用深度生成模型设计可合成的新型可电离脂质，加速mRNA药物研发。
A Deep Generative Model for the Design of Synthesizable Ionizable Lipids
- 基于深度生成模型，自动生成可合成的可电离脂质结构。
- 模型能提供实际可行的合成路径，提升候选分子的可实现性。
- 适合药物研发人员快速探索新型脂质载体，尤其用于mRNA疫苗。
脂质纳米颗粒（LNPs）在现代生物医学中至关重要，可保护mRNA免受快速降解，从而实现有效递送。其中，可电离脂质在保护RNA并促进其进入细胞质方面起关键作用。然而，设计可电离脂质过程复杂。传统方法难以高效探索分子空间。深度生成模型可加速该过程，但现有模型针对小分子设计，不适用于结构复杂的脂质。为此，我们开发了一种专为可电离脂质设计的深度生成模型，能够生成新颖且可合成的脂质结构，并提供使用可获得构建单元的合成路径，解决了可合成性问题。该进展有望简化脂质递送系统的开发，加快新治疗药物（包括mRNA疫苗和基因疗法）的部署。
原文摘要 · Abstract (English)
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.
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