Research Grants Council, University Grants Committee CityU11202021, CityU11202222, CityU11100323, CityU11101324
6 · The paper itself

Abstract

A significant amount of effort has been poured into optimizing the delivery system that is demanded by novel therapeutic modalities. Lipid nanoparticle presents as a solution to transfect cells safely and efficiently with nucleic acid-based therapeutics. Among the components that make up the lipid nanoparticle, ionizable lipids are crucial for the transfection efficiency. Traditionally, the design of ionizable lipids relies on literature search and personal experience. With advancements in computer science, we argue that the use of machine learning can accelerate the design of ionizable lipids systematically. Assuming researchers in lipid nanoparticle synthesis may come from various backgrounds, an entry-level guide is needed to outline and summarize the general workflow of incorporating machine learning for those unfamiliar with it. We hope this can jumpstart the use of machine learning in their projects.

Indexed as

Ionizable lipids designLipid nanoparticle formulationMachine learningNucleic acid therapy

Identifiers

PMID40665077
PMCPMC12263518

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.