Machine Learning and Artificial Intelligence in Nanomedicine

ABSTRACT

Nanomedicine harnesses nanoscale materials, such as lipid, polymeric, and inorganic nanoparticles, to deliver diagnostic or therapeutic agents for cancer, infectious disease, and neurological disorders, among others. However, translating promising nanoparticle designs into clinically approved products remains a challenge. Factors such as particle size, surface chemistry, and payload interactions must be optimized, and preclinical results often fail to predict human efficacy. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools to address these hurdles at every stage of nanomedicine development. By rapidly screening extensive libraries and extracting structure–function relationships, AI‐driven models can rationalize nanoparticle formulation, predict biodistribution, and guide optimal design. Techniques like high‐throughput DNA barcoding and automated liquid handling facilitate robust, large‐scale data collection, feeding into computational pipelines that expedite discovery while reducing reliance on resource‐intensive trial‐and‐error experiments. AI‐based platforms also enable improved modeling of protein corona formation, which profoundly affects nanoparticle immunogenicity and cellular uptake. Despite these advances, challenges persist in data standardization, model generalizability, and establishing a clear regulatory framework since no dedicated U.S. Food and Drug Administration (FDA) guidance addresses the intersection of AI and nanomedicine. Overcoming these limitations requires harmonized data sharing, rigorous in vivo validation, and clear ethical and regulatory guidelines. This review summarizes the rapidly evolving landscape of AI in nanomedicine, highlighting key successes in design and preclinical prediction, as well as persistent obstacles to full‐scale clinical integration. By illuminating these dynamics, we aim to chart a more efficient path forward in developing next‐generation nanomedicine.

Integration of machine learning and artificial intelligence in nanomedicine to connect nanoparticle designs and toxicological data for optimizing design and formulation, as well as improving biological modeling.

1. Introduction

Nanomedicine offers a highly adaptable framework for delivering a broad range of therapeutics, ranging from small‐molecule drugs to biologics and vaccines. Owing to their nanoscale dimensions and functional surfaces, nanoparticles (NPs) can navigate complex biological environments, such as crossing physical and physiological barriers (e.g., blood–brain barrier) and selectively interacting with target cells (Mitchell et al. 2021). Consequently, nanomaterials have been utilized for diagnostics and therapeutics of cancer, infectious diseases, and neurological disorders (Huang et al. 2024; Shi et al. 2017; Wang, Hu, et al. 2024). Despite these advances, however, many nanomedicine candidates fail to achieve successful clinical translation (Blanco et al. 2015; Chen et al. 2023; Shi et al. 2017; Wilhelm et al. 2016). This high failure rate stems from multifaceted design demands such as size, shape, surface chemistry, and payload properties, as well as from in vitro and in vivo animal models that often poorly predict clinical performance (Kim et al. 2024). Moreover, the nano‐bio interface, which refers to the dynamic interaction between NP surfaces and biomolecules such as plasma proteins and cell membranes, introduces complexities. Even minor changes in surface characteristics can substantially influence NPs' interactions with biomolecules, altering their biodistribution, immune responses, and cellular uptake (Francia et al. 2019; Shaw et al. 2025). Several meta‐analysis studies have curated large databases on pharmacokinetics, biodistribution, and tumor delivery of NPs with various physicochemical properties (Chen et al. 2023; Cheng et al. 2020; Wilhelm et al. 2016). These challenges and databases underscore the need and provide the feasibility of applying machine learning (ML) and artificial intelligence (AI) approaches for streamlining nanomedicine discovery, accelerating formulation efforts, and bridging significant knowledge gaps (Nuhn 2023).

Recent advancements in ML and AI offer a new avenue to tackle these barriers throughout the nanomedicine development lifecycle. By examining extensive, high‐dimensional datasets and extracting patterns, AI‐driven approaches can significantly accelerate and refine nanomedicine design and formulation (Ho 2022; Li et al. 2024; Mi et al. 2024; Shan et al. 2024). ML‐driven tools are increasingly vital in predicting preclinical outcomes such as biodistribution, toxicity, and efficacy, minimizing reliance on labor‐intensive trial‐and‐error (Banaye Yazdipour et al. 2023; Chou et al. 2023; Mi et al. 2024). Additionally, intricate nano‐bio interactions often govern therapeutic efficacy and safety, and AI‐based modeling of these interfaces guides strategies for more targeted nanomedicine (Ban et al. 2020). AI/ML is particularly well‐suited for nanomedicine because of its ability to model nonlinear relationships, optimize multidimensional design spaces, and integrate diverse experimental, simulation, and literature‐derived data. These strengths are essential for tackling complex formulation challenges, such as correlating NP physicochemical properties with in vivo performance or predicting protein corona formation under various biological conditions. However, these approaches hinge on robust, diverse datasets, necessitating high‐throughput platforms to standardize information curation. Such systems improve reproducibility, integrate cross‐laboratory insights, and bolster synergy among researchers.

Despite these advancements, multiple challenges and limitations remain in applying AI and ML to nanomedicine. Data scarcity, algorithmic bias, and regulatory uncertainties can dampen the confidence and practicality of AI‐driven results. In particular, data scarcity remains a fundamental barrier, as high‐quality, well‐annotated nanomedicine datasets are limited in size and consistency. To address this, emerging strategies such as data augmentation and transfer learning are increasingly being explored to enhance model robustness and generalizability in low‐data scenarios (Bets et al. 2024; Jahandoost et al. 2024). Meanwhile, translating model insights into manufacturable products still demands rigorous experimental verification. Navigating these issues requires sustained efforts in developing robust datasets, refining computational methodologies, and fostering collaboration among researchers, clinicians, and industry stakeholders. This review explores how AI can drive innovation in nanomedicine, covering from design and formulation to preclinical prediction and data curation, and critically examines current barriers to its widespread implementation. By analyzing emerging research paradigms, we aim to offer a roadmap for integrating AI into nanomedicine development, ultimately leading to more efficient discovery, safer therapeutic profiles, and accelerated clinical translation. In this review, we use AI as an umbrella term that encompasses a variety of computational approaches, including ML, which is a central focus of this article. This distinction is important because we discuss both general AI technologies, such as generative models, reinforcement learning, and autonomous systems, and ML‐based predictive modeling.

