From algorithm to application: AI-powered design of ionizable lipids for mRNA delivery

Danhong Liang , Chi Xu , Haijun Li , Xinpeng Ma , Peng Gao , Bo Ying

BME Horizon ›› 2026, Vol. 4 ›› Issue (3) : 202603

PDF (768KB)
BME Horizon ›› 2026, Vol. 4 ›› Issue (3) :202603 DOI: 10.70401/bmeh.2026.0026
Mini review
research-article
From algorithm to application: AI-powered design of ionizable lipids for mRNA delivery
Author information +
History +
PDF (768KB)

Abstract

Artificial intelligence (AI) is revolutionizing the design of ionizable lipids, the pivotal components of lipid nanoparticles (LNPs) for messenger RNA (mRNA) delivery, enabling efficient exploration of vast chemical space of ionizable lipids beyond the reach of traditional methods. This mini-review explores the burgeoning field of AI-powered design and optimization of ionizable lipids for mRNA delivery. We also discuss the critical role of high-throughput experimental strategies, particularly barcoding coupled with next-generation sequencing, in generating the large-scale in vivo datasets for model training. Finally, we discuss current challenges, including data quality and the necessity for domain-specific modeling strategies, and present a future outlook on the integration of AI with scientific computing for LNP research.

Keywords

Lipid nanoparticle / ionizable lipid / molecular design / machine learning / artificial intelligence

Cite this article

Download citation ▾
Danhong Liang, Chi Xu, Haijun Li, Xinpeng Ma, Peng Gao, Bo Ying. From algorithm to application: AI-powered design of ionizable lipids for mRNA delivery. BME Horizon, 2026, 4 (3) : 202603 DOI:10.70401/bmeh.2026.0026

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Gote V, Bolla PK, Kommineni N, Butreddy A, Nukala PK, Palakurthi SS, et al. A comprehensive review of mRNA vaccines. Int J Mol Sci. 2023; 24(3): 2700.

[2]

Zhang Y, Sun C, Wang C, Jankovic KE, Dong Y . Lipids and lipid derivatives for RNA delivery. Chem Rev. 2021; 121(20): 12181-12277.

[3]

Cullis PR, Felgner PL . The 60-year evolution of lipid nanoparticles for nucleic acid delivery. Nat Rev Drug Discov. 2024; 23(9): 709-722.

[4]

Eygeris Y, Gupta M, Kim J, Sahay G . Chemistry of lipid nanoparticles for RNA delivery. Acc Chem Res. 2022; 55(1): 2-12.

[5]

Hald Albertsen C, Kulkarni JA, Witzigmann D, Lind M, Petersson K, Simonsen JB . The role of lipid components in lipid nanoparticles for vaccines and gene therapy. Adv Drug Deliv Rev. 2022; 188: 114416.

[6]

Han X, Zhang H, Butowska K, Swingle KL, Alameh MG, Weissman D, et al. An ionizable lipid toolbox for RNA delivery. Nat Commun. 2021; 12: 7233.

[7]

Gyanani V, Goswami R . Key design features of lipid nanoparticles and electrostatic charge-based lipid nanoparticle targeting. Pharmaceutics. 2023; 15(4): 1184.

[8]

Maier MA, Jayaraman M, Matsuda S, Liu J, Barros S, Querbes W, et al. Biodegradable lipids enabling rapidly eliminated lipid nanoparticles for systemic delivery of RNAi therapeutics. Mol Ther. 2013; 21(8): 1570-1578.

[9]

Wasungu L, Hoekstra D . Cationic lipids, lipoplexes and intracellular delivery of genes. J Control Release. 2006; 116(2): 255-264.

[10]

Kulkarni JA, Cullis PR, van der Meel R . Lipid nanoparticles enabling gene therapies: From concepts to clinical utility. Nucleic Acid Ther. 2018; 28(3): 146-157.

[11]

Barbier AJ, Jiang AY, Zhang P, Wooster R, Anderson DG . The clinical progress of mRNA vaccines and immunotherapies. Nat Biotechnol. 2022; 40(6): 840-854.

[12]

Jayaraman M, Ansell SM, Mui BL, Tam YK, Chen J, Du X, et al. Maximizing the potency of siRNA lipid nanoparticles for hepatic gene silencing in vivo. Angew Chem Int Ed. 2012; 51(34): 8529-8533.

