Artificial intelligence and machine learning-guided bio-orthogonal engineering of smart soft polymeric nanocarriers for precision drug delivery and translational nanomedicine

1 Introduction

Soft polymeric nanocarriers, including nanogels, polymeric nanoparticles, micelles, polymersomes, and dendritic assemblies, have emerged as versatile platforms for precision drug delivery, imaging, and theranostics. Their biomedical value arises from tunable size, deformability, degradability, surface chemistry, and cargo-loading capacity. These features can improve payload protection, circulation stability, tissue access, stimulus-responsive release, and biological selectivity. Hydrogels, microgels, nanogels, and clinically relevant nanoparticle systems have therefore become important foundations for modern drug-delivery research (Jiang et al., 2014; Buwalda et al., 2017; Anselmo and Mitragotri, 2016; 2019; 2021; Mitchell et al., 2021).

Despite this promise, rational design remains difficult because nanocarrier performance reflects a coupled network of physicochemical and biological variables. Polymer molecular weight, monomer composition, architecture, crosslink density, mesh size, and viscoelasticity are among the variables that govern nanocarrier behavior. Together with surface hydration, charge, degradation rate, and ligand density, these parameters collectively influence encapsulation efficiency, release kinetics, protein corona formation, immune recognition, cellular uptake, biodistribution, and pharmacokinetic–pharmacodynamic behavior. Mechanical softness is increasingly recognized as a functional design parameter: nanoparticle elasticity can modulate circulation, macrophage uptake, tumor delivery, tissue penetration, and even transport across biological barriers (Anselmo et al., 2015; Rancan et al., 2016; Chen et al., 2022; Wang et al., 2024). Protein corona studies further show that nanocarrier identity in biological fluids may differ substantially from the pristine material identity, altering targeting, transport, and therapeutic response (Ke et al., 2017; Alberg et al., 2020; Berrecoso et al., 2020; Singh et al., 2021).

Bioorthogonal chemistry offers a strategy to introduce complex functionality into soft nanocarriers with high chemical selectivity under mild conditions. Reactions such as strain-promoted azide–alkyne cycloaddition (SPAAC), copper-catalyzed azide–alkyne cycloaddition (CuAAC), and inverse electron-demand Diels–Alder (IEDDA) ligation enable modular ligand installation, imaging, pretargeting, and activation chemistry (Ramil and Lin, 2013; Li et al., 2016; Handula et al., 2021; Taiariol et al., 2022; Adhikari et al., 2024). In parallel, artificial intelligence and machine learning (AI/ML) are becoming useful tools for navigating formulation complexity, linking descriptors with performance, and prioritizing experiments in nanomedicine development (Jayasinghe et al., 2022; Hickman et al., 2023; Chou et al., 2025).

In this Mini Review, we discuss how bioorthogonal chemistry and AI/ML can be integrated to guide the rational engineering of smart soft polymeric nanocarriers, with emphasis on modular functionalization, predictive formulation, biological performance, and translational challenges.

2 Bioorthogonal chemistry for modular engineering of soft polymeric nanocarriers

Bioorthogonal reactions are particularly attractive for soft nanocarriers because they separate particle formation from later-stage functionalization and can proceed without substantially disturbing hydrated polymer networks. SPAAC enables copper-free conjugation between azides and strained cyclooctynes, making it attractive for biomolecule coupling and post-fabrication surface engineering. CuAAC remains valuable for high-yield polymer synthesis and scaffold construction, although copper-associated toxicity and purification requirements constrain direct biological use. IEDDA ligation, commonly based on tetrazine and trans-cyclooctene chemistry, offers exceptionally rapid kinetics for pretargeted imaging, rapid labeling, and triggered release (Ramil and Lin, 2013; Li et al., 2016; Handula et al., 2021; Tomarchio et al., 2024; Adhikari et al., 2024). The principal soft polymeric nanocarrier platforms, their distinguishing physicochemical features, and their relative suitability for bioorthogonal modification are compared in Table 1.

TABLE 1. Comparison of principal soft polymeric nanocarrier platforms with respect to physicochemical characteristics, suitability for bioorthogonal modification, key advantages, and principal limitations.

