For decades, the design of nanoparticle drug delivery systems has been an exercise in patient, expensive trial and error. Formulation scientists would mix lipids, polymers and drug payloads in one combination after another, measure what came out, and iterate slowly toward something that worked. A new review published in Nature Reviews Bioengineering argues that this paradigm is being replaced by something far more powerful: artificial intelligence and machine learning models that can predict how a nanoparticle will behave before a single drop of it is ever made in the laboratory. The review, led by Magdalini Panagiotakopoulou and Daniel A. Heller of Memorial Sloan Kettering Cancer Center, together with colleagues at the University of Toronto, Duke University and the Technion, maps out how computational tools are transforming every stage of nanomedicine development, from the first sketch of a formulation to predictions of how a particle will travel through a living body.
The stakes are considerable. Drug development remains notoriously slow and costly, with the vast majority of clinical candidates ultimately failing, and nanoparticle therapeutics have struggled more than most to cross the valley between promising preclinical results and approved medicines. Nanoparticles are exquisitely complex objects: their size, surface charge, shape, composition and manufacturing conditions all influence how they behave, and these variables interact in ways that defy intuition. A lipid nanoparticle built for mRNA delivery, for example, contains several distinct lipid species whose ratios must be tuned precisely, while a polymeric particle may depend on molecular weight, block architecture and solvent conditions simultaneously. Traditional design-of-experiments approaches can explore this space only a few factors at a time. Machine learning, by contrast, thrives on high-dimensional parameter spaces, finding patterns across thousands of variables that no human team could hold in mind at once.
The review emphasizes that the choice of algorithm is not arbitrary; it is dictated by how much data exists. In the small-data regime that still characterizes much of nanomedicine, where a typical laboratory might generate hundreds rather than millions of data points, tree-based ensembles such as random forests and gradient boosting methods, along with Gaussian process models, tend to outperform more fashionable architectures. Gaussian processes carry the additional advantage of quantifying their own uncertainty, telling researchers not just what the model predicts but how confident it is, which is invaluable when deciding which experiment to run next. Deep learning, including transformer-based neural networks, only becomes competitive when large, standardized datasets are available, such as those emerging from high-throughput lipid screening campaigns. This mismatch between model ambition and data reality is one of the field’s central tensions, and the authors argue that honest recognition of it should guide method selection more than novelty does.
At the formulation design and optimization stage, machine learning is already delivering concrete wins. Models can predict the physicochemical properties of lipid, polymeric and self-assembling nanoparticles, including particle size, encapsulation efficiency and drug release kinetics, from composition and process variables alone. One highlighted study used machine learning to predict critical liposome quality attributes and then inverted the model to identify the manufacturing parameters needed to hit a target size, easing the transition to microfluidic production. Another combined automation with data-efficient learning to navigate a large oral lipid nanoparticle formulation space from limited experiments. Generative adversarial networks have been applied to predict nanoparticle size in microfluidic synthesis, while Gaussian process models have been used to optimize polymeric particles for encapsulation efficiency and therapeutic efficacy. In each case, the computational model acts as a surrogate for laborious bench work, compressing months of iterative optimization into a fraction of the experiments.
Perhaps the most striking frontier is the discovery of entirely new excipients. Rather than merely optimizing known ingredients, researchers are now using machine learning to screen vast virtual libraries of candidate molecules. One widely cited effort combined machine learning with combinatorial chemistry to accelerate the discovery of ionizable lipids for mRNA delivery, exploring enormous chemical spaces computationally before synthesizing only the most promising candidates. Transformer-based neural networks have been trained to design lipid nanoparticles de novo, and artificial intelligence-guided frameworks have produced ionizable lipids with improved delivery performance through iterative cycles of virtual screening and experimental feedback. Computationally guided high-throughput approaches have even explored a design space of more than two million drug-excipient pairings for self-assembling nanoparticles, demonstrating that scale of exploration which would be unthinkable by hand.