2. AI‐Driven Nanomedicine Design and Formulation

The intricate optimization of nanomedicines involves tuning interdependent factors such as particle size, shape, surface groups, and drug cargo. Traditionally, researchers rely on costly, time‐consuming trial‐and‐error workflows, varying one parameter at a time with limited scalability and difficulty in capturing complex structure–function relationships. Such approaches often struggle to identify optimal designs due to their inability to explore the vast chemical design space efficiently or detect nonlinear dependencies between formulation variables and biological outcomes. Shan et al. (2024) recently introduced a directed evolution framework (Figure 1) that merges virtual and physical compound libraries, combinatorial synthesis, DNA/peptide barcoding for in vivo screening, and ML‐driven data analysis. This approach addresses the shortcomings of traditional methods by replacing linear screening with an iterative, data‐driven optimization process. This cyclical approach streamlines nanomedicine discovery by leveraging structure–activity relationships revealed during high‐throughput experimentation to guide iterative refinements, ultimately leading to more efficient NP designs. Through a continuous feedback loop, each design modification is informed by new data on nano‐bio interactions, enhancing both discovery speed and ultimate therapeutic performance. In the following sections, we detail how AI‐driven methodologies accelerate the initial discovery of NP formulations (Section 2.1) and address crucial aspects of stability, endosomal escape, and related design refinements (Section 2.2).

FIGURE 1.

Directed evolution framework for accelerating nanomedicine design, combining virtual and physical compound libraries, modular synthesis, DNA/peptide barcoding for in vivo screening, and machine learning–driven analysis. This cyclical approach uses iterative feedback to identify structure–activity relationships, guiding more efficient nanoparticle optimization. Reproduced from Shan et al. (2024) under the terms of the Creative Commons Attribution 4.0 International License.

2.1. Computational Approaches for Nanomedicine Formulation

Recent advances in AI and ML have facilitated a paradigm shift in the rational design of nanomedicine formulations. In the context of lipid nanoparticles (LNPs), several studies have employed computational pipelines to optimize ionizable lipids for RNA therapeutics, significantly reducing reliance on empirical trial‐and‐error (Li et al. 2024; Shan et al. 2024; Wang, Chen, et al. 2024; Witten et al. 2024; Xue et al. 2024). For example, Li et al. (2024) and Shan et al. (2024) reported ML‐driven screening of LNPs that improved mRNA delivery efficiency. One notable effort by Wang et al. (2022) applied large‐scale in silico screening (Nearly 20 million ionizable lipids) to discover novel ionizable lipids that outperformed well‐established benchmarks, including MC3 (i.e., the primary ionizable lipid in Onpattro, the first FDA‐approved RNAi therapy) and SM‐102 (i.e., used in Moderna's COVID‐19 mRNA vaccines), thus improving RNA delivery. Similarly, by synergistically combining deep‐learning methodologies with high‐throughput combinatorial lipid synthesis chemistry, Xue et al. (2024) introduced the AI‐Guided Ionizable Lipid Engineering (AGILE) platform, utilizing a pre‐trained Graph Neural Network (GNN) model and combinatorial libraries to screen 1200 lipids, eventually extrapolating to 12,000 variants for enhanced mRNA transfection. By creating a large‐scale dataset of > 9000 LNPs activity measurements, Witten et al. (2024) demonstrated an AI‐guided approach for designing LNPs specifically for pulmonary gene therapy, further underscoring the versatility of computational pipelines in expanding RNA delivery applications. This acceleration stems from the ability of ML models to rapidly screen large chemical libraries, prioritize promising candidates based on learned structure–activity relationships, and reduce the need for costly, iterative wet‐lab experiments. Additionally, by analyzing high‐dimensional data, these models can uncover nonlinear dependencies between molecular features and biological outcomes that traditional methods may miss (Witten et al. 2024; Xu et al. 2024). These computational pipelines reduce developmental timelines and reveal subtle structure–function relationships, empowering researchers to fine‐tune ionizable lipid composition, pKa, and chain lengths for improved delivery.

Beyond LNPs, AI‐driven formulation pipelines now span materials‐level optimization of polymeric micelles, dendrimers, and inorganic nanocarriers, through biologically informed simulation, to cloud‐based deployment. At the materials level, evolutionary algorithms and deep generative networks search vast design spaces of polymer composition, surface chemistry, and core–shell architecture, rapidly highlighting candidates with favorable stability and loading capacity (Ding et al. 2023; Rezvantalab et al. 2024). These in silico designs are then evaluated in multiscale tumor or tissue simulations coupled to reinforcement‐learning agents. For example, Stillman et al. (2021) iteratively evolved NP properties to maximize intratumoral penetration and cytotoxic payload release, while deep‐learning surrogates have predicted colloidal stability and long‐acting release kinetics essential for sustained dosing (Kim et al. 2024). Insights from such models are disseminated through web portals like FormulationAI (Ding et al. 2023), where researchers upload NP descriptors, obtain AI‐generated performance scores, and iteratively refine non‐lipid formulations using community datasets. By linking materials generation, biological‐context modeling, and open‐access deployment, these advances illustrate a coherent expansion of AI applications beyond LNPs. Complementing these efforts, open‐source databases and standardized reporting frameworks are steadily improving model reproducibility and cross‐laboratory utility (Agrahari and Agrahari 2018; Dordevic et al. 2022). Parallel progress in AI‐driven protein‐structure prediction, most notably AlphaFold (Jumper et al. 2021; Yang et al. 2023), has further enabled the design of protein‐ and peptide‐based targeting moieties for nanomedicine. These ligands require specific three‐dimensional conformations to bind cellular receptors with high affinity and specificity, such as integrins or transferrin receptors, to facilitate receptor‐mediated uptake. AI‐based prediction of peptide structures allows researchers to rationally design or refine ligands prior to synthesis, increasing their likelihood of effective binding. For example, AlphaFold can be used to design peptide ligands and protein‐based NPs with defined shapes, symmetries, and biological functionalities, expanding the scope of receptor‐specific targeting strategies in nanomedicine (Gomari et al. 2025). These protein‐ or peptide‐based moieties can be conjugated to nanocarrier surfaces to improve tumor accumulation, enhance cellular uptake, and reduce off‐target effects. Ultimately, these interconnected computational strategies, ranging from ionizable‐lipid discovery to polymeric and inorganic carrier optimization, tumor‐scale simulation, web‐based formulation engines, and AI‐designed targeting moieties, demonstrate a unifying theme: integrating AI with mechanistic modeling and iterative experimental feedback accelerates rational nanomedicine design and unlocks formulation pathways that were previously inaccessible through traditional screening alone.