[13]

Hashiba K, Taguchi M, Sakamoto S, Otsu A, Maeda Y, Suzuki Y, et al. Impact of lipid tail length on the organ selectivity of mRNA-lipid nanoparticles. Nano Lett. 2024; 24(41): 12758-12767.

[14]

Sabnis S, Kumarasinghe ES, Salerno T, Mihai C, Ketova T, Senn JJ, et al. A novel amino lipid series for mRNA delivery: Improved endosomal escape and sustained pharmacology and safety in non-human Primates. Mol Ther. 2018; 26(6): 1509-1519.

[15]

Ansell SM, Du X, inventors. Novel lipids and lipid nanoparticle formulations for delivery of nucleic acids. World patent WO2017075531 A1. 2017.

[16]

Benenato KE, Kumarasinghe ES, Cornebise M, inventors. Novel lipids and lipid nanoparticle formulations for delivery of nucleic acids. Canadian patent application CA 2998810 A1. 2017.

[17]

Payne JE, Chivukula P, Tanis SP, Karmali P, inventors. Ionizable cationic lipid for RNA delivery. United States patent US 9670152 B2. 2018.

[18]

Han X, Alameh MG, Xu Y, Palanki R, El-Mayta R, Dwivedi G, et al. Optimization of the activity and biodegradability of ionizable lipids for mRNA delivery via directed chemical evolution. Nat Biomed Eng. 2024; 8(11): 1412-1424.

[19]

Luong KD, Singh A . Application of transformers in cheminformatics. J Chem Inf Model. 2024; 64(11): 4392-4409.

[20]

O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR . Open Babel: An open chemical toolbox. J Cheminform. 2011; 3: 33.

[21]

Yap CW . PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. J Comput Chem. 2011; 32(7): 1466-1474.

[22]

Rogers D, Hahn M . Extended-connectivity fingerprints. J Chem Inf Model. 2010; 50(5): 742-754.

[23]

Carhart RE, Smith DH, Venkataraghavan R . Atom pairs as molecular features in structure-activity studies: Definition and applications. J Chem Inf Comput Sci. 1985; 25(2): 64-73.

[24]

Durant JL, Leland BA, Henry DR, Nourse JG . Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci. 2002; 42(6): 1273-1280.

[25]

Weininger D . SMILES, a chemical language and information system. 1. J Chem Inf Comput Sci. 1988; 28(1): 31-36.

[26]

Heller SR, McNaught A, Pletnev I, Stein S, Tchekhovskoi D . InChI, the IUPAC international chemical identifier. J Cheminform. 2015; 7: 23.

[27]

Zhang K, Yang X, Wang Y, Yu Y, Huang N, Li G, et al. Artificial intelligence in drug development. Nat Med. 2025; 31(1): 45-59.

[28]

Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP . Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics. 2023; 15(7): 1916.

[29]

Catacutan DB, Alexander J, Arnold A, Stokes JM . Machine learning in preclinical drug discovery. Nat Chem Biol. 2024; 20(8): 960-973.

[30]

Zeng X, Wang F, Luo Y, Kang SG, Tang J, Lightstone FC, et al. Deep generative molecular design reshapes drug discovery. Cell Rep Med. 2022; 3(12): 100794.

[31]

van den Broek RL, Patel S, van Westen GJP, Jespers W, Sherman W . In search of beautiful molecules: A perspective on generative modeling for drug design. J Chem Inf Model. 2025; 65(18): 9383-9397.

[32]

Dhumal DM, Patil PD, Kulkarni RV, Akamanchi KG . Experimentally validated QSAR model for surface pKa prediction of heterolipids having potential as delivery materials for nucleic acid therapeutics. ACS Omega. 2020; 5(49): 32023-32031.

[33]

Wang W, Feng S, Ye Z, Gao H, Lin J, Ouyang D . Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm. Acta Pharm Sin B. 2022; 12(6): 2950-2962.

[34]

Xu Y, Ma S, Cui H, Chen J, Xu S, Gong F, et al. AGILE platform: A deep learning powered approach to accelerate LNP development for mRNA delivery. Nat Commun. 2024; 15: 6305.

[35]

Li B, Raji IO, Gordon AGR, Sun L, Raimondo TM, Oladimeji FA, et al. Accelerating ionizable lipid discovery for mRNA delivery using machine learning and combinatorial chemistry. Nat Mater. 2024; 23(7): 1002-1008.

