Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics

Abstract

The integration of artificial intelligence, protein engineering, and sustainable nanomedicine is driving a paradigm shift in theranostics by enabling highly precise disease diagnosis and targeted therapy. AI-driven methodologies, including machine learning and deep learning, facilitate the rapid analysis of complex biological and chemical datasets, accelerating protein structure prediction, molecular docking, and structure-activity relationship modeling. These capabilities support the rational design of proteins and peptides with enhanced specificity, therapeutic efficacy, and safety, while enabling personalized treatment strategies tailored to individual molecular profiles. In parallel, sustainable nanomedicine focuses on the development of biodegradable, biocompatible, and environmentally benign nanomaterials to improve drug bioavailability, stability, and controlled release. AI-assisted optimization further refines nanocarrier design by balancing therapeutic performance with safety and environmental impact. Advanced intelligent nanocarriers capable of real-time monitoring, adaptive drug release, and degradation into non-toxic by-products represent a significant advancement over conventional static systems. The theranostic paradigm has become central to precision medicine, particularly in oncology, especially where AI-designed nanoplatforms enable targeted delivery of imaging agents and therapeutics to tumors, while allowing continuous treatment monitoring and minimizing off-target effects. Emerging applications in neurological, infectious, and cardiovascular diseases further highlight the broad clinical potential of this approach. Accordingly, this review summarizes AI-driven protein design strategies, sustainable nanocarrier engineering, and their convergence in next-generation theranostic systems, critically discussing mechanistic insights, translational challenges, and design principles required for developing safe, scalable, and clinically adaptable intelligent nanomedicines.

Graphical abstract

Highlights

• •AI is revolutionizing theranostics by accelerating protein design, optimizing nanocarriers, and enabling real-time, adaptive treatment strategies for precision medicine.
• •The convergence of AI and sustainable nanomedicine promises highly effective, biodegradable, and biocompatible therapeutics, minimizing environmental and health risks.
• •Machine and deep learning techniques are pivotal in predicting protein structures and analyzing molecular interactions, leading to safer and more targeted therapies.
• •Intelligent nanocarriers merge diagnosis and therapy, offering capabilities like real-time monitoring and adaptive drug release, moving beyond conventional static systems.
• •This multidisciplinary approach paves the way for personalized medicine, tailoring treatments to individual molecular profiles and enabling future closed-loop, feedback-guided therapies.

Abbreviations:

AI

artificial intelligence

ML:

Machine Learning

DL:

Deep Learning

RNA

Ribonucleic acid

RFDiffusion

RoseTTAFold Diffusion

RFAA

RosettaFold All-Atom

AFDB

AlphaFold

XRC

X-ray crystallography

NMR

nuclear magnetic resonance

cryo-EM

cryo-electron microscopy

VAEs

Variational Autoencoders

GANs

Generative Adversarial Networks

Beta-hCG

beta-human chronic gonadotropin

CM

computational modeling

AI-SPE

AI-Sustainable Protein Engineering

BioNTech

Biopharmaceutical New Technologies

GMP

good manufacturing practice

PLGA

poly (lactic-co-glycolic acid)

PEG

polyethylene glycol

MegaMolBART

Megascale Molecular Bidirectional AutoRegressive Transformer

NVIDIA

NVIDIA Deep Learning Accelerator

QSAR

Quantitative Structure-Activity Relationship

SVM

Support Vector Machine

EAs

Evolutionary Algorithms

TL:

Transfer Learning

PNP

Polymeric Nanoparticle

RL:

