AI FOR PREDICTING AND ENGINEERING TISSUE TROPISM

Rational lipid nanoparticle (LNP) design for messenger RNA (mRNA) delivery is being reshaped by artificial intelligence (AI), which is steadily displacing empirical trial-and-error formulation with predictive, data-driven engineering. Three related developments are considered here. The first is the growing use of machine learning (ML) and deep learning (DL) architectures—random forests, gradient-boosted ensembles, deep neural networks, graph neural networks (GNNs), and explainable AI (XAI) frameworks among them—to predict organ-level biodistribution directly from an LNP's physicochemical and compositional descriptors. The second is Selective Organ Targeting (SORT) lipid technology: its mechanistic underpinnings, and the way AI-guided combinatorial screening and generative modelling are now speeding the discovery of SORT-like lipids for liver, lung, spleen, brain, and tumour delivery. The third is digital twin (DT) technology, an emerging framework for patient-specific prediction of mRNA-LNP pharmacokinetics, pharmacodynamics, and biodistribution, with clear implications for virtual clinical trials and precision nanomedicine. The discussion that follows compares these algorithmic approaches critically, points to persistent gaps in data quality and model interpretability, and sets out research priorities for moving AI-optimised, tissue-tropic LNPs into clinical use.