Machine learning techniques for lipid nanoparticle formulation
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 th...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-07-01
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| Series: | Nano Convergence |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40580-025-00502-4 |
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| Summary: | 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. Graphical Abstract |
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| ISSN: | 2196-5404 |