[36]

Wang W, Chen K, Jiang T, Wu Y, Wu Z, Ying H, et al. Artificial intelligence-driven rational design of ionizable lipids for mRNA delivery. Nat Commun. 2024; 15: 10804.

[37]

Yu T, Yao C, Sun Z, Shi F, Zhang L, Lyu K, et al. LipidBERT: A lipid language model pre-trained on METiS de novo lipid library. arXiv:2408.06150 [Preprint]. 2024.

[38]

Yuan Y, Wu Y, Cheng J, Yang K, Xia Y, Wu H, et al. Applications of artificial intelligence to lipid nanoparticle delivery. Particuology. 2024; 90: 88-97.

[39]

Kularatne RN, Crist RM, Stern ST . The future of tissue-targeted lipid nanoparticle-mediated nucleic acid delivery. Pharmaceuticals. 2022; 15(7): 897.

[40]

Cheng Q, Wei T, Farbiak L, Johnson LT, Dilliard SA, Siegwart DJ . Selective organ targeting (SORT) nanoparticles for tissue-specific mRNA delivery and CRISPR-Cas gene editing. Nat Nanotechnol. 2020; 15(4): 313-320.

[41]

Huayamares SG, Lokugamage MP, Rab R, Da Silva Sanchez AJ, Kim H, Radmand A, et al. High-throughput screens identify a lipid nanoparticle that preferentially delivers mRNA to human tumors in vivo. J Control Release. 2023; 357: 394-403.

[42]

Sarode A, Fan Y, Byrnes AE, Hammel M, Hura GL, Fu Y, et al. Predictive high-throughput screening of PEGylated lipids in oligonucleotide-loaded lipid nanoparticles for neuronal gene silencing. Nanoscale Adv. 2022; 4(9): 2107-2123.

[43]

Naidu GS, Yong SB, Ramishetti S, Rampado R, Sharma P, Ezra A, et al. A combinatorial library of lipid nanoparticles for cell type-specific mRNA delivery. Adv Sci. 2023; 10(19): 2301929.

[44]

Qiu M, Tang Y, Chen J, Muriph R, Ye Z, Huang C, et al. Lung-selective mRNA delivery of synthetic lipid nanoparticles for the treatment of pulmonary lymphangioleiomyomatosis. Proc Natl Acad Sci U S A. 2022; 119(8): e2116271119.

[45]

Chen J, Ye Z, Huang C, Qiu M, Song D, Li Y, et al. Lipid nanoparticle-mediated lymph node-targeting delivery of mRNA cancer vaccine elicits robust CD8+ T cell response. Proc Natl Acad Sci U S A. 2022; 119(34): e2207841119.

[46]

Dahlman JE, Kauffman KJ, Xing Y, Shaw TE, Mir FF, Dlott CC, et al. Barcoded nanoparticles for high throughput in vivo discovery of targeted therapeutics. Proc Natl Acad Sci U S A. 2017; 114(8): 2060-2065.

[47]

Guimaraes PPG, Zhang R, Spektor R, Tan M, Chung A, Billingsley MM, et al. Ionizable lipid nanoparticles encapsulating barcoded mRNA for accelerated in vivo delivery screening. J Control Release. 2019; 316: 404-417.

[48]

Radmand A, Lokugamage MP, Kim H, Dobrowolski C, Zenhausern R, Loughrey D, et al. The transcriptional response to lung-targeting lipid nanoparticles in vivo. Nano Lett. 2023; 23(3): 993-1002.

[49]

Rhym LH, Manan RS, Koller A, Stephanie G, Anderson DG . Peptide-encoding mRNA barcodes for the high-throughput in vivo screening of libraries of lipid nanoparticles for mRNA delivery. Nat Biomed Eng. 2023; 7(7): 901-910.

[50]

Kobierski J, Wnętrzak A, Chachaj-Brekiesz A, Dynarowicz-Latka P . Predicting the packing parameter for lipids in monolayers with the use of molecular dynamics. Colloids Surf B Biointerfaces. 2022; 211: 112298.

[51]

Philipp J, Dabkowska A, Reiser A, Frank K, Krzysztoń R, Brummer C, et al. pH-dependent structural transitions in cationic ionizable lipid mesophases are critical for lipid nanoparticle function. Proc Natl Acad Sci U S A. 2023; 120(50): e2310491120.

PDF (768KB)

0

Accesses

0

Citation

Detail

Sections
Recommended

/