RNA-RNA Interaction Prediction in Therapeutics and Drug Discovery

RNA-based therapeutics have emerged as a transformative modality in modern medicine, enabling precise modulation of gene expression and offering new treatment strategies for previously intractable diseases. Central to the success and safety of many RNA therapeutics is a detailed understanding of RNA-RNA interactions (RRIs), including both intended target binding and unintended off-target effects. Computational prediction of RRIs plays a critical role in the design, optimization, and validation of therapeutic RNAs such as antisense oligonucleotides, small interfering RNAs, messenger RNAs, and CRISPR-associated RNAs. This chapter examines the role of RRI prediction in therapeutic and drug discovery pipelines, highlighting computational strategies, deep learning models, and validation frameworks. Examples of applications in oncology, infectious diseases, and genetic disorders illustrate how accurate RRI modeling supports efficacy, safety assessment and contribute to evidence generation for therapeutic development. Challenges and future directions for clinically deployable RRI prediction systems are also discussed.