Artificial intelligence (AI)-enabled RNA therapeutic design: from molecular optimization to system-level evaluation.

Authors

Yongtian Wang, Jiaqi Wu

Published in

British journal of pharmacology. Oct 12, 2026. Epub Oct 12, 2026.

Abstract

RNA therapeutics are increasingly moving from empirical screening towards rational, computation-guided design. Recent advances in RNA language models, generative models and structure-aware learning have improved sequence representation, candidate generation and structure-constrained optimization. However, favourable molecular properties do not necessarily translate into desirable biological performance, because therapeutic outcomes also depend on modality-specific mechanisms, delivery and exposure, molecular interactions, cellular state, immune responses and network-level adaptation. Here, we review AI-enabled RNA therapeutic design through the specific lens of the interface between molecular optimization and system-level evaluation. We compare the design constraints of siRNAs, antisense oligonucleotides, aptamers, mRNAs and translation-oriented circRNAs, and examine the capabilities and current limitations of sequence-, generative- and structure-based computational models. We further discuss the data resources and perturbation representations required to connect molecular candidates with broader cellular-response models. In this context, virtual cells are considered as approximate computational frameworks for candidate prioritization and risk-oriented system-level assessment rather than complete digital replicas of living cells. Finally, we outline a conceptual generation-simulation-feedback-optimization framework for integrating molecular design with progressively higher-level evaluation. This perspective emphasizes the need for better interfaces among RNA design, pharmacological context and cellular-response modelling, rather than relying on molecular optimization alone.

PMID:
42864907
Bibliographic data and abstract were imported from PubMed on 13 Oct 2026.

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