Reinforcement Learning

ADCs

antibody-drug conjugates

MRI

Magnetic Resonance Imaging

ProGen

Protein Generator

Protein MPNN

Protein Massage Passing Neural Network

RoseTTAFold

Rosetta-based Transform for Protein Folding

ESMFold

Evolutionary Scale Modeling Fold

GAN

Generative Adversarial Network

BRCA1

Breast Cancer Type 1 Susceptibility Gene

Auto Dock-GPU

Automated Docking using Graphics

AMPs

Antimicrobial Peptides

HER2

Human Epidermal Growth Factor Receptor2

PSA

Prostate-Specific Antigen

AFP

Alpha-Fetoprotein

CEA

Carcinoembryonic Antigen

mRNA

messenger ribonucleic acid

XAI

Explainable Artificial Intelligence

MIT

Massachusetts Institute of Technology

PK

pharmacokinetic

PD

pharmacodynamic

ProteinMPNN

Protein Message Passing Neural Network

DNN

Deep Neural Networks

IoT

Internet of Thing

BO

Bayesian Optimization

AuNP

Gold Nanoparticle

SLN

Solid Lipid Nanoparticle

GNN

Graph Neural Network

LNP

Lipid Nanoparticle

QDs

quantum dots

RMSE

Root Mean Squared Error

CNN

Convolutional Neural Network

SHAP

Shapley Additive explanations

RF

Random Forest

MOF

Metal-Organic Framework

QD

Quantum Dot

IONPs

iron oxide nanoparticles

MAE

Mean Absolute Error

MSE

Mean Squared Error

R²

Coefficient of Determination

NNE

Neural Network Ensemble

LIME

Local Interpretable Model-agnostic Explanations

Point	Implication/Novelty/Challenges
Revolutionizing Protein Design with AI	AI could accelerate selective and stable protein/peptide engineering, although predictive accuracy and clinical validation remain challenging.
Sustainable Nanomedicine: AI-Guided Greener Alternatives	Intelligent nanocarriers could enable adaptive release with safe biodegradation, but scalability and regulatory issues may limit translation.
Theranostics: Dual-Function Smart Systems	Integrated diagnostic therapeutic platforms could advance real-time personalized treatment, but reproducibility and long-term safety remain concerns.
Multidisciplinary Convergence: AI, Nanotechnology, and Medicine	AI-driven closed-loop theranostics could be a forward-looking model for adaptive, personalized, and sustainable precision medicine.

1. Introduction

The integration of artificial intelligence (AI) into molecular design has radically transformed various aspects of medicine, offering unprecedented opportunities for drug development, precision therapeutics, and personalized healthcare [1]. AI-driven methodologies, particularly machine learning (ML) and Deep Learning (DL), have produced a paradigm shift in how biomolecules such as proteins and peptides can be engineered to improve their specificity and therapeutic potential [2]. These tools allow rapid and accurate protein structure prediction, molecular docking, and structure-activity relationship (SAR) modeling, significantly reducing the time and cost associated with traditional drug discovery pipelines [3]. By analyzing vast amounts of biological and chemical data, AI can design novel proteins that are better suited for targeted therapy, reducing the time required for drug development, and also improving the safety and efficacy of the final products. The capacity of AI to process and analyze large-scale biological datasets, including genomics and proteomics data, has significantly advanced personalized medicine by enabling the development of patient-specific therapeutic strategies. In this context, AI-driven and computational approaches not only enhances the molecular design of therapeutics but also fundamentally advances the paradigm of precision medicine by enabling predictive and personalized treatment strategies. At the same time, sustainability in nanomedicine has emerged as a major research focus in recent years. Nanotechnology has significantly improved the performance of drug delivery systems by enhancing the bioavailability, stability, and targeted delivery of therapeutic agents [4,5]. However, concerns have been raised over the long-term environmental and biological impacts of nanomaterials, particularly those that are non-biodegradable or exhibit toxicity to various organisms. For example, some inorganic and polymeric nanocarriers are known to persist in the environment or accumulate in tissues, raising questions about their long-term safety and regulatory acceptance. Therefore, there is a growing request for sustainable nanomedicine that employs biodegradable, biocompatible, and environmentally friendly nanomaterials. The integration of green chemistry principles into the synthesis of nanocarriers ensures that the production, application, and disposal of nanoparticles minimize environmental impact and pose no significant risks to human health. For instance, biodegradable nanocarriers can be designed to be degraded into non-toxic byproducts, reducing the potential for long-term accumulation in the body and the environment [6].

