数据平衡变压器用于在mRNA输送中加速电离性脂质纳米颗粒查.
Kun Wu1,2, Xiulong Yang1,2, Zixu Wang3
1Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.
Briefings in bioinformatics
|April 26, 2024
概括
我们开发了TransLNP,这是一个变压器模型,用于加速用于mRNA输送的可电离性脂质纳米粒子 (LNP) 选. TransLNP准确地预测了转换效率,减少了实验时间和成本.
科学领域:
- 生物技术是生物技术.
- 计算化学计算化学
- 药物输送系统 药物输送系统
背景情况:
- 电离性脂质纳米颗粒 (LNP) 对于mRNA输送至关重要,但查是耗时且昂贵的.
- 有效选择LNP是mRNA药物开发中的一个主要挑战.
研究的目的:
- 加速用于mRNA输送系统的LNP的早期开发.
- 引入TransLNP,这是一个基于变压器的模型,用于预测LNP传输效率.
主要方法:
- TransLNP利用粗粒度的原子序列和细粒度的空间信息.
- 通过3D坐标重建和原子预测进行分子预训可以改善属性预测.
- BalMol 块通过平滑标签和特征分布来解决数据不平衡.
主要成果:
- 在转变属性预测方面,TransLNP的表现优于最先进的方法.
- 确定了4267个分子转化悬崖,将结构相似性与转化效率差异联系起来.
- 通过分析分子转染悬崖,揭示了预测错误的关键来源.
结论:
- 翻译LNP显著加快了对mRNA输送的LNP查.
- 该模型提供了对结构-传递关系的洞察.
- 公共可用的代码,模型和数据有助于进一步的研究.
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