To unlock the full promise of messenger (mRNA) therapies, expanding the toolkit of lipid nanoparticles is paramount. However, a pivotal component of lipid nanoparticle development that remains a bottleneck is identifying new ionizable lipids. Here we describe an accelerated approach to discovering effective ionizable lipids for mRNA delivery that combines machine learning with advanced combinatorial chemistry tools. Starting from a simple four-component reaction platform, we create a chemically diverse library of 584 ionizable lipids. We screen the mRNA transfection potencies of lipid nanoparticles containing those lipids and use the data as a foundational dataset for training various machine learning models. We choose the best-performing model to probe an expansive virtual library of 40,000 lipids, synthesizing and experimentally evaluating the top 16 lipids flagged. We identify lipid 119-23, which outperforms established benchmark lipids in transfecting muscle and immune cells in several tissues. This approach facilitates the creation and evaluation of versatile ionizable lipid libraries, advancing the formulation of lipid nanoparticles for precise mRNA delivery.

为了充分发挥信使 (mRNA) 疗法的潜力，扩展脂质纳米颗粒的工具包至关重要。然而，脂质纳米颗粒开发的一个关键组成部分仍然是一个瓶颈，那就是识别新的可电离脂质。在这里，我们描述了一种加速方法来发现有效的可电离脂质用于 mRNA 传递，该方法将机器学习与先进的组合化学工具相结合。从简单的四组分反应平台开始，我们创建了包含 584 种可电离脂质的化学多样性库。我们筛选含有这些脂质的脂质纳米颗粒的 mRNA 转染效力，并将这些数据用作训练各种机器学习模型的基础数据集。我们选择性能最佳的模型来探测包含 40,000 种脂质的庞大虚拟库，合成并通过实验评估标记的前 16 种脂质。我们鉴定出脂质 119-23，它在转染多种组织中的肌肉和免疫细胞方面优于既定的基准脂质。这种方法有助于创建和评估多功能可电离脂质库，推进脂质纳米粒子的配方以实现精确的 mRNA 递送。