2.2. Machine Learning Insights Into Stability, Endosomal Escape, and Beyond

NP stability refers to the ability of NPs to maintain their physicochemical properties, such as size, shape, and surface characteristics when exposed to physiological or biological environments. Ensuring stability is essential because instability can lead to aggregation, rapid clearance, or unintended interactions with biological molecules, thereby reducing therapeutic efficacy (Hou et al. 2021). Endosomal escape, on the other hand, describes the capacity of NPs to efficiently exit endosomal compartments after cellular uptake, thereby releasing their therapeutic payload into the cytoplasm. Poor endosomal escape limits the effectiveness of NP‐based therapeutics by trapping drugs within intracellular vesicles, preventing them from reaching their targets (Carrasco et al. 2021). While stability and endosomal escape are distinct processes, they are interrelated. For example, NPs that exhibit poor colloidal or structural stability under endosomal conditions may aggregate or disassemble prematurely, hindering escape. Conversely, NPs designed with controlled destabilization under acidic pH, such as proton‐sponge or membrane‐disruptive materials, can enhance escape efficiency by leveraging their stability profiles to trigger release at the appropriate intracellular stage (Nayanathara et al. 2025; Smith et al. 2019).

Recent advances in ML have offered new avenues for understanding and optimizing NP stability and endosomal escape. By analyzing a large dataset of physicochemical descriptors ranging from size, zeta potential, and shape to chemical composition, a ML model can identify patterns that conventional experiments might overlook. These predictive models can guide the rational design of NPs with enhanced colloidal stability, minimal aggregation, and improved endosomal escape. Although direct examples focusing on both NP stability and endosomal escape remain limited, related research has already showcased the value of data‐driven strategies in informing NP design principles. For instance, Chen and Lv (2022) provided a comprehensive review of ML‐assisted NP synthesis, emphasizing how these approaches not only enhance control over NP stability and structural properties but also offer mechanistic insights into tuning key parameters such as reaction conditions and material attributes. Building upon these principles, incorporating additional factors like proton‐buffering capacity and specific ligand interactions could further advance predictive models for endosomal escape, thereby bridging the gap between in vitro characterization and in vivo performance. Moreover, the increasing use of stimuli‐responsive materials that exploit pH gradients, temperature shifts, or enzymatic activation introduces additional layers of complexity, which ML‐based models are well positioned to characterize and optimize (Fatima et al. 2024).

3. The Role of AI in Nanoparticle Pharmacokinetics and Biodistribution

3.1. AI‐Powered Physiologically Based Pharmacokinetic Modeling for Predicting Nanoparticle Pharmacokinetics

Optimizing NP pharmacokinetics (PK), biodistribution, and safety requires understanding multifactorial biological barriers and the complex NP interactions with cells, tissues, and endogenous biomolecules (Mahmoudi et al. 2023; Park 2020; Yuan et al. 2023). Moreover, preclinical data based on animal disease models often fail to accurately predict human outcomes due to interspecies differences in tissue permeability, immune clearance rates, and NP‐protein interactions. These translational challenges highlight the need for predictive, mechanistic, and integrative approaches. PBPK modeling has proved especially promising in predicting the pharmacokinetics and biodistribution of NPs (Chou et al. 2023, 2022; Dogra et al. 2020; Li et al. 2010; Lin, Aryal, et al. 2022; Yuan et al. 2019), offering a mechanistic framework for understanding their behavior in biological systems. Unlike conventional empirical approaches, the PBPK model frames the body into physiologically relevant compartments and accounts for not only physiological (e.g., tissue volumes and blood flow rates) but also chemical‐specific parameters (e.g., plasma protein binding and elimination rate constants). By considering mechanistic compartment models, the extensive experimental data, including in vitro and in vivo, alongside literature‐derived virtual datasets, can be incorporated into the model to achieve high‐fidelity simulations of NP behavior in biological systems. However, developing PBPK models for nanomedicines requires many parameters that define NP fate (e.g., permeability coefficient, cellular uptake rate) and are difficult to measure experimentally in a comprehensive manner (Le et al. 2022). AI and ML aid in bridging these gaps by predicting these critical parameters and using them to develop a generic PBPK model for NPs (Chou et al. 2023; Chou and Lin 2023). For example, Chou et al. (2023) pioneered an AI‐PBPK workflow that couples a deep‐neural QSAR model with a mouse PBPK structure, achieving R ² = 0.83 (RMSE = 3% ID) for 24 h tumor delivery across 378 datasets and eliminating the need for new animal calibration. Mi et al. (2024) then curated 534 mouse biodistribution profiles spanning 10 NP platforms and trained deep neural network (DNN) models to predict tumor delivery and tissue distribution of NPs; the best model achieved R ² = 0.45 for liver AUC and R ² = 0.79 for spleen on the test set, while SHAP analysis highlighted hydrodynamic size and zeta potential as dominant drivers. Most recently, Khakpour et al. (2025) proposed a multi‐view cross‐attention network with RF/XGB ensemble that injects prior knowledge (size, charge, shape) into model training; 5‐fold cross‐validation showed consistent RMSE reductions versus baseline multi‐layer perceptron (MLP) models, and saliency analysis highlighted hydrodynamic diameter as the strongest driver of all four tumor‐related parameters. Collectively, these examples show how interpretable tree‐based models and deep nets supply the kinetic “missing pieces” that turn sparse data into high‐fidelity PBPK simulations.

Additionally, AI can assist in cross‐species translation by learning patterns from both animal and human datasets, enabling the estimation of human‐specific parameters using in vitro data or virtual experiments. For example, Mi et al. (2024) and Chou and Lin (2023) developed AI‐assisted PBPK and QSAR models using data derived from mice, focusing on predicting NP biodistribution based on physicochemical descriptors. Although these models are species‐specific, they demonstrate the potential for combining QSAR models with PBPK frameworks to infer kinetic parameters such as permeability and clearance rates. This conceptually lays the groundwork for human translation, where AI could eventually be used to estimate human‐specific parameters using in vitro data and structural descriptors. While AI has not yet resolved these complexities in practice, it offers a flexible platform for integrating diverse biological datasets, potentially including in vitro human tissue models, to support more physiologically relevant predictions in the future.

When integrated with QSAR modeling, which establishes links between NP physicochemical attributes and experimentally measured kinetic parameters, AI‐assisted PBPK simulations become a powerful tool for rational NP design. For instance, rather than conducting exhaustive wet lab experiments, researchers can computationally predict how modifying a single NP property, such as surface charge or particle size, might influence organ‐specific accumulation profiles, toxicity, or tumor delivery efficiency (Lin, Chou, et al. 2022; Mi et al. 2024). In a representative workflow, a Nano‐Tumor Database containing key NP descriptors (e.g., size, shape, and surface charge) was used to train an AI‐QSAR model to yield kinetic parameters that feed into a PBPK model to form an AI‐assisted PBPK model for NPs (Chou et al. 2023) (Figure 2). Simulations identify the most promising NPs for subsequent validation in animal studies.

