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Here, we employ machine learning (ML)\u2010assisted structure\u2013activity relationship (SAR) analysis to guide the engineering of a library of 120 degradable ionizable lipids, enabling the rational design of fluorinated aromatic lipid nanoparticles (LNPs) optimized for combinatorial RNA delivery. ML\u2010based feature\u2010importance analysis prioritizes \u2013CF\n                    <jats:sub>3<\/jats:sub>\n                    aromatic tails, and molecular dynamics simulations confirm that these tails enhance RNA binding and nanoparticle stability. The resulting A\n                    <jats:sub>2<\/jats:sub>\n                    T\n                    <jats:sub>5<\/jats:sub>\n                    \u2010s LNPs, functionalized with lactobionic acid for selective HCC targeting, enable efficient co\u2010delivery of p53 mRNA and Nrf2 siRNA. This strategy restores ferroptosis and induces apoptosis in sorafenib\u2010resistant HCC by suppressing SLC7A11, leading to marked tumor inhibition. Our study demonstrates an ML\u2010assisted LNP optimization strategy, advancing precision RNA therapeutics to overcome resistance in refractory liver cancer.\n                  <\/jats:p>","DOI":"10.1002\/adma.202519473","type":"journal-article","created":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T09:52:13Z","timestamp":1767952333000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Optimized Lipid Nanoparticles for Co\u2010Delivery of mRNA and siRNA Therapeutics in Refractory Liver Cancer"],"prefix":"10.1002","volume":"38","author":[{"given":"Yuqin","family":"Liao","sequence":"first","affiliation":[{"name":"Department of Radiology Zhongnan Hospital of Wuhan University School of Pharmaceutical Sciences Wuhan University  Wuhan China"},{"name":"State Key Laboratory of Drug Research &amp; Center of Pharmaceutics Shanghai Institute of Materia Medica Chinese Academy of Sciences  Shanghai 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