The review then turns to preclinical evaluation, where machine learning is being asked a harder question: not what a particle is, but what it does to cells and tissues. Models can predict cellular uptake, transfection efficiency and cytotoxicity from formulation features, and interpretable algorithms have linked delivery efficiency to tumor genomic mutations, offering a route toward personalized nanomedicine. Massively parallel pooled screening, combined with machine learning, has revealed biological regulators of lipid nanoparticle delivery, such as the lysosomal transporter SLC46A3, connecting particle-cell interactions to multi-omic data. Other models predict the functional composition of the protein corona, the layer of biomolecules that coats every nanoparticle entering a biological fluid and often determines its fate. Physics-informed neural networks, which embed known physical laws into the learning process, are being used to quantify transcytosis and diffusion across in vitro models of the blood-brain barrier, one of the most formidable obstacles in drug delivery.
In vivo prediction represents the ultimate prize. Machine learning models are now integrating nanoparticle characteristics with complex biological datasets, from tumor genomics to routine medical imaging, to forecast biodistribution, tumor accumulation and therapeutic outcomes. Interpretable radiomics models have been shown to predict nanomedicine tumor accumulation from standard clinical scans, while machine-learning-assisted analysis of individual tumor vessels has illuminated how nanoparticles permeate vasculature. Studies of nanoparticle delivery to the brain, to glioblastoma and to tumors more broadly have all benefited from these approaches. The authors caution, however, that a well-known weakness persists: correlations between in vitro and in vivo performance are often weak, meaning that cell culture results remain an imperfect proxy, and models trained on them inherit that limitation. Bridging this gap is among the field’s most urgent challenges.
A particularly transformative development is the marriage of machine learning with autonomous experimentation, the so-called self-driving laboratories. In these closed-loop systems, a model proposes the next best experiment, robotic platforms execute it, analytical instruments feed the results back, and the model updates itself in an unbroken design-make-test-analyse cycle. Platforms powered by deep learning and foundation models have already accelerated lipid nanoparticle development for mRNA delivery and enabled the autonomous discovery of new ionizable lipid designs. Molecular dynamics simulations and physics-informed frameworks add mechanistic grounding, ensuring that models do not merely interpolate between data points but respect the underlying physics of self-assembly, diffusion and molecular interaction. Together these tools promise workflows in which the boundary between prediction and validation becomes increasingly porous.
None of this will happen automatically, and the review is refreshingly candid about the obstacles. Nanomedicine datasets are frequently small, inconsistently reported and locked away in individual laboratories, which starves algorithms of the fuel they need. The authors call for community-wide adoption of FAIR data principles, making data findable, accessible, interoperable and reusable, alongside standardized minimum-information reporting frameworks, open-source code sharing and large-scale repositories built through public-private partnerships. They also stress the importance of interpretability, since regulators and clinicians will need to understand why a model recommends a particular formulation before trusting it with patients. If those cultural and infrastructural shifts take hold, the authors argue, machine learning could finally deliver on nanomedicine’s long-deferred promise, shortening development pipelines, improving clinical translation and bringing precisely engineered nanoparticle therapies to patients faster than ever before.
Subject of Research: The application of artificial intelligence and machine learning to the design, optimization and preclinical evaluation of nanoparticle drug delivery systems
Article Title: Artificial intelligence and machine learning in nanoparticle drug delivery systems
Article References: Panagiotakopoulou, M., Goren, A., Reker, D., Schroeder, A., Allen, C., & Heller, D. A. (2026). Artificial intelligence and machine learning in nanoparticle drug delivery systems. Nature Reviews Bioengineering. https://doi.org/10.1038/s44222-026-00495-7
Image Credits: AI Generated
DOI: 10.1038/s44222-026-00495-7
Keywords: artificial intelligence, machine learning, nanoparticles, drug delivery, lipid nanoparticles, nanomedicine, mRNA delivery, formulation optimization, self-driving laboratories, protein corona, biodistribution prediction, FAIR data principles
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Blake Davidson. (September 20, 2026). AI Is Rewriting How Nanoparticle Medicines Are Designed. Scienmag. https://scienmag.com/ai-is-rewriting-how-nanoparticle-medicines-are-designed/
Blake Davidson. “AI Is Rewriting How Nanoparticle Medicines Are Designed.” Scienmag, 20 September 2026, https://scienmag.com/ai-is-rewriting-how-nanoparticle-medicines-are-designed/. Accessed 20 September 2026.
Blake Davidson. “AI Is Rewriting How Nanoparticle Medicines Are Designed.” Scienmag. September 20, 2026. https://scienmag.com/ai-is-rewriting-how-nanoparticle-medicines-are-designed/
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