FIGURE 2.

Overview of the computational workflow integrating AI and PBPK modeling to predict nanoparticle delivery efficiency to the tumor site in tumor‐bearing mice. Panel (A) shows the assembly of a comprehensive Nano‐Tumor Database capturing key NP properties and tumor parameters. Panel (B) illustrates the development of an AI‐QSAR model that leverages machine learning (e.g., DNN, RF) to predict tumor‐specific kinetic parameters including KTRES_max (maximum uptake rate), KTRES_50 (time to half‐maximum uptake), KTRES_n (Hill coefficient), and KTRES_rel (release rate constant). Panel (C) depicts the AI‐assisted PBPK model that incorporates these parameters to simulate NP biodistribution across multiple tissues. Model performance is assessed using metrics such as Adjusted R ² and RMSE. Adapted with permission from Chou et al. (2023).

3.2. Data‐Driven Model for Nanoparticle Pharmacokinetics

Beyond classical PBPK, neural ordinary differential equations (NODEs) and physics‐informed neural networks (PINNs) unify mechanistic insight with data‐driven learning (Bram et al. 2024; Karniadakis et al. 2021). Traditional PBPK modeling already leverages compartmental ODEs to describe how NPs transport through various tissues over time. However, NODEs extend this framework by learning the dynamics directly from data while preserving the continuous‐time nature of ODE solutions, thereby merging the interpretability of mechanistic models with the adaptability of deep learning. A previous study by Lu et al. (2021) demonstrated how NODEs can predict drug concentration profiles more accurately than traditional compartmental models (Lu et al. 2021). In this study, the authors applied NODEs to model the pharmacokinetics of various drugs using sparse and noisy patient data, overcoming the limitations of conventional PK models that require predefined equations. The NODE framework dynamically adjusts ADME parameters, allowing it to learn nonlinear drug kinetics from real‐world patient data. This approach is particularly valuable for NP‐based drug delivery, where NP degradation, drug release kinetics, and biodistribution often deviate from classical PK assumptions. For example, in NP‐based cancer therapy, where tumor permeability and the enhanced permeability and retention (EPR) effect influence drug accumulation, a NODE‐driven model can adaptively refine its predictions based on patient‐specific PK data. On the other hand, PINNs go one step further by embedding known physiological constraints, such as pharmacokinetic equations with biologically plausible parameters, including blood flow rates or tissue permeability, into the loss function itself. A recent example of PINNs in pharmacokinetics is the CMINNs model (Compartment Model Informed Neural Networks) developed by Ahmadi Daryakenari et al. (2025). They applied PINNs to study the long‐term pharmacokinetics of amiodarone, a drug with complex absorption and elimination patterns. By introducing a time‐dependent absorption rate, their model captured fluctuations in drug levels more accurately while staying consistent with known pharmacokinetic principles. This approach made predictions more flexible and data‐driven, reducing the need for rigid, predefined models. This ensures that network predictions do not simply fit the data but remain consistent with the underlying pharmacokinetics principles. In practical terms, PINNs can ingest limited but high‐quality in vivo measurements (e.g., NP concentrations in blood or target tissues at discrete time points) while filling in unmeasured dynamics in a manner that respects the governing equations of NP transport (Ahmadi Daryakenari et al. 2025). By doing so, these approaches can reduce the amount of experimental data required for model calibration, enhance prediction accuracy in untested dosing scenarios, and provide an interpretable framework for validating new NP designs. Future developments that couple PINNs or NODEs with existing PBPK platforms may offer a powerful hybrid modeling paradigm that integrates mechanistic compartmental structures, AI‐based parameter estimation, and first‐principles constraints to expedite the discovery and optimization of next‐generation NP therapeutics.

4. AI and Computational Modeling for Analyzing Nano‐Bio Interactions

4.1. AI‐Driven Analysis of Nano‐Bio Interactions and Protein Corona Formation

Upon NPs entering biological fluids, proteins and other biomolecules adsorb onto their surfaces, thus creating a new biological identity that may differ markedly from their as‐synthesized characteristics (Hajipour et al. 2023; Monopoli et al. 2012). This protein corona can affect opsonization, immune recognition, and NP localization in target tissues (Cai et al. 2022; Chou and Lin 2024; Corbo et al. 2016; Francia et al. 2019). Traditional protein corona studies are frequently low‐throughput, but AI‐driven analysis can manage the complex, high‐dimensional data generated by proteomics. ML algorithms can correlate NP properties (e.g., size, surface charge, and hydrophobicity) with specific protein adsorption profiles (Ban et al. 2020; Duan et al. 2020; Findlay et al. 2018; Fu et al. 2024; Huzar et al. 2025; Saeedimasine et al. 2024), thereby uncovering relationships such as associations between particular protein families and NP surface functional groups that might otherwise go unnoticed. These correlations stem from how different NP properties influence electrostatic, hydrophobic, and steric interactions with plasma proteins. For example, positively charged NPs preferentially adsorb negatively charged proteins like albumin, while hydrophobic surfaces favor absorption of apolipoproteins or immunoglobulins (Lundqvist et al. 2008; Walkey et al. 2012). The extent and strength of protein absorption can be experimentally characterized using total protein binding (e.g., μg protein per mg NP), binding affinity constants (via isothermal titration calorimetry), and exchange kinetics (using fluorescence correlation spectroscopy or surface plasmon response), which together delineate hard versus soft corona layers (Cedervall et al. 2007; Monopoli et al. 2012).

Moreover, AI techniques like random forests, DNNs, and support vector machines have become invaluable for identifying which physicochemical attributes of NPs most strongly predict subsequent protein corona formation, cellular uptake, trafficking routes, or immunological responses. For example, Findlay et al. (2018) applied random forest models to silver nanoparticle–protein corona formation, achieving an impressive AUC of 0.83 and F1‐score of 0.81 using features like NP size, surface charge, and solution ionic strength. Ban et al. (2020) trained a random forest meta‐model on a broad protein corona dataset to predict functional protein categories (e.g., apolipoproteins, immunoglobulins), identifying links between protein corona composition and macrophage uptake and cytokine release. More recently, Cao et al. (2025) employed random forest models to predict NP‐induced pulmonary fibrosis, leveraging descriptors from NP properties, cytokine release, and cellular signaling features. These studies demonstrate how ML frameworks can move beyond protein corona composition, linking protein corona signatures to cellular and immunological outcomes thereby enabling rational design of safer, more targeted nanomedicines.

