Lipid discovery for mRNA delivery guided by machine learning - News & views

Aug 2, 2026

Machine learning combined with high-throughput synthesis is used to accelerate the development of ionizable cationic lipids for nanoparticle-based messenger RNA delivery. The article explains why lipid nanoparticle technology is essential for protecting mRNA from degradation and enabling intracellular delivery, and why identifying lipids with strong potency and safety remains challenging. A reported workflow creates large combinatorial libraries, measures in vitro transfection via reporter mRNA, trains models on the resulting data, and advances screening toward lipids that outperform approved references in local and systemic delivery.

LanguageEnglish

CategoryResearch & Report

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Pages3

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Drug delivery

Machine learning and high-throughput chemistry for ionizable cationic lipids

LNP requirements for intracellular mRNA delivery

Library screening and machine-learning-guided discovery

Lipid discovery for mRNA delivery guided by machine learning Citation for published version (APA): van der Meel, R. , Grisoni, F. , & Mulder, W. J. M. (2024) . Lipid discovery for mRNA delivery guided by machine learning. Nature Materials, 23(7), 880-881 . [https://doi.org/10.1038/s41563-024-01934-9](https://doi.org/10.1038/s41563-024-01934-9) Document license: TAVERNE DOI: 10.1038/s41563-024-01934-9 Document status and date: Published: 01/07/2024 Document Version: Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement: [www.tue.nl/taverne](www.tue.nl/taverne) Take down policy If you believe that this document breaches copyright please contact us at: [openaccess@tue.nl](openaccess@tue.nl) providing details and we will investigate your claim. Download date: 12. Jun. 2025 | News & views
Drug delivery | [https://doi.org/10.1038/s41563-024-01934-9](https://doi.org/10.1038/s41563-024-01934-9) | | --- | --- | | Lipid discovery formRNA delivery guided by machine learning
Roy van der Meel, Francesca Grisoni & Willem J. M. Mulder Check for updates | | Combining machine learning with high-throughput synthesis expeditesionizable cationic lipid development for nanoparticle-based messenger RNA delivery. Sophisticated delivery nanotechnology is a prerequisite for the successful therapeutic application of messenger RNA (mRNA), as it prevents premature degradation and ensures intracellular delivery1. Among the various delivery platforms, lipid nanoparticle (LNP) technology has been clinically translated and deployed at large scale. LNPs typically consist of phospholipids, cholesterol and polyethylene glycol-functionalized lipids. In addition, critical formRNA incorporationin LNPs are ionizable cationic lipids. When charged at low pH, these molecules enable efficient mRNA complexation and induce endosomalescape following cell uptake. An elaborate effort to identify effective ionizable cationic lipids facilitated the clinical translation of LNP-based therapeutics2–4, including the first ever small interfering RNA (siRNA) drug5 and the COVID-19 mRNA vaccines6. Despite these successes, unlocking the potential of RNA therapeutics beyond hepatic and vaccination applications requires potent ionizable cationic lipids with superb safety profiles. However, their development is slowed down by the vast structural design space and LNP screening limitations. Now, writing in Nature Materials, Li et al.7 report on an approa

FAQs

Why are lipid nanoparticles crucial for messenger RNA delivery?

Lipid nanoparticles protect mRNA from premature degradation and support intracellular delivery. Their ionizable cationic lipids complex mRNA at low pH and promote endosomal escape after uptake.

What challenge limits the development of ionizable cationic lipids?

The structural design space is vast, and conventional LNP screening is limited. This slows down identifying lipids with both high potency and excellent safety profiles.

How does the reported approach use machine learning?

A combinatorial lipid library is synthesized and tested using luciferase reporter mRNA to quantify transfection. The resulting dataset trains machine-learning models, which then guide further screening to discover superior ionizable cationic lipids.

Aug 2, 2026

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