AI-driven techniques can play a key role in optimizing the design of such nanomaterials by predicting their degradation rates, improving the selection of raw materials, and enhancing their efficacy in drug delivery while minimizing side effects [7,8]. By addressing these challenges, AI and sustainable nanomedicine are emerging as leading drivers of safer and more effective therapeutic delivery systems. Theranostics is another rapidly emerging topic in precision medicine, combining both diagnostic and therapeutic functions within a single platform [9]. This dual approach allows for real-time monitoring of disease progression and treatment response, while simultaneously delivering targeted therapeutics to the disease site. This approach could not only improve treatment outcomes but also minimize the adverse effects of therapy, as each treatment is customized for the individual patient. Furthermore, theranostic systems are promising for a wide variety of diseases, including neurological disorders, infectious diseases, and cardiovascular conditions, where personalized diagnostic and therapeutic approaches can dramatically enhance patient care [10,11]. AI plays a critical role in the development of advanced theranostic systems via integrating molecular imaging, biomarker detection, and real-time data analysis that provide more precise disease monitoring and allow therapeutic adjustments [12,13]. Based on these considerations, this review aims to explore the multidisciplinary convergence of AI-driven protein design and sustainable nanomedicine, highlighting their potential for theranostic applications in precision medicine. Despite numerous reviews on AI-driven protein engineering or nanomedicine, a unified and integrative analysis connecting these two rapidly advancing fields remains absent. Indeed, we have checked the number of publications in Scopus using the keyword “AI-driven protein design and sustainable nanomedicine”; however, no publication was found. Therefore, other keywords were chosen and searched, including “AI and Protein design”, “AI and Theranostics”, “AI and Precision medicine”, “Sustainable nanomedicine and Theranostics”, and “Protein design and Precision medicine” with results shown in Fig. 1. These findings emphasize the importance of conducting an in-depth overview of this subject to advance the field.

Fig. 1.

Scopus data about the number of publications using different keywords (“AI-driven protein design and sustainable nanomedicine”; however, no publication was found. Therefore, other keywords were chosen and searched, including: A) Artificial intelligence and Precision medicine, B) Artificial intelligence and Protein design, C) Artificial intelligence and Theranostics, D) Protein design and Precision medicine, and E) Sustainable nanomedicine and Theranostics) during last ten years.

The first objective of this study is to examine the role of AI in the design of proteins and how these innovations are driving advances in personalized medicine. We have focused on how AI can enable the design of proteins with greater specificity and therapeutic potential, offering innovative solutions for targeted therapy. Secondly, we have discussed the importance of sustainability in nanomedicine, including a detailed exploration of eco-friendly nanomaterials, the challenges in ensuring their safety and biodegradability, and how AI-assisted modeling can optimize these processes. Lastly, we have discussed theranostics as an emerging field, exploring how AI can enhance diagnostic accuracy and enable the development of targeted, responsive treatments. This includes the integration of diagnostic imaging, therapeutic delivery, and real-time monitoring into one cohesive system. By linking these domains together, this review provides a novel and timely perspective that goes beyond a descriptive summary and highlights both opportunities and critical challenges that must be addressed for successful clinical translation.

2. Fundamental of artificial intelligence methodologies for protein design

AI has emerged as a transformative framework in protein design by enabling the systematic learning of complex sequence–structure–function relationships that are difficult to resolve using conventional computational or experimental approaches alone. In contrast to accelerated computing tools focused on computational efficiency, AI-driven methodologies introduce data-centric learning frameworks that uncover hierarchical structures in biological data and enable predictive and generative modeling [14]. For instance, classical ML methods, as the AI-driven protein design strategies, are typically relied on engineered features derived from amino acid sequences, physicochemical descriptors, evolutionary conservation metrics, or structural properties. Supervised learning algorithms such as support vector machines, random forests (RF), and gradient improving have been widely applied to predict stability, solubility, binding affinity, and enzymatic activity. Although these models may exhibit limited scalability for highly complex of high-dimensional biological datasets, they remain particularly valuable for interpretable structure property relationship analysis, especially in small to medium-sized datasets relevant to bioactive material-protein interactions [15].

On the other hand, convolutional neural networks (CNNs) are well suited for protein-related tasks due to their ability to learn spatially localized patterns from structural inputs. In protein modeling, CNNs are commonly trained on voxelized three-dimensional (3D) structures, contact maps, or inter-residue distant matrices. By hierarchically capturing both local and global structural features, CNN-based models have demonstrated strong performance in identifying folding patterns, residue–residue interactions, and binding interfaces. These capabilities provide a methodological foundation for structure-aware protein analysis, independent of specific downstream design objectives [16].

Protein sequences exhibit intrinsic sequential dependencies that can be effectively modeled using recurrent neural networks (RNNs). These architectures are designed to capture long-range interactions along amino acid chains, enabling the prediction of sequence-dependent properties such as folding propensity, functional motifs, and mutational tolerance. Sequence-based neural networks provide a general-purpose framework for learning protein grammar and sequence logic, forming a core methodological component of AI-driven protein engineering [17].