Beyond these three well-established platforms, several emerging bioorthogonal reactions are attracting attention for nanomedicine applications. Staudinger ligation between azides and triarylphosphines was among the earliest truly bioorthogonal reactions and, while generally slower than SPAAC or IEDDA, remains relevant for specific contexts where aqueous compatibility and functional group selectivity are prioritized (Prescher and Bertozzi, 2005). Sydnone–alkyne cycloadditions offer an attractive alternative to azide-based chemistry with tunable kinetics and minimal background reactivity in biological media (Wallace and Chin, 2014). Isonitrile-based reactions, including the α-ketoacid–hydroxylamine (KAHA) ligation and related variants, have been explored for their potential in bioconjugation under physiologically relevant conditions (Nguyen and Prescher, 2020). Nitrone–alkyne cycloadditions and diazo-transfer reactions represent additional handles that may find application in the design of stimuli-responsive nanocarriers. Collectively, expanding the bioorthogonal toolkit provides researchers with greater flexibility to select reaction pairs matched to the specific surface environment, kinetic requirements, and downstream functional objectives of a given nanocarrier system, and represents an active area for future integration with AI-guided design.

The core reactions and their roles in soft nanocarrier engineering are summarized in Figure 1.

In polymeric nanocarriers, bioorthogonal reactions can support four major functions. First, they enable network construction and crosslink-density control in nanogels and related soft matrices. Bioorthogonal or IEDDA-mediated nanogel formation has been used to generate responsive and degradable networks with controlled size and release characteristics (Oehrl et al., 2020; Alkanawati et al., 2021). Second, they facilitate post-fabrication conjugation of targeting ligands, proteins, peptides, or reporter units, allowing the core carrier formulation and surface biology to be tuned independently (Taiariol et al., 2022). Third, they provide rapid reaction handles for imaging and pretargeted delivery. Tetrazine–trans-cyclooctene chemistry has enabled self-assembled multimodal imaging nanoparticles, polymer-based pretargeting agents, and radiolabeling of long-circulating liposomal nanomedicines (van Onzen et al., 2019; Stéen et al., 2020; Mishra et al., 2024). Fourth, bioorthogonal chemistry can be leveraged for therapeutic activation, exemplified by click-mediated tumor-targeting strategies and release-triggered nanodrug concepts (Yoo et al., 2019; Tomarchio et al., 2024).

Nanogels are especially suitable for this modularity because their hydrated polymer networks can couple cargo retention with chemically addressable interfaces. Prior work on enzyme-responsive polyethylene glycol nanogels, redox-active nitroxide nanogels, and enzyme-loaded polymeric nanogels demonstrates how polymer composition, crosslinking, and responsive functionality can regulate antioxidant activity, cargo protection, and biochemical function (Basak et al., 2021; Basak et al., 2022; Basak et al., 2024; Basak, 2025; Basak and Das, 2025a). In related soft delivery systems, zwitterionic and stimuli-responsive liposomes, wound-healing nanogels, and inflammatory cardiovascular hydrogels further illustrate how soft-matter design principles influence biological outcomes (Basak and Das, 2025a; Basak et al., 2025; Basak et al., 2025b; Basak and Das, 2025c; Basak and Das, 2026).

However, reaction efficiency at nanocarrier interfaces is not guaranteed by solution-phase chemistry. Steric crowding, local polarity, polymer hydration, corona formation, ligand size, surface curvature, and burial of reactive handles can affect conversion and final ligand presentation. These challenges suggest that bioorthogonal design should be considered not only as a chemical problem, but also as a soft-matter optimization problem suited to quantitative modeling.

From a translational perspective, bioorthogonal functionalization strategies face additional barriers beyond chemical efficiency. The long-term in vivo stability of bioorthogonal linkages under physiological conditions — including susceptibility to hydrolysis, enzymatic cleavage, or competitive displacement by endogenous biomolecules — must be systematically characterized for each carrier system. Immunogenicity of non-natural chemical handles such as strained cyclooctynes or tetrazine motifs remains incompletely understood, and their potential to elicit immune responses upon repeated administration requires careful preclinical evaluation. Regulatory acceptance of nanocarriers bearing non-standard chemical functionalities will further require robust safety and stability data packages that go beyond what is typically generated in academic proof-of-concept studies.

3 AI/ML-guided predictive design of smart soft nanocarriers

The design of soft polymeric nanocarriers is inherently multidimensional. Small changes in polymer architecture, formulation conditions, reaction conditions, or surface chemistry can substantially alter particle size, polydispersity, encapsulation efficiency, release behavior, and biological fate. AI/ML methods are valuable because they can detect nonlinear structure–property–performance relationships that are difficult to establish through one-variable-at-a-time experimentation (Jayasinghe et al., 2022; Chou et al., 2025). Representative AI/ML approaches currently applied in nanomedicine, together with their input descriptors, predicted outputs, advantages, and limitations, are summarized in Table 2.

