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@article{191174,
        author = {Md Ikbal Husain and Minam Jongkey and Nabanita Chakma and Pabitra Debnath and Somenath Routh and Susmita Sarkar},
        title = {Machine Learning-Based Design of Lipid Nanoparticles for mRNA Delivery: A Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {6283-6287},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191174},
        abstract = {Lipid nanoparticles (LNPs) are widely used as delivery systems for messenger RNA (mRNA) therapeutics due to their ability to protect mRNA from degradation and improve cellular uptake. Their effectiveness was clearly demonstrated during the COVID-19 pandemic, where LNP-based vaccines played a major role in rapid vaccine development. However, traditional LNP formulation methods largely depend on trial-and-error approaches, which are time-consuming and limited in their ability to explore a wide range of lipid compositions. In recent years, machine learning (ML) has emerged as a useful tool to support the rational design of LNPs by analyzing large datasets and predicting formulation performance. This review discusses the basic principles of LNP design, limitations of conventional development strategies and the application of ML techniques in optimizing LNP formulations for mRNA delivery. Recent studies highlighting ML-assisted LNP optimization are also summarized. Overall, the integration of machine learning with experimental research offers a promising approach for improving the efficiency and reliability of mRNA delivery systems.},
        keywords = {Lipid nanoparticles (LNPs), mRNA, Machine Learning.},
        month = {January},
        }