Graph neural networks (GNNs) offer a powerful and flexible representation for proteins by modeling them as graphs, where nodes correspond to amino acid residues and edges encode spatial proximity or physicochemical interactions. This representation naturally reflects the rational and topological nature of protein structures. GNN-based approaches have exhibited strong predictive performance across protein stability assessment, interaction modeling, and functional annotation. The capacity of GNNs to integrate geometric, structural, and chemical information positions them as a foundational methodology for protein modeling in complex biological and bioactive material environments [18].

Generative AI models, such as variational autoencoders (VAEs) and generative adversarial networks (GANs), introduce probabilistic frameworks for learning latent representations of sequences or structures. Through sampling of learned latent spaces, generative models facilitate the generation of novel protein candidates that satisfy underlying biological constraints. From a methodological perspective, these models establish the theoretical basis for exploring protein design spaces beyond naturally occurring sequences, supporting data-driven hypothesis generation in protein engineering [19].

Despite their strong predictive performance, AI models in protein design often suffer limited interpretability. To address this limitation, techniques such as feature attribution analysis, attention mapping, and sensitivity analysis are increasingly employed to enhance model transparency. Improving interpretability is essential for assessing model reliability, reducing bias, and enabling rational decision-making, particularly in biomedical and bioactive material contexts where safety and reproducibility are critical [20]. Building upon the fundamental AI methodologies outlined in section 2, the following section focuses on their systematic application within protein design workflows, emphasizing practical integration rather than underlying algorithmic principles.

3. Artificial intelligence-driven protein design

AI has emerged as a powerful tool in protein science, revolutionizing structure prediction, protein engineering, and molecular design [21]. AI-driven protein structure prediction enables the accurate determination of 3D protein conformations directly from amino acid sequences, significantly improving our understanding of protein folding, molecular interactions, and mechanistic functions [22]. DL models can be trained on vast structural databases providing unique accuracy, facilitating drug discovery and advancing biomolecular research [23]. Beyond structure prediction, AI-driven approaches enable rational protein engineering by optimizing the stability, specificity, and biological activity. ML algorithms can analyze large datasets to guide rational design and directed evolution, accelerating the development of novel enzymes, therapeutic proteins, and biomaterials [24]. Furthermore, AI-driven protein design enables the de novo creation of newly designed synthetic proteins with tailored properties [25]. Generative models and reinforcement learning (RL) approaches can facilitate the design of functional proteins with desired structural properties, paving the way for innovative therapeutics, biosensors, and nanomedicine applications. These advances highlight the critical role of AI in bridging molecular-level insights to medical innovations, promoting the sustainable and efficient solutions for precision theranostics (Fig. 2).

Fig. 2.

AI for protein structure prediction, protein engineering, and protein design. (A) Different types of AI models could be used for the prediction of protein structure when protein sequences are given them; (B) AI could also be used for predicting the effect of mutations on a protein that lead to produce engineered proteins; (C) AI could be used in transformative impact on protein design, changing it from a slow, trial-and-error process into a predictive, programmable, and highly efficient discipline. AI enables the generation of entirely novel proteins that do not exist in nature as well as designing proteins which are increasingly used in biosensing and theranostic platforms. Proteins structures in panels A and B were visualized using ChimeraX (version 1.8). The 3D point graphic representation in panel C (Protein Design) was generated in R.

Table 1 summaries the principal AI methodologies in contemporary protein engineering and nanocarrier design, presenting their underlying computational principles, representative tools, and domain-specific applications. It differentiates between transformer-based architectures, GNNs, diffusion models, generative models, and reinforcement learning, and specifies how each methodology contributes to protein structure prediction, sequence optimization, interaction modeling, and the development of smart and sustainable nanocarriers.

Table 1.

Overview of AI-driven protein design and smart nanocarrier development for bioactive materials and theranostics.