TABLE 2. Representative AI/ML approaches applied to nanomedicine, including model type, input descriptors, predicted outputs, key advantages, limitations, and supporting literature.

3.1 Soft-matter descriptors and formulation variables

For AI/ML-guided nanocarrier design, experimentally meaningful soft-matter properties must be converted into quantitative descriptors. Relevant features include polymer molecular weight, monomer composition, hydrophilic–hydrophobic balance, charge density, crosslink density, mesh size, swelling ratio, viscoelasticity, ligand density, particle size, polydispersity, and degradation kinetics. These descriptors can be linked to outputs such as drug loading, particle stability, release profile, surface conjugation, uptake, toxicity, biodistribution, and therapeutic performance.

Polymer-specific examples already show that neural-network models can predict nanoparticle size from formulation chemistry, while ML-assisted formulation studies have linked process variables with nanocarrier size and dispersity (Kimmig et al., 2021; Di Francesco et al., 2023). It is useful to briefly situate these applications within the broader landscape of AI/ML algorithm classes relevant to nanocarrier design. Supervised learning models — including artificial neural networks (ANNs), random forests, gradient boosting machines, and support vector regression — are the most widely applied, mapping experimentally measured input descriptors to quantitative output targets such as particle size, encapsulation efficiency, or release rate. Deep learning architectures, including convolutional neural networks (CNNs) and graph neural networks (GNNs), have begun to find application where molecular structure or spatial relationships are important inputs, such as in lipid nanoparticle composition screening or polymer sequence design. Unsupervised learning methods, including principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and clustering algorithms, are valuable for identifying latent structure in high-dimensional nanocarrier datasets and revealing groupings of formulations with similar biological behavior. Reinforcement learning and Bayesian optimization provide principled frameworks for sequential experimental design, selecting the next experiment to maximize information gain or predicted performance improvement. Generative models, including variational autoencoders (VAEs) and generative adversarial networks (GANs), represent an emerging frontier for de novo nanocarrier design, proposing entirely new polymer compositions or formulation conditions rather than interpolating within existing datasets. Each algorithm class carries distinct assumptions, data requirements, and interpretability characteristics that should guide model selection for specific nanocarrier design objectives. Several experimental studies have begun to close the loop between AI/ML prediction and biological validation. Di Francesco et al. (2023) employed a machine learning-instructed microfluidic platform to optimize the synthesis of curcumin-loaded liposomes, demonstrating that model-guided selection of process parameters produced formulations with improved size control and encapsulation efficiency compared with conventional design-of-experiment approaches. Bao et al. (2025) applied an active-learning workflow to nanomedicine optimization, integrating automated experimentation with predictive modeling to iteratively improve nanoformulation performance with substantially fewer experiments than traditional screening. Witten et al. (2025) used deep learning to screen large libraries of lipid nanoparticle compositions for pulmonary gene delivery, and experimentally confirmed that AI-selected candidates achieved improved transfection efficiency in relevant biological models. In the hydrogel domain, Cheong et al. (2026) demonstrated that automated active learning could prospectively optimize drug release profiles, with model-guided formulations validated against independently measured release kinetics. These examples illustrate that AI/ML-guided design is beginning to yield experimentally confirmed advances rather than remaining purely predictive in silico exercises, though biological validation in more complex in vivo settings remains limited. In the context of soft polymeric nanocarriers specifically, ML models have also been applied to predict the release behavior of hydrogel-based systems from polymer crosslink density and swelling parameters, and to classify nanoparticle–cell interactions from high-content imaging data, illustrating the breadth of experimental readouts that can serve as AI/ML training targets (Jayasinghe et al., 2022; Chou et al., 2025).

3.2 Predicting bio orthogonal functionalization and biological behavior

A particularly promising direction is reaction-aware modeling of bioorthogonal functionalization. Inputs such as click-handle density, ligand size, reaction class, solvent composition, pH, time, temperature, and nanocarrier surface environment could be used to predict conjugation yield, surface accessibility, or functional ligand density. Such models would help determine whether SPAAC, CuAAC, or IEDDA is most suitable for a specific carrier design objective.

