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Recent advances in RNA language models, generative models and structure\u2010aware learning have improved sequence representation, candidate generation and structure\u2010constrained optimization. However, favourable molecular properties do not necessarily translate into desirable biological performance, because therapeutic outcomes also depend on modality\u2010specific mechanisms, delivery and exposure, molecular interactions, cellular state, immune responses and network\u2010level adaptation. Here, we review AI\u2010enabled RNA therapeutic design through the specific lens of the interface between molecular optimization and system\u2010level evaluation. We compare the design constraints of siRNAs, antisense oligonucleotides, aptamers, mRNAs and translation\u2010oriented circRNAs, and examine the capabilities and current limitations of sequence\u2010, generative\u2010 and structure\u2010based computational models. We further discuss the data resources and perturbation representations required to connect molecular candidates with broader cellular\u2010response models. In this context, virtual cells are considered as approximate computational frameworks for candidate prioritization and risk\u2010oriented system\u2010level assessment rather than complete digital replicas of living cells. Finally, we outline a conceptual generation\u2013simulation\u2013feedback\u2013optimization framework for integrating molecular design with progressively higher\u2010level evaluation. This perspective emphasizes the need for better interfaces among RNA design, pharmacological context and cellular\u2010response modelling, rather than relying on molecular optimization alone.<\/jats:p>","DOI":"10.1111\/bph.70712","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T05:30:32Z","timestamp":1791869432000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence (AI)\u2010enabled RNA therapeutic design: from molecular optimization to system\u2010level evaluation"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2766-3106","authenticated-orcid":false,"given":"Yongtian","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science Northwestern Polytechnical University  Xi'an China"},{"name":"Shenzhen Research Institute of Northwestern Polytechnical University, Sanhang Science &amp; Technology Building  Shenzhen City China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8796-6263","authenticated-orcid":false,"given":"Jiaqi","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science Northwestern Polytechnical University  Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,12]]},"reference":[{"key":"e_1_2_14_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf549"},{"key":"e_1_2_14_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-019-0193-y"},{"key":"e_1_2_14_4_1","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2017.0237"},{"key":"e_1_2_14_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2020.11.037"},{"key":"e_1_2_14_6_1","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/s41592-021-01100-y","article-title":"Low\u2010N protein engineering with data\u2010efficient deep learning","volume":"18","author":"Biswas S.","year":"2021","journal-title":"Nature Methods"},{"key":"e_1_2_14_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bth378"},{"key":"e_1_2_14_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0667-5"},{"key":"e_1_2_14_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms21228420"},{"key":"e_1_2_14_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41573-021-00283-5"},{"key":"e_1_2_14_11_1","doi-asserted-by":"crossref","unstructured":"Chen J. Hu Z. Sun S. Tan Q. Wang Y. Yu Q.Zong L. Hong L. Xiao J. 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