The Computational Journey of siRNA Silencing Efficiency: Resources,Methods, and Future Directions

Accurate prediction of small interfering RNA (siRNA) silencing efficiency is critical for accelerating nucleic acid drug development. Over the past two decades, the field has transitioned from empirical sequence-derived heuristics to data-driven methods such as deep learning and graph neural networks. In this review, we systematically discuss current research from three integrated perspectives: data resources, design rules, and computational algorithms. Firstly, we summarize strengths, biases, and gaps in existing datasets, including low-throughput saturation scanning data, high-throughput panels, and specialized repositories for chemically modified siRNAs. Next, we outline the evolution of siRNA design principles, from classical guidelines such as GC content windows, terminal thermodynamic asymmetry, and positional base preferences, to more sophisticated thermodynamic-structural features incorporating target mRNA accessibility, and finally, to advanced clinical GalNAc-siRNA strategies involving seed destabilization and stereochemically purified backbones. Regarding computational algorithms, the progression is categorized into three stages: early linear regression models, traditional machine learning approaches, and modern deep learning frameworks, alongside various specialized algorithms tailored for chemically modified siRNAs. Current studies demonstrate steadily improving prediction accuracy, yet significant challenges persist, including insufficient data coverage for disease-relevant targets and hard-to-transfect cells, limited paired measurements of bare versus modified sequences, and inadequate integration of off-target effects and immunogenicity metrics. Moving forward, there is an urgent need to establish highquality, dose- and time-resolved datasets across diverse tissues, embed biophysical priors into interpretable deep-learning architectures, and adopt multi-task modeling approaches to optimize efficiency, safety, and delivery simultaneously. Collectively, these advancements promise to propel siRNA design beyond liver-centric applications towards precision therapeutics targeting multiple tissues and diseases.