AI/ML can also support prediction of downstream biological behavior. Protein corona formation, cellular uptake, macrophage recognition, and release behavior may all be influenced by combined physicochemical properties rather than any single variable. Protein-corona studies on polymeric and targeted nanoparticles demonstrate that surface chemistry and ligand conjugation can alter corona composition and biological fate, providing important variables for future predictive models (Sebak et al., 2020; Berrecoso et al., 2020; Singh et al., 2021). Despite the recognized importance of the protein corona, its systematic incorporation into ML predictive frameworks remains an underexplored research gap. Current models largely treat protein corona formation as a confounding variable rather than an explicitly encoded feature, in part because comprehensive, standardized proteomic datasets paired with nanocarrier physicochemical descriptors are not yet widely available. Future models could integrate mass spectrometry-derived corona composition data — including the relative abundance of opsonins, dysopsonins, and apolipoproteins — as additional input features linked to uptake, immune evasion, and in vivo biodistribution outcomes. Generating such paired datasets in a standardized format, and developing dimensionality-reduction strategies capable of handling high-dimensional proteomic inputs alongside lower-dimensional material descriptors, represents an important methodological priority. Achieving this integration would substantially improve the biological realism of predictive models and could help close the gap between in vitro formulation performance and in vivo therapeutic behavior. In silico and AI-guided nanomedicine studies further suggest that modeling may eventually aid prediction of biodistribution, toxicity, and therapeutic response, although generalizability remains constrained by dataset heterogeneity and biological complexity (Jayasinghe et al., 2022; Chou et al., 2025).

3.3 Closed-loop optimization and active learning

The integration of AI/ML with automated synthesis and modular chemistry could shift nanocarrier development toward closed-loop discovery. Self-driving laboratory concepts combine high-throughput experimentation, machine learning, and iterative decision-making to reduce experimental burden and accelerate optimization (Hickman et al., 2023). However, practical implementation of self-driving laboratories for nanocarrier development faces substantial challenges that are not always apparent from proof-of-concept demonstrations. Laboratory automation requires significant capital investment in robotics, liquid-handling systems, inline characterization equipment, and data infrastructure, which limits accessibility to well-resourced research environments. Automated synthesis platforms may not yet reproduce the full range of soft nanocarrier fabrication methods, particularly those involving complex self-assembly, nanoprecipitation, or multi-step surface functionalization that are sensitive to precise manual technique. Data integration across heterogeneous instruments — each generating output in different formats and with different calibration requirements — adds computational complexity and potential for systematic error. Model updating presents a further challenge: as new experimental data are generated, predictive models must be retrained or fine-tuned without losing performance on previously learned relationships, a problem known as catastrophic forgetting in continual learning systems. Finally, the throughput advantage of automated platforms is only realized if the biological assays used to evaluate nanocarrier performance can also be miniaturized and automated, which is more difficult for complex cell-based or in vivo readouts than for physicochemical characterization. Alongside prediction accuracy, uncertainty quantification (UQ) deserves dedicated attention in AI-guided nanocarrier design. Most published ML models in nanomedicine report point predictions — single estimated values for size, encapsulation efficiency, or biological uptake — without accompanying confidence intervals or estimates of prediction uncertainty. For translational applications, knowing how confident a model is in a given prediction is at least as important as the prediction itself, because overconfident recommendations for formulations or functionalization conditions that lie outside the model’s reliable applicability domain can mislead experimental prioritization. Probabilistic approaches such as Bayesian neural networks, Gaussian process models, Monte Carlo dropout, and conformal prediction frameworks can provide calibrated uncertainty estimates alongside predictions, allowing researchers to distinguish between high-confidence and exploratory recommendations. Reporting uncertainty alongside predictions should be treated as a minimum standard for AI models intended to inform translational design decisions, and future closed-loop pipelines should use uncertainty as an explicit criterion for experimental selection, prioritizing measurements that most efficiently reduce model uncertainty in the design space. Recent active-learning workflows for nanoformulations and hydrogel drug-release profiles demonstrate the practical value of model-guided experimental selection in complex delivery systems (Bao et al., 2025; Cheong et al., 2026). Deep learning has also enabled large-scale in silico screening of lipid nanoparticle structures for nucleic acid delivery, illustrating how data-driven discovery can move beyond minor parameter adjustment toward chemically diverse design spaces (Witten et al., 2025).

This workflow logic is summarized in Figure 2.