4.2. Computational Modeling and Predictive Insights Into Protein Corona Dynamics

Simulations that incorporate experimental binding affinities also provide predictive insight into corona dynamics, highlighting how different proteins compete on NP surfaces over time (Salvati et al. 2013). For example, Wei et al. (2017) employed coarse‐grained molecular simulations adjusted with experimentally determined protein–NP binding affinities to illustrate the dynamic exchange of proteins on silica NP surfaces over time, capturing how different proteins compete or displace each other under biologically relevant conditions. These results are critical because the composition and structure of the protein corona can drastically shift NP interaction with cell‐surface receptors, influencing routes of endocytosis and subsequent intracellular trafficking. Complementing such simulation‐based methods, ML models have been developed to predict which proteins are most likely to adsorb onto a given NP (Ban et al. 2020; Fu et al. 2024; Huzar et al. 2025). Ban et al. (2020) used a random forest model trained on a comprehensive database of mass spectrometry data to successfully predict protein binding profiles for a variety of NPs and highlight how small tweaks in NP design can affect protein absorption. Similarly, Huzar et al. (2025) synthesized a library of diverse DNA nanostructures and investigated the interaction between DNA nanostructures design features and the protein corona composition by developing the XGBoost model. Importantly, these AI‐driven methodologies are not only capable of predicting protein composition but also correlate specific protein signatures with changes in immunogenicity, biodistribution, and toxicity. By coupling such ML‐based protein‐corona predictions with functional assays, such as measuring cellular uptake rates or evaluating cytotoxic responses, researchers can determine whether particular corona‐forming proteins favor receptor‐mediated internalization or, conversely, hinder cell entry by triggering opsonization and clearance (Ban et al. 2020). The link between protein corona composition and biological outcomes has also been explored in high‐throughput screening studies. For example, Boehnke et al. (2022) identified a pathway responsible for NP uptake by analyzing protein–protein interaction networks, highlighting the SLC46A3 gene as a predictive biomarker for LNP internalization. Moreover, a recent study employing label‐free mass spectrometry‐based proteomics demonstrated how soft and difficult‐to‐recover LNPs form coronas enriched with proteins such as vitronectin, C‐reactive protein, and alpha‐2‐macroglobulin (Voke et al. 2025). Intriguingly, while these proteins enhanced LNP uptake in HepG2 cells, they likely did not necessarily improve mRNA expression because the coronal proteins promoted lysosomal trafficking (Voke et al. 2025). These findings underscore the importance of considering the protein corona when developing LNP‐based therapeutics, as even higher uptake does not guarantee favorable intracellular routing or gene‐expression outcomes.

4.3. The Possibility of Generative AI for Inverse Nanomedicine Design

Generative Adversarial Networks (GANs) and reinforcement learning (RL), while more commonly used in de novo drug design and discovery (Atz et al. 2024; Korshunova et al. 2022; Popova et al. 2018), hold great promise for advancing nano‐bio interactions by offering adaptive strategies that build on traditional ML capabilities. GANs, widely applied in drug discovery to generate novel molecules with optimized pharmacological properties, represent a compelling avenue for inverse design in the NPs' space (Rahman et al. 2025). By training on experimental datasets of NP size, shape, surface chemistry, and corresponding biodistribution or toxicity profiles, GANs can propose entirely new formulations aimed at maximizing tumor targeting or minimizing immune evasion (Hasanzadeh et al. 2022). RL extends these capabilities by iteratively refining NP designs through feedback loops, rewarding policy networks for discovering configurations that meet performance criteria such as low off‐target accumulation or enhanced cellular uptake (Tao et al. 2021). Moving further, emerging generative AI strategies, particularly GANs and RL, open new frontiers in inverse NP design by translating structural, physicochemical, and processing parameters into latent representations that can be inverted to produce new formulations, such as those featuring more favorable protein coronas. Although no direct demonstrations focusing on tailoring NP–protein corona interactions have been reported, the principles have been validated in related domains such as porous (Yao et al. 2021) or photonic materials (Liu et al. 2023), as well as molecular synthesis (Sanchez‐Lengeling and Aspuru‐Guzik 2018). Porous and photonic materials offer mechanistic analogies that inform generative AI modeling in nanomedicine. For example, pore size optimization in porous materials parallels surface tuning in NPs, which both aim to regulate molecular interaction profiles, whether for controlled adsorption in filters or protein corona formation in biological media. In AI modeling, these problems share a similar structure: defining a latent design space constrained by functional performance metrics. However, nanomedicine presents additional complexity due to the dynamic and context‐dependent nature of biological systems, such as varying pH, enzymatic environments, and immune response. To overcome these challenges, generative pipelines can incorporate strategies such as multi‐task learning, uncertainty quantification, and physics‐informed constraints. These methods help bridge the gap between the relatively predictable optimization of porous materials and the more complex, context‐dependent behavior of nanoparticles within biological systems. This analogy not only validates the application of generative AI frameworks to materials design but also provides a foundational blueprint for extending such models to the more complex, data‐driven challenges of nanomedicine, particularly in tailoring NP surfaces for specific protein corona profiles.

By coupling these generative methods with large‐scale proteomic datasets on NP–protein interactions, researchers can systematically propose NP surfaces that either minimize the binding of high‐immunogenic proteins or selectively adsorb proteins that facilitate targeted cell entry. In one of our ongoing works (Figure 3), we are integrating generative AI with extensive protein corona data to identify and optimize NP surface characteristics that promote desirable protein corona formation for enhanced tumor targeting (Canchola et al. 2025). By bridging these in silico predictions with iterative laboratory validation, we aim to accelerate the rational design of next‐generation NPs that reliably achieve improved accumulation at the tumor site, minimize immunogenic risk, and streamline the path toward clinical translation.

FIGURE 3.

Generative AI‐driven optimization of nanoparticle surface properties for favorable protein corona formation and enhanced tumor targeting. The figure illustrates a protein corona (PC)‐Generative Adversarial Network (GAN) model, trained on a protein corona database, which generates PC patterns as input for a neural ordinary differential equation‐based pharmacokinetic (NODE‐PK) model. The NODE‐PK model is then trained and validated using a Nano‐Tumor Database to predict PC‐guided biodistribution and tumor delivery efficiency of nanoparticles.

