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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.00928 |
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| _version_ | 1866910722936537088 |
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| author | Ou, Yuxuan Zhao, Jingyi Tripp, Austin Rasoulianboroujeni, Morteza Hernández-Lobato, José Miguel |
| author_facet | Ou, Yuxuan Zhao, Jingyi Tripp, Austin Rasoulianboroujeni, Morteza Hernández-Lobato, José Miguel |
| contents | Lipid nanoparticles (LNPs) are vital in modern biomedicine, enabling the effective delivery of mRNA for vaccines and therapies by protecting it from rapid degradation. Among the components of LNPs, ionizable lipids play a key role in RNA protection and facilitate its delivery into the cytoplasm. However, designing ionizable lipids is complex. Deep generative models can accelerate this process and explore a larger candidate space compared to traditional methods. Due to the structural differences between lipids and small molecules, existing generative models used for small molecule generation are unsuitable for lipid generation. To address this, we developed a deep generative model specifically tailored for the discovery of ionizable lipids. Our model generates novel ionizable lipid structures and provides synthesis paths using synthetically accessible building blocks, addressing synthesizability. This advancement holds promise for streamlining the development of lipid-based delivery systems, potentially accelerating the deployment of new therapeutic agents, including mRNA vaccines and gene therapies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00928 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Deep Generative Model for the Design of Synthesizable Ionizable Lipids Ou, Yuxuan Zhao, Jingyi Tripp, Austin Rasoulianboroujeni, Morteza Hernández-Lobato, José Miguel Machine Learning Artificial Intelligence Lipid nanoparticles (LNPs) are vital in modern biomedicine, enabling the effective delivery of mRNA for vaccines and therapies by protecting it from rapid degradation. Among the components of LNPs, ionizable lipids play a key role in RNA protection and facilitate its delivery into the cytoplasm. However, designing ionizable lipids is complex. Deep generative models can accelerate this process and explore a larger candidate space compared to traditional methods. Due to the structural differences between lipids and small molecules, existing generative models used for small molecule generation are unsuitable for lipid generation. To address this, we developed a deep generative model specifically tailored for the discovery of ionizable lipids. Our model generates novel ionizable lipid structures and provides synthesis paths using synthetically accessible building blocks, addressing synthesizability. This advancement holds promise for streamlining the development of lipid-based delivery systems, potentially accelerating the deployment of new therapeutic agents, including mRNA vaccines and gene therapies. |
| title | A Deep Generative Model for the Design of Synthesizable Ionizable Lipids |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.00928 |