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Generation of Cationic Nanoliposomes for the Efficient Delivery of In Vitro Transcribed Messenger RNA

Generation of Cationic Nanoliposomes for the Efficient Delivery of In Vitro Transcribed Messenger RNA

Published on: February 1, 2019

Data-balanced transformer for accelerated ionizable lipid nanoparticles screening in mRNA delivery.

Kun Wu^1,2, Xiulong Yang^1,2, Zixu Wang³

¹Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.

Briefings in Bioinformatics

|April 26, 2024

Summary

We developed TransLNP, a transformer model to accelerate ionizable lipid nanoparticle (LNP) screening for mRNA delivery. TransLNP accurately predicts transfection efficiency, reducing experimental time and costs.

Keywords:

data imbalance ionizable lipid nanoparticles transfection cliffs

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Area of Science:

Biotechnology
Computational Chemistry
Drug Delivery Systems

Background:

Ionizable lipid nanoparticles (LNPs) are crucial for mRNA delivery but screening is time-consuming and costly.
Efficient selection of LNPs is a major challenge in mRNA drug development.

Purpose of the Study:

To accelerate the early development of LNPs for mRNA delivery systems.
To introduce TransLNP, a transformer-based model for predicting LNP transfection efficiency.

Main Methods:

TransLNP utilizes coarse-grained atomic sequence and fine-grained spatial information.
Molecular pretraining via 3D coordinate reconstruction and atom prediction improves property prediction.
The BalMol block addresses data imbalance by smoothing label and feature distributions.

Main Results:

TransLNP outperforms state-of-the-art methods in transfection property prediction.
Identified 4267 molecular transfection cliffs, linking structural similarity to transfection efficiency differences.
Revealed key sources of prediction errors by analyzing molecular transfection cliffs.

Conclusions:

TransLNP significantly accelerates LNP screening for mRNA delivery.
The model provides insights into structure-transfection relationships.
Publicly available code, model, and data facilitate further research.