MEG-mod: A Multiview Enhanced Graph Neural Network for Knockdown Efficiency Prediction of Chemically Modified siRNA

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Pearson correlation coefficient of 0.9171

• Primary Outcome: Knockdown efficiency prediction accuracy

• Nnt Or Sensitivity: Outperformed existing methods (specific metrics not detailed)

• Confidence Interval: Not reported

Clinical Application

High feasibility with web server implementation enabling broad access Relevant to Australian biotechnology sector and research institutions developing RNA therapeutics, though TGA approval pathways for siRNA drugs remain evolving Researchers and pharmaceutical companies developing siRNA therapeutics

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.

References

1. 1.Chen, Y., Tong, M., Chen, L., Li, W., Zhu, K., Li, J., Tang, Y., & Liu, G. (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