AI Methodology	Mechanistic Principles	Representative Tools/Models	Applications in Protein Design	Applications in Smart & Sustainable Nanocarriers	Ref.
Supervised Machine Learning (ML)	Pattern recognition form labeled datasets; regression/classification models; feature engineering	ProFET-based classifiers; sequence function predictors; ML-driven ADMET models	Functional annotation; evolutionary conservation analysis; prediction of protein stability and solubility	Predicting nanocarrier biocompatibility, toxicity, and payload release kinetics; optimizing polymer/lipid composition	[26]
Deep Learning (DL)	Multi-layer neural networks; convolutional and fully connected architectures; nonlinear feature extraction	Deep Sequence; DL-based protein stability predictors	Predicting mutational effects; enhancing protein thermostability and enzyme activity	Predicting nanoparticle-cell interactions; optimizing nano-bio interface properties	[27]
Transformer Architectures	Self-attention mechanisms capturing long-range dependencies; large-scale sequence embeddings; evolutionary information integration	AlphaFold2, RoseTTAFold. ESMFold, ProtT5	Protein structure prediction; predicting folding intermediates; sequence design through contextual embeddings	Predicting ligand-nanocarrier interactions; mapping molecular surfaces; target-specific carrier optimization	[28]
Graph Neural Networks (GNNs)	Node-edge relational learning; geometric representation of protein or nanoparticle structures; message passing	Protein Message Passing Neural Network (ProteinMPNN), GraphTrans, GCN-based nanoparticle property predictors	Generative protein sequence design; protein-protein interaction modeling; topology-aware redesign	Modeling ligand-nanocarrier interactions; mapping molecular surfaces; target-specific carrier optimization	[29,30]
Diffusion Models	Probabilistic denoising; generative modeling of structural manifolds; sampling of conformational states	DiffDock, Diffusion-based peptide/nanoparticle generators	Generative protein-ligand docking; conformational sampling; novel peptide design	Predicting drug-carrier docking; generative design of polymer/lipid architectures, cargo loading optimization	[31]
Generative Models (VAEs, GANs, Autoregressive Models)	Latent space learning; probabilities sequence generation; iterative refinement	ProGen, Protein GAN, VAE-based NP generative models	De novo enzyme/protein design; functional motif engineering	Designing new nanosystems with sustainable materials; generating optimized drug carriers with tunable degradation profiles	[32,33]
Reinforcement Learning (RL)	Reward-driven optimization; sequential decision-making; multi-objective search	Alpha Design, RL-based peptide design frameworks	Multi-parameter protein optimization (activity, stability, binding)	Adaptive optimization of nanocarrier release behavior; maximizing therapeutic index under multi-constraint design	[34]

3.1. AI-driven protein modeling and structure prediction

The integration of AI into protein folding, docking, and binding affinity prediction has greatly enhanced the precision of these prediction results [35]. Experimental structure determination methods, such as X-ray crystallography (XRC), nuclear magnetic resonance (NMR) spectroscopy, and cryo-electron microscopy (cryo-EM), are both time-consuming and expensive, whereas AI-driven approaches are faster and more cost-effective alternatives [36]. In recent years, AI has revolutionized the field of protein design by significantly improving the accuracy and efficiently of protein structure prediction [37,38]. AI models now allow experts to predict protein-protein interactions and molecular binding affinities with high accuracy [39], which is crucial for drug development and the design of nanomedicines with improved therapeutic efficacy. Tools such as AlphaFold and Rosetta Fold have emerged as powerful AI-driven platforms, enabling researchers to predict protein structures with unprecedented accuracy [[40], [41], [42], [43]]. These advancements not only accelerate the process of understanding protein folding, but also open new avenues in drug design and molecular engineering. AlphaFold, developed by Google DeepMind, demonstrates remarkable success in predicting the 3D structures of proteins based on their amino acid sequences. This AI tool utilizes DL algorithms and large-scale protein datasets to model protein structure, allowing for a deeper understanding of protein function and interactions. By predicting previously unsolved protein structures, it transforms biochemistry and drug discovery and enables targeted protein design [40,[44], [45], [46]].