Meanwhile, autonomous research systems or “robot scientists” combine ML‐driven modeling with automated experimentation, closing the loop through active learning and evolutionary algorithms (Hickman et al. 2023; Rapp et al. 2024). In this paradigm, each iteration can fine‐tune NP attributes to yield corona compositions conducive to stable circulation, reduced immune clearance, and improved therapeutic action (Hickman et al. 2023; Rapp et al. 2024). Over successive generations, design rules are validated, suboptimal prototypes discarded, and promising leads advanced, ultimately moving the field closer to “safe‐by‐design” nanosystems characterized by carefully modulated protein coronas and robust clinical performance.

5. High‐Throughput Platforms and Database Curation for Nanomaterials

5.1. High‐Throughput Nanoparticle Formulation and AI‐Driven Optimization

High‐throughput experimental platforms, such as DNA barcoding, microfluidic automation, and combinational synthesis, have rapidly expanded the range of possible NP formulation, characterization, and performance testing (Dahlman et al. 2017; Gimondi et al. 2023; Xue et al. 2024). By systematically varying parameters, such as particle size, surface chemistry, and cargo loading, researchers can now generate large‐scale datasets capturing how each formulation performs in vitro and in vivo. For example, DNA barcoding uniquely tags each NP variant, allowing hundreds or thousands of formulations to be pooled and evaluated simultaneously in a single assay (Dahlman et al. 2017). Coupled with automated liquid handling systems, multi‐well plate readers, and next‐generation sequencing, these methods enable detailed readouts of NP uptake, biodistribution, and therapeutic efficacy across diverse cell lines or animal models (Dahlman et al. 2017). One recent study exemplifies the power of high‐throughput screening by leveraging DNA barcoding for LNP optimization in SARS‐CoV‐2 vaccine development (Guimaraes et al. 2024). In this study, LNPs encapsulated unique barcoded DNA (b‐DNA) were formulated via microfluidic mixing and administered to mice, enabling deep sequencing analysis of tissue‐specific delivery. The study identified top‐performing LNPs that exhibited enhanced antigen uptake in antigen‐presenting cells and demonstrated robust immunogenicity against SARS‐CoV‐2 variants. The schematic in Figure 4 illustrates this approach, highlighting how combinatorial LNP formulation and high‐throughput sequencing can accelerate nanomedicine discovery. In addition, high‐throughput chemical synthesis methods have enabled the rapid generation of diverse NP libraries by systematically screening large collections of chemical, lipid, polymer, and small‐molecule precursors (Shan et al. 2024). As a result, the rapidly expanding database of nanomedicine formulations now offers a solid foundation for identifying design principles that were previously obscured by limited insights and inconsistent experimental methodologies. Building upon these comprehensive NP formulation datasets, ML and AI models can play a critical role in analyzing high‐throughput nanomedicine experiments. One notable example is the AGILE platform (Xu et al. 2024), which combines deep learning with combinatorial libraries of ionizable lipids to streamline mRNA delivery. Through iterative feedback loops, where initial experimental data trains predictive models and those models guide subsequent rounds of synthesis, AGILE exemplifies how computational intelligence can optimize nanomedicine discovery. In this manner, AI‐driven frameworks amplify the impact of high‐throughput experimentation, bringing the field closer to the truly rational design of NP formulations.

FIGURE 4.

Schematic of LNPs encapsulating barcoded DNA (b‐DNA) for accelerated in vivo delivery screening and immunization. (A) LNPs were formulated via microfluidic mixing of an aqueous phase of b‐DNA and an ethanol phase of lipids. Each LNP formulation was encapsulated with a unique b‐DNA and screened in vivo by pooling b‐DNA‐LNPs and administering them to mice. Four hours post injection, b‐DNA delivery to target organs was quantified via deep sequencing. (B) Immunization with an optimized LNP‐based DNA vaccine protected K18‐hACE2 mice against SARS‐CoV‐2 variants, reducing lethality and eliciting strong cellular and humoral immune responses. Panels A and B are adapted from Guimaraes et al. 2024 with permission from the publisher.

5.2. Advanced Proteomics and Predictive Protein Corona Profiling

High‐throughput platforms have also been developed to screen the protein corona composition and characterize NPs' behavior from biodistribution and cellular uptake to immune recognition. Recent efforts have harnessed high‐throughput proteomics to systematically map how different NPs interact with complex protein mixtures (Ban et al. 2020; Blume et al. 2020; Gharibi et al. 2024; Liessi et al. 2021; Ouassil et al. 2022). For example, Gharibi et al. (2024) pioneered a uniform data processing pipeline to harmonize protein corona analyses across various proteomics core facilities, underlining that standardized protocols and consistent data processing are essential to achieving reproducible, cross‐laboratory insights. Blume et al. (2020) introduced a multi‐nanoparticle profiling approach to achieve rapid and precise plasma proteome characterizations, revealing how subtle surface modifications can shift the composition of the adsorbed proteome. Beyond identifying which protein adsorbs, there is a growing emphasis on understanding how they bind to nanomaterial surfaces and subsequently interact in biological systems. Liessi et al. (2021) used isobaric labeling proteomics to probe protein corona orientation, demonstrating that the presence and specific binding orientation of proteins can change NP–biological interactions. Such mechanistic details help clarify phenomena like opsonization and immune clearance. Meanwhile, Ban et al. (2020) employed ML to link protein corona composition with downstream cellular recognition, implying that data‐driven models can predict how corona formation may alter a NP's therapeutic or toxicological profile. Ouassil et al. (2022) further extended these predictive approaches to carbon nanotubes, using a supervised learning model to forecast which proteins would preferentially adsorb. Overall, these advances illustrate a shift from purely observational proteomics to an era of high‐throughput, predictive, and standardized protein corona research in which harmonized pipelines and robust computational analyses uncover the detailed molecular mechanisms that dictate nanomaterial fate in vivo.

