Nucleic Acid Foundation Models for siRNA Drug Design

Small interfering RNA (siRNA) therapeutics have become an important class of nucleic acid medicines. Meanwhile, nucleic acid foundation models and RNA language models have demonstrated promising performance in selected RNA-related tasks and may support siRNA drug design through transferable sequence representation learning.

This mini-review selectively surveys and critically evaluates representative nucleic acid foundation models and their potential roles in siRNA design, including representation learning, lowdata adaptation, multitask prediction, generative design, and multi-objective optimization. Particular attention is given to RNA language models such as RNA-FM and RiNALMo, while DNA, regulatory, unified DNA-RNA, and multimodal models are positioned according to their direct or contextual relevance to siRNA development.

Current models can be broadly grouped into DNA genomic language models, RNA language models, epigenomic/regulatory models, unified DNA-RNA models, and multimodal biomolecular foundation models. Across these categories, existing models provide complementary capabilities for sequence, structure, regulatory context, and cross-modal representation, but none yet constitutes a complete framework for therapeutic siRNA design.

Although these models show strong transferability across genomic and RNA tasks, their value for siRNA-specific therapeutic design remains insufficiently established. A central challenge is the mismatch between the natural RNA grammar and the therapeutic oligonucleotide grammar. Real siRNA development requires simultaneous consideration of efficacy, specificity, chemical modification, stability, immunogenicity, off-target effects, delivery compatibility, and manufacturability.

Future progress will depend on modification-aware representations, therapeutic oligonucleotide datasets, multimodal integration, interpretable and experimentally testable outputs, robust cross-dataset and cross-protocol evaluation, and Pareto-based multi-objective selection. These requirements define a practical roadmap for moving from transferable RNA representations to therapeutically credible siRNA design systems.