Ionizable lipids are a key component of lipid nanoparticles, the leading nonviral messenger RNA delivery technology. Here, to advance the identification of ionizable lipids beyond current methods, which rely on experimental screening and/or rational design, we introduce lipid optimization using neural networks, a deep-learning strategy for ionizable lipid design. We created a dataset of >9,000 lipid nanoparticle activity measurements and used it to train a directed message-passing neural network for prediction of nucleic acid delivery with diverse lipid structures. Lipid optimization using neural networks predicted RNA delivery in vitro and in vivo and extrapolated to structures divergent from the training set. We evaluated 1.6 million lipids in silico and identified two structures, FO-32 and FO-35, with local mRNA delivery to the mouse muscle and nasal mucosa. FO-32 matched the state of the art for nebulized mRNA delivery to the mouse lung, and both FO-32 and FO-35 efficiently delivered mRNA to ferret lungs. Overall, this work shows the utility of deep learning for improving nanoparticle delivery.

可电离脂质是脂质纳米颗粒的关键成分，脂质纳米颗粒是领先的非病毒信使 RNA 递送技术。在这里，为了超越当前依赖于实验筛选和/或合理设计的方法，推进可电离脂质的鉴定，我们引入了使用神经网络的脂质优化，这是一种用于可电离脂质设计的深度学习策略。我们创建了一个 >9,000 脂质纳米颗粒活性测量数据集，并使用它来训练一个定向消息传递神经网络，用于预测具有不同脂质结构的核酸递送。使用神经网络的脂质优化预测了体外和体内的 RNA 递送，并外推到与训练集不同的结构。我们用计算机评估了 160 万种脂质，并确定了 FO-32 和 FO-35 两种结构，它们将 mRNA 局部递送到小鼠肌肉和鼻粘膜。FO-32 与雾化 mRNA 递送到小鼠肺的最新技术相匹配，FO-32 和 FO-35 都有效地将 mRNA 递送到雪貂肺。总体而言，这项工作显示了深度学习在改善纳米颗粒递送方面的效用。