5.3. Data Curation in Nanomedicine

High‐throughput experimentation and proteomic analysis produce extensive datasets on NP formulations, protein corona profiles, and in vivo performance. Data curation converts these raw findings into standardized, annotated, and shareable resources that enable cross‐laboratory collaboration and effective AI/ML applications. In nanomedicine, this process is vital for linking physicochemical descriptors (e.g., size, surface charge, and coating) with biological outcomes (e.g., biodistribution, efficacy, and toxicity) so that ML models can draw meaningful correlations and make appropriate predictions. While many research teams compile in‐house datasets for specific formulations, a lack of comprehensive access to the datasets and reporting inconsistencies often limit broader utility. Public repositories address this gap by enforcing uniform data structures and promoting large‐scale collaborations. Table 1 shows several key curated databases and peer‐reviewed datasets designed to integrate parameters such as NP physicochemical characterization, protein corona profiles, synthesis and experimental analysis conditions, toxicological characterization, and more. Each database has a unique focus: eNanoMapper provides a semantic infrastructure for nanoparticle data integration and supports modeling applications (Jeliazkova et al. 2015); caNanoLab specializes in cancer nanomedicine and includes detailed NP characterization linked to oncology research (Gaheen et al. 2013); Nano‐Tumor Database focuses on pharmacokinetics, tissue distribution, and tumor delivery of NPs (Chen et al. 2023); PubVINAS (Yan et al. 2020) aggregates simulation data relevant to NP‐biomolecule interactions; and S²NANO supports multimodal data upload and visualization of omics, physicochemical, and toxicological data. While most of the listed platforms act as data repositories for comprehensive reporting and sharing of data, some platforms like eNanoMapper (Jeliazkova et al. 2015) and PubVINAS (Yan et al. 2020) also offer integrated modeling tools for public use. Platforms like caNanoLab and S²NANO depend largely on community‐driven submissions, while others involve manual curation through literature search and text or data mining. Merging data from multiple sources in this way poses several challenges, particularly in the normalization of extracted data into meaningful and comparable results. This is especially true for those that offer modeling services as their accuracy and reproducibility are highly dependent on the input data quality. Individual research projects may use a variety of experimental methods that may not be directly comparable or even lack crucial analytical methods that may influence NP characterization or biological endpoints. For example, there are multiple approaches for measuring and reporting expression levels of proteins from NP‐protein corona studies, like stable isotope labeling, isobaric tagging, or label‐free methods using spectral counts or signal intensity (Bantscheff et al. 2012; Rozanova et al. 2021). Some groups have attempted to handle these reporting differences by normalizing relative protein expression levels by molecular weight (as relative protein abundance) (Ban et al. 2020; Canchola et al. 2025), which allows for direct comparison between cases. However, even this approach is limited due to the inherent incompatibility between some protein measurements.

TABLE 1.

Curated databases and published datasets for nanomaterials.

Database/study	Link	No of data points	Data source	Key feature	References
Curated Repositories & Knowledgebases
Ai2Tox	https://wb.ai2tox.com	> 800	Literature	Public database for nanoparticle characterization data, quantitative protein corona profiles, and analytical protocols for protein corona analysis	Canchola et al. (2025)
caNanoLab	https://cananolab.cancer.gov/#/	> 1600	Data contributed by Individual Laboratories	Annotated database of nanoparticle designs, design protocols, and physicochemical and toxicological characterizations	Gaheen et al. (2013)
eNanoMapper	https://www.enanomapper.net/	> 5000	Literature, Data contributed by Individual Laboratories	Public database of nanomaterial characterization data; provides resources for ontology and QSAR modeling	Jeliazkova et al. (2015)
NBI Knowledgebase	https://nbi.oregonstate.edu/	> 200	Literature	Repository for nanomaterial synthesis protocols, characterization, and biological interactions (e.g., ADME data, cytotoxicity, and more)	—
Public Virtual Nanostructure Simulation (PubVINAS)	https://www.pubvinas.com/	> 700	Literature, Data contributed by Individual Laboratories	A curated database of annotated nanomaterial characteristics and biological interactions; provides tools for data visualization and predictive modeling	Yan et al. (2020)
S2NANO	http://portal.s2nano.org/	> 33,000	Data contributed by Individual Laboratories	Data repository for nanoparticle synthesis, characterization data, and toxicological characterization (e.g., cytotoxicity); provides tools for QSAR and cytotoxicity prediction modeling	—
Published Curated Datasets
Nanoparticle protein corona profiles	—	> 650	Literature, Experimental Data	Dataset of nanoparticle characterization data and quantitative protein corona profiles; missing nanoparticle property data experimentally derived	Ban et al. (2020)
Large‐scale inorganic nanoparticle	—	> 750	Literature	Dataset of inorganic nanoparticle outcomes for tumor delivery and therapeutic efficacy	Mendes et al. (2024)
Nanoparticle protein corona overview	—	> 1700	Literature	Dataset of nanoparticle characterization data and analytical protocols for nanoparticle preparation and protein corona characterization	Hajipour et al. (2023)
Nano‐Tumor Database	—	> 2000	Literature	Nano‐Tumor Database of nanoparticle biodistribution in plasma, tumor, and other organ compartments	Chen et al. (2023)

Note: All web links were accessed in March 2025; “‐” represents not available.

To address these challenges, some researchers have begun advocating for the use of FAIR (Findable, Accessible, Interoperable, Reusable) principles as a means of enhancing the usability and longevity of datasets in nanomedicine (Ammar et al. 2024; Kochev et al. 2020). This initiative encourages the publication and curation of datasets that are well‐annotated, machine‐readable, and standardized across repositories, to enable seamless integration into repositories, cross‐study comparisons, and AI‐driven analyses. By implementing FAIR principles, these platforms can facilitate large‐scale data harmonization, improve reproducibility, and support advanced computational modeling efforts in nanomedicine. By curating detailed protein corona information in these repositories, researchers can better understand how subtle corona changes influence cellular uptake or immune recognition.

6. Challenges and Limitations of AI/ML in Nanomedicine

As the integration of AI and ML expands the frontiers of nanomedicine research, several obstacles and constraints have become apparent. Despite notable successes in accelerating the design of LNPs for medical therapy and uncovering hidden structure–activity relationships, these computational methodologies face hurdles in data quality and standardization, model validation, and practical implementation. These key challenges must be addressed to fully leverage the potential of AI/ML in nanomedicine.