For soft polymeric nanocarriers, a closed-loop pipeline could begin with a modular polymer scaffold, generate bioorthogonally functionalized variants, quantify formulation and biological outputs, train predictive models, and propose the next set of candidates. Such systems may be especially useful when the desired formulation must simultaneously satisfy release control, ligand presentation, immune compatibility, manufacturability, and stability constraints.

3.4 Limitations and reliability of current AI/ML models

Despite their promise, AI/ML models applied to nanomedicine face several important and underappreciated limitations that can be organized around three interconnected concerns: data dependency, model validation, and experimental reproducibility. Data scarcity remains a fundamental constraint: most nanocarrier datasets are small, compiled from heterogeneous experimental protocols, and lack the scale required to train generalizable deep learning models. This scarcity increases susceptibility to overfitting, wherein models achieve high performance on training data but fail to predict outcomes for structurally or compositionally novel carriers. Systematic bias is a related concern — if training datasets overrepresent certain polymer classes, size ranges, or cell lines, model predictions will reflect those biases rather than true physicochemical or biological relationships.

External validation is rarely reported in published nanomedicine AI studies. Most models are evaluated on held-out portions of the same dataset used for training, which does not adequately test transferability to different laboratories, instrumentation, or biological systems. Poor reproducibility of experimental inputs — arising from batch variability, inconsistent purification, or protocol differences — further undermines model reliability, because errors in training labels propagate directly into prediction uncertainty. Addressing these limitations will require coordinated investment in curated benchmark datasets, standardized experimental reporting, and prospective external validation studies, ideally across multiple institutions.

4 Discussion

While the convergence of bioorthogonal chemistry and AI/ML provides a compelling framework for next-generation soft nanocarrier engineering, a balanced assessment requires explicit consideration of the scientific, biological, manufacturing, and regulatory challenges that currently limit translation of these approaches from proof-of-concept studies to clinically viable products. Bioorthogonal chemistry offers precise molecular handles for modular functionalization, while AI/ML provides a way to integrate multivariable information and guide design choices. Together, they could transform formulation development from descriptive trial-and-error work into a more predictive and iterative process.

Several opportunities emerge from this integration. AI/ML can assist in selecting polymer architectures, crosslinkers, and ligand densities before synthesis. Reaction-aware models may improve selection among SPAAC, CuAAC, and IEDDA chemistries for specific nanocarrier environments. Multimodal datasets that combine reaction conversion, nanocarrier structure, protein corona features, uptake, release, and in vivo behavior could reveal design rules that remain inaccessible through isolated experiments. Bioorthogonal modularity may also support more personalized delivery systems by enabling rapid adjustment of surface ligands, activation motifs, or imaging functions in response to disease-specific biological cues.

A natural extension of this vision is the integration of multi-omics data — including genomics, transcriptomics, proteomics, and metabolomics — alongside patient-specific biomarkers and digital health information into AI-guided nanocarrier design workflows. Tumor microenvironment heterogeneity, patient immune profiles, disease-stage-dependent receptor expression, and pharmacogenomic variation all influence nanocarrier behavior and therapeutic outcomes in ways that cannot be captured by material-level descriptors alone. Incorporating such biological complexity into predictive models would represent a meaningful step toward truly individualized nanomedicine, where formulation decisions are informed not only by general polymer–biology relationships but by the molecular landscape of the individual patient. This ambition requires not only algorithmic advances but also infrastructure for linking materials databases with clinical and omics data repositories in a privacy-preserving and ethically governed manner, which itself remains a significant organizational and regulatory challenge.

It is also worth noting that the AI/ML–bioorthogonal chemistry framework discussed in this review is not inherently limited to drug delivery. The same design logic — modular surface functionalization, data-driven optimization of soft-matter properties, and predictive biological performance modeling — could be applied to tissue engineering scaffolds, where control over degradation rate, mechanical compliance, and cell-adhesion ligand density are critical design parameters. Biosensing platforms incorporating bioorthogonally functionalized hydrogels or nanoparticle transducers could benefit from AI-guided optimization of signal-to-noise, selectivity, and stability. Theranostic systems requiring simultaneous imaging and therapeutic function could leverage bioorthogonal radiolabeling and AI-assisted multimodal data integration. Regenerative medicine applications, including cell encapsulation and controlled delivery of morphogenic factors, present design challenges — particularly around long-term mechanical stability and host integration — that are well-suited to the iterative closed-loop approaches described here. Extending the framework to these areas would broaden both the scientific impact and the translational reach of AI-guided bioorthogonal nanocarrier engineering.

