MEG mod：一种用于预测化学修饰siRNA敲除效率的多视图增强图神经网络
MEG-mod: A Multiview Enhanced Graph Neural Network for Knockdown Efficiency Prediction of Chemically Modified siRNA.
摘要(中文译文)
化学修饰对于提高siRNA的稳定性和敲低效率至关重要。然而，修饰类型和位置的组合复杂性使得理性设计变得困难。在此，我们提出MEG-mod，一种用于预测化学修饰siRNA敲低效率的深度学习框架。通过整合文献阅读智能体、人工整理和公共数据库资源，我们构建了一个扩展数据集。MEG-mod联合建模序列上下文、理化性质、化学修饰特征以及双链结构关系。基于结构感知Transformer的图神经网络捕捉双链siRNA内部的碱基配对和邻接依赖关系，而修饰-碱基融合模块则建模上下文依赖的修饰效应。MEG-mod优于现有方法，达到了0.9171的皮尔逊相关系数。注意力机制能够对关键修饰位置和修饰类型进行可解释分析，并提供与已报道实验设计一致的建议。MEG-mod作为公共网络服务器（https://lmmd.ecust.edu.cn/megmod/）提供使用，为化学修饰siRNA的预测和候选优先级排序提供了实用工具。
Abstract
Chemical modification is essential for improving the stability and knockdown efficiency of siRNAs. However, the combinatorial complexity of modification types and positions makes rational design difficult. Here, we propose MEG-mod, a deep learning framework for predicting the knockdown efficiency of chemically modified siRNAs. By integrating a literature-reading agent, manual curation, and public database resources, we construct an expanded data set. MEG-mod jointly models sequence context, physicochemical properties, chemical modification features, and duplex structural relationships. A structure-aware Transformer-based graph neural network captures base-pairing and adjacency dependencies within double-stranded siRNAs, while a modification-base fusion module models context-dependent modification effects. MEG-mod outperforms existing methods, achieving a pearson correlation coefficient of 0.9171. The attention mechanism enables interpretable analysis of key modification positions and modification types, and provides recommendations consistent with reported experimental designs. MEG-mod is available as a public web server (https://lmmd.ecust.edu.cn/megmod/), providing a practical tool for prediction and candidate prioritization of chemically modified siRNAs.
如何引用
AMA
Yuanting Chen, Mengyu Tong, Long Chen, Weihua Li, Keyun Zhu, Jiaying Li, et al. MEG-mod: A Multiview Enhanced Graph Neural Network for Knockdown Efficiency Prediction of Chemically Modified siRNA.. Journal of medicinal chemistry. 2026; doi:10.1021/acs.jmedchem.6c00411.
APA
Yuanting Chen, Mengyu Tong, Long Chen, Weihua Li, Keyun Zhu, Jiaying Li, et al (2026). MEG-mod: A Multiview Enhanced Graph Neural Network for Knockdown Efficiency Prediction of Chemically Modified siRNA.. Journal of medicinal chemistry. https://doi.org/10.1021/acs.jmedchem.6c00411
GB/T 7714
Yuanting Chen, Mengyu Tong, Long Chen, Weihua Li, Keyun Zhu, Jiaying Li, et al. MEG-mod: A Multiview Enhanced Graph Neural Network for Knockdown Efficiency Prediction of Chemically Modified siRNA.[J]. Journal of medicinal chemistry, 2026 doi:10.1021/acs.jmedchem.6c00411.
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