6.1. Data Constraints and Standardization

One of the most critical obstacles in developing AI/ML methods for nanomedicine lies in maintaining data quality, ensuring diversity, and curating an adequately sized dataset. Gharibi et al. (2024) highlight how a uniform processing pipeline for protein corona analysis across proteomics facilities can dramatically enhance reproducibility, illustrating the impact that consistent metadata collection and analytical standards can have on multi‐lab studies. Nevertheless, the overall availability of well‐annotated datasets remains limited because researchers do not routinely publish their raw data or provide comprehensive protocols, hindering collaborative efforts to build robust ML models. This shortfall is compounded by the fact that, even when data is shared, it may come in disparate formats or lack key experimental details, further complicating data integration. To address these gaps, data curation has emerged as an essential strategy for compiling, harmonizing, and disseminating nanomaterial information for computational model development. Several domain‐specific databases have been created in the past decade to meet this need, including caNanoLab (https://cananolab.cancer.gov/#/) established by the National Cancer Institute (NCI) in 2007 to accumulate data on nanotechnologies used in cancer care (Gaheen et al. 2013). By collecting details on preclinical safety, drug efficacy, nano‐bio interactions, and characterization parameters, caNanoLab provides a central resource aligned with National Institutes of Health (NIH) data‐sharing guidelines, thus streamlining the clinical translation of nanomedicine strategies. Furthermore, Bender and Cortes‐Ciriano (2021a) highlight how data comprehensiveness and bias‐free curation are indispensable for effective ML‐driven predictions, warning that limited datasets can cause models to overlook promising clinical candidates. Ultimately, large‐scale, standardized, and openly accessible data repositories supplemented by structured curation frameworks are indispensable for advancing AI/ML applications in nanomedicine.

6.2. Model Generalizability and Validation

While high‐quality datasets form the foundation for robust AI/ML models in nanomedicine, the capacity of these models to generalize beyond narrowly defined tasks remains a critical hurdle (Bender and Cortes‐Ciriano 2021a). Many models heavily rely on in vitro data, such as LNP transfection efficiency measured in a specific cell line (Ding et al. 2023; Wu et al. 2024), but struggle to capture the more complex realities of in vivo conditions (Zhu et al. 2022). Translating these in vitro findings to animal models or human trials involves additional factors like immune responses, tissue barriers, and off‐target effects, complicating model predictions (Carrasco et al. 2021). To bridge this gap, researchers have begun pre‐training models on expansive in vitro datasets before fine‐tuning them into smaller in vivo sets; however, the scarcity of relevant in vivo data often limits comprehensive validation (Bae et al. 2025; Shan et al. 2024). This shortfall has led to growing interest in few‐shot AI/ML methods, which can maintain predictive power even when trained on minimal data. Overcoming these constraints will require rigorous multi‐platform validation encompassing diverse NP formulations and administration routes together with ongoing refinement of computational frameworks to enhance their ability to generalize to clinically relevant scenarios (Wang, Chen, et al. 2024). While the above discussion focuses on bridging in vitro and in vivo complexities, other significant hurdles such as algorithmic bias, data scarcity, and regulatory uncertainties also shape the broader landscape of AI‐driven nanomedicine. Table 2 summarizes these key challenges and proposes potential solutions, ranging from adopting FAIR principles for data sharing to integrating “robot scientist” platforms for high‐throughput experimental validation (Bender and Cortes‐Ciriano 2021a; Dembski et al. 2023).

TABLE 2.

Key challenges and potential solutions in AI/ML‐driven nanomedicine.

Challenge	Impact on research	Potential solutions/strategies
Data Scarcity and Quality	Insufficient, inconsistent data hampers model training and reduces predictive power Gharibi et al. (2024)	Develop centralized data‐sharing platforms (caNanoLab, eNanoMapper); adopt FAIR principles; encourage multi‐lab collaborations Bender and Cortes‐Ciriano (2021b)
Model Generalizability	In vitro–trained models often fail to predict in vivo outcomes, limiting clinical relevance Wang, Chen, et al. (2024)	Conduct multi‐tier validations (cell lines → animals → clinical); leverage transfer learning or physics‐informed AI to bridge in vitro–in vivo gaps
Algorithmic Bias	Overfitting can skew results toward the most common nanoparticle types or experimental conditions, missing rare cases	Diversify training datasets; apply cross‐validation and interpretability frameworks (e.g., SHAP values); incorporate domain knowledge to reduce systematic bias
Regulatory & Ethical Hurdles	Lack of specific FDA guidelines for AI‐driven nanomedicine slows translation; ethical concerns over data privacy persist	Engage regulators early to define clear pathways; adopt transparent and explainable ML protocols; develop ethical guidelines for patient data use
Scalability of Experimental Validation	Complex and iterative design–synthesis–testing cycles limit throughput	Integrate high‐throughput platforms with “robot scientist” systems and active learning; automate iterative experimentation for faster model improvement

7. Conclusion

Nanomedicine presents extraordinary opportunities for precision drug delivery, but progress has been hampered by complex design variables, varied in vivo performance, and a paucity of reliable predictive models. Recent AI/ML breakthroughs have advanced the field, enabling researchers to accelerate NPs design cycles, analyze high‐throughput data, and refine preclinical predictions. Integrative platforms that combine computational modeling with robust experimental data, such as DNA barcoding for NP libraries and large‐scale proteomic screening for protein corona studies demonstrate how innovative pipelines can drive rational formulation, optimize endosomal escape mechanisms, and adapt to complex biological environments. At the same time, the success of AI‐enhanced nanomedicine hinges on transparent data sharing, high‐quality datasets, and thoughtful regulatory guidance. Challenges persist in scaling these models, ensuring they generalize clinically relevant scenarios, and addressing ethical concerns around patient data and algorithmic fairness. Nonetheless, the growing synergy between AI/ML and nanomedicine promises a more rational, data‐driven era in therapeutic development, with higher efficiencies and reduced failure rates. Future efforts will likely focus on fostering interdisciplinary collaborations, refining mechanistic insights through physics‐informed models, and expanding regulatory frameworks to accommodate adaptive, real‐time AI systems. As these endeavors evolve, the horizon of truly personalized, safe‐by‐design NP therapies becomes increasingly attainable.

Author Contributions

Wei‐Chun Chou: conceptualization (lead), data curation (lead), funding acquisition (lead), investigation (lead), methodology (lead), project administration (lead), resources (lead), supervision (lead), visualization (lead), writing – original draft (lead). Alexa Canchola: conceptualization (equal), data curation (equal), formal analysis (equal), investigation (equal), methodology (equal), visualization (equal), writing – original draft (equal). Fan Zhang: writing – review and editing (equal). Zhoumeng Lin: conceptualization (lead), funding acquisition (lead), project administration (lead), resources (equal), supervision (equal), writing – review and editing (equal).

Conflicts of Interest

The authors declare no conflicts of interest.

Chou, W.‐C. , Canchola A., Zhang F., and Lin Z.. 2025. “Machine Learning and Artificial Intelligence in Nanomedicine.” Wiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology 17, no. 4: e70027. 10.1002/wnan.70027.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.