Important barriers remain. First, standardized and interoperable datasets represent a foundational requirement that the field has not yet adequately addressed. Studies reporting polymer composition, nanocarrier physicochemical properties, bioorthogonal functionalization efficiency, and biological assay results routinely use different experimental protocols, characterization methods, statistical reporting standards, and biological readout definitions. This heterogeneity prevents meaningful cross-study aggregation and limits the development of models capable of generalizing across research groups or nanocarrier platforms. What is needed are community-agreed minimum reporting standards for soft nanocarrier studies — analogous to MIAME standards in genomics — that specify which physicochemical descriptors, biological assays, and processing metadata must be reported to enable downstream computational use. Findable, accessible, interoperable, and reusable (FAIR) data principles should guide the curation of nanomedicine repositories, and journals in the field could play a role in mandating structured data deposition alongside publication. Without such infrastructure, AI/ML models will continue to be trained on fragmentary and incompatible data, severely limiting their predictive reliability and translational impact. Second, soft nanocarriers are sensitive to batch-to-batch variability, purification conditions, and storage history, which complicates reproducibility and model transferability. Third, model interpretability — and more specifically the development of explainable artificial intelligence (XAI) approaches — represents an essential and increasingly urgent priority. Translational researchers and regulatory agencies will require evidence that computational predictions are traceable to physically meaningful features and mechanistically credible, rather than merely statistically accurate on a held-out test set. Black-box models such as deep neural networks may achieve high predictive accuracy while offering little insight into which polymer or biological features drive their predictions, which substantially limits their utility for hypothesis generation, formulation troubleshooting, and regulatory review. XAI methods such as SHAP (SHapley Additive exPlanations) value analysis, attention-map visualization, integrated gradients, and concept activation vectors can provide post hoc interpretability for complex models (Lundberg and Lee, 2017). For nanomedicine specifically, such analyses could reveal, for example, which surface chemistry descriptors most strongly influence immune recognition, or which crosslink density ranges most reliably produce the desired release kinetics. Embedding interpretability from the model design stage — through architectures such as attention-based networks or physics-informed neural networks that constrain learning to physically plausible relationships — is preferable to purely post hoc interpretation and should be considered a design criterion rather than an afterthought. Fourth, biological translation is difficult because in vitro uptake or release results may not capture dynamic protein corona evolution, vascular transport, immune surveillance, or patient heterogeneity. Finally, scalable and GMP-compatible manufacturing must be incorporated into design logic from the earliest stages of nanocarrier development. Nanocarriers optimized solely for bench-scale performance may not survive translational constraints, even when they appear chemically elegant. AI/ML tools could play a meaningful role in addressing this challenge through several routes. Predictive models trained on scale-dependent process variables — such as mixing intensity, feed rate, solvent exchange dynamics, and temperature gradients — could help identify formulation parameters that are robust to scale-up rather than narrowly optimized for laboratory conditions. Process analytical technologies (PAT), including inline dynamic light scattering, turbidity monitoring, and Raman spectroscopy, generate continuous process data streams that are well-suited to machine learning-based quality monitoring and anomaly detection. Integration of AI models with PAT frameworks could enable real-time process control, detecting batch deviations and triggering corrective adjustments before product quality is compromised. For bioorthogonal functionalization steps, AI-guided optimization of reaction conditions at process scale — accounting for mass transfer limitations, reagent concentration gradients, and mixing heterogeneity — would further support the translation of bench-scale chemistry to manufacturing-compatible workflows. Incorporating these manufacturing-relevant constraints into closed-loop design pipelines from the outset, rather than treating manufacturing feasibility as a later-stage filter, could substantially reduce attrition between promising laboratory formulations and clinically viable products.

Regulatory considerations deserve explicit attention as AI/ML tools move closer to clinical workflows. Current regulatory frameworks for pharmaceuticals and nanomedicines — including those issued by the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) — were not designed with AI-assisted formulation development in mind, and considerable uncertainty remains about how AI-derived design decisions will be scrutinized during product review. The FDA’s emerging guidance on AI and machine learning in drug development emphasizes the need for model transparency, prospective validation, and documentation of training data provenance (U.S. Food and Drug Administration, 2021). For predictive models used to guide nanocarrier design, this translates to requirements for uncertainty quantification, clearly defined applicability domains, and evidence that model predictions generalize beyond training conditions. Regulatory agencies are also beginning to consider how continuous learning models — which update as new experimental data are generated — can be qualified without requiring full re-validation at each update. Engagement between computational nanomedicine researchers and regulatory scientists at an early stage, ideally through pre-submission meetings or inclusion in regulatory science research programs, would help develop shared standards before AI-designed nanomedicines reach clinical evaluation. Frameworks such as the FDA’s Digital Health Center of Excellence and the EMA’s reflection papers on complex innovative trial designs may provide useful precedents for how AI evidence packages could be structured (European Medicines Agency, 2023).

Overall, the field would benefit from four coordinated priorities: standardized and shareable datasets, automation-compatible modular chemistry, interpretable predictive models with uncertainty reporting, and translational criteria that include manufacturability, stability, and regulatory feasibility. With these elements in place, AI/ML-guided bioorthogonal engineering could help produce soft polymeric nanocarriers that are not only multifunctional and responsive, but also more reproducible, scalable, and clinically relevant. The envisioned translational progression is summarized in Figure 3.

5 Future outlook and research priorities

Several unresolved challenges and research priorities warrant coordinated attention from the nanomedicine, polymer chemistry, and computational communities over the next decade.

• Standardized, FAIR nanomedicine datasets. The absence of curated, interoperable datasets remains the most immediate constraint on AI/ML progress. Community-wide adoption of minimum reporting standards — specifying polymer descriptors, characterization methods, biological assay conditions, and processing metadata — would enable cross-study model training and meaningful external validation.
• Protein corona-integrated predictive models. The protein corona fundamentally redefines nanocarrier identity in biological environments, yet its systematic incorporation into ML models is nascent. Paired datasets linking corona proteomic composition with biodistribution and therapeutic outcome represent a high-priority experimental target.
• Uncertainty-aware and explainable AI. Future models must move beyond point predictions toward calibrated uncertainty estimates, and should be designed with interpretability as a primary criterion rather than an afterthought. Physics-informed and mechanistically constrained architectures may help achieve this without sacrificing predictive power.
• Automation-compatible bioorthogonal chemistry. The development of bioorthogonal reaction protocols compatible with robotic liquid handling, inline reaction monitoring, and high-throughput purification would accelerate the integration of modular functionalization into closed-loop discovery platforms.
• Multi-omics-informed personalization. Integrating patient genomic, proteomic, and metabolomic data with nanocarrier design models represents a transformative but technically complex goal. Early work on linking tumor microenvironment profiling with formulation selection would provide valuable proof-of-concept.
• Regulatory science co-development. AI-designed nanocarriers will require regulatory pathways that do not yet fully exist. Proactive engagement between researchers and regulatory agencies — including participation in regulatory science research programs and submission of voluntary data packages — will be essential for building the evidentiary frameworks needed for clinical translation.
• Manufacturing-aware design. Closed-loop optimization pipelines should incorporate manufacturing feasibility criteria — including scale-up robustness, GMP compatibility, and PAT integration — from the earliest design stages, rather than treating manufacturing as a final-stage constraint.

Progress across these priorities will require not only disciplinary depth but genuine interdisciplinary collaboration among polymer chemists, bioengineers, computational scientists, clinicians, and regulatory specialists.

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Footnotes

Edited by: Sugam Kumar, Bhabha Atomic Research Centre (BARC), India

Reviewed by: Amin Orash Mahmoudsalehi, Monterrey Institute of Technology and Higher Education (ITESM), Mexico

Monika Bhairam, Columbia Institute of Pharmacy (CIP), India

Footnotes

AI, artificial intelligence; ANN, artificial neural network; CNN, convolutional neural network; CuAAC, copper-catalyzed azide–alkyne cycloaddition; EMA, European Medicines Agency; FAIR, findable, accessible, interoperable, and reusable; FDA, U.S. Food and Drug Administration; GAN, generative adversarial network; GMP, good manufacturing practice; GNN, graph neural network; IEDDA, inverse electron-demand Diels–Alder; ML, machine learning; PAT, process analytical technology; PCA, principal component analysis; PD, pharmacodynamics; PK, pharmacokinetics; SHAP, SHapley Additive exPlanations; SPAAC, strain-promoted azide–alkyne cycloaddition; TCO, trans-cyclooctene; UQ, uncertainty quantification; VAE, variational autoencoder; XAI, explainable artificial intelligence.