Rosetta Suite is another widely-used AI software package that provides a set of methods, including RoseTTAFold, RoseTTAFold all atom, RFDiffusion and other tools, for protein structure prediction, protein-protein interaction modeling, and protein design [[47], [48], [49]]. Rosetta's combination of computational techniques, such as DL, molecular dynamics simulation and energy function refinement, allows for the design of novel proteins with desired functions and stability [50,51]. It has become instrumental in the design of therapeutic proteins, catalysts enzyme, and biomaterials, enabling advances in targeted therapeutics and sustainable nanomedicine. Compared to AlphaFold, which excels in highly accurate single-chain protein structure prediction, RoseTTAFold offers greater flexibility through its modular framework and integration with the broader Rosetta suite. While AlphaFold achieves unique accuracy in individual protein structure prediction, its capacity to model large macromolecular complexes and conformational dynamics remains limited. In contrast, RoseTTAFold, while generally less accurate for single-chain predictions, is more adaptable for modeling protein-protein interactions, protein design, and custom applications. Both methods have some unresolved challenges, particularly in modeling intrinsically disordered regions, conformational heterogeneity, and predicting functional dynamics [52,53].

3.2. AI-driven engineering of functional proteins for theranostic applications

The engineering of functional proteins for theranostic applications involves three key strategies to improve targeting precision, catalytic efficiency, and diagnostic performance [54]. The first strategy enhances binding affinity to enable selective targeting of specific cells, tissues, or receptors [55]. This targeted design enables site-specific drug delivery, enhancing efficacy and minimizing effects on healthy tissues. The second strategy enhances specificity by restricting protein interactions to intended targets that minimize off-target effects and ensure precise therapeutic action [56]. Enhanced specificity also improves imaging accuracy in diagnostic procedures by enabling proteins to selectively bind specific biomolecules or cellular structures. This precision enhances detection sensitivity, reduces false-positive and false-negative results, and ultimately improves diagnostic reliability. The third strategy involves the rational engineering of enzymes to enhance catalytic efficiency, stability, and selectivity, thereby enabling more effective functional performance. Enzymes play a pivotal role in both therapeutic and diagnostic applications, and their activity can be optimized through rational design techniques [57]. This optimization is particularly critical for enzyme-based therapies, where catalytic activity directly determines treatment efficacy, as well as for diagnostic applications, where precise enzyme function enables accurate biomolecule detection. These three strategies could enhance binding affinity, specificity, and catalytic efficiency, facilitating the generation of functional proteins that are pivotal for theranostic advancement (Fig. 3). Engineered proteins can thus provide more precise targeting, enhanced therapeutic efficacy, and reliable diagnosis significantly improving both therapeutic interventions and diagnostic procedures.

Fig. 3.

Engineered functional proteins for theranostics involving three key strategies. (1) Enhanced binding affinity. Proteins are engineered to specifically target and bind to cells, tissues, or receptors, ensuring the precise delivery of therapeutic agents to the intended site. (2) Enhanced specificity. This improves the efficacy of targeted therapy and ensures more precise imaging in diagnostic procedures by ensuring that proteins interact exclusively with their intended targets, reducing off-target effects. (3) Rational design of enzymes. Enzymes can be designed for optimized catalytic activity, enhancing their efficiency, stability, and selectivity, thus improving their effectiveness in both therapeutic and diagnostic applications. Produced by Blender 3.5.

Recent progress in AI has further refined these protein-engineering strategies, enabling the design of proteins with customized properties for specific theranostic purposes. AI algorithms can analyze vast datasets of protein sequences and structures to identify critical motifs that enhance stability, binding affinity, and catalytic performance. Moreover, AI-based simulations have improved the prediction of protein-protein and protein-ligand interactions, accelerating the development of theranostic agents that combine therapeutic and diagnostic functionalities within a single molecular entity [40]. This integration holds major implications for drug delivery and precision medicine, paving the way toward personalized and highly efficient treatment modalities [58,59].

Real-world applications of AI-engineered proteins have demonstrated both potential and drawbacks. For example, AI-designed antibodies predicted to have high affinity have often failed to maintain binding stability in human serum, revealing limitations in training datasets that rely heavily on non-human protein structures. Similarly, enzyme variants optimized by DL models sometimes exhibit reduced catalytic efficiency under physiological pH or temperature, underscoring the gap between computational predictions and experimental performance [[60], [61], [62], [63], [64], [65], [66]]. Moreover, while AI-driven methods have shown measurable improvements in predicting binding specificity and catalytic efficiency, current applications often manage computational costs, accessibility issues, and the biological complexity of real systems. Dataset biases, such as the overrepresentation of model organisms, can introduce systemic blind spots, reducing reliability when predictions are translated to human or clinical contexts. Furthermore, computational predictions sometimes fail under non-ideal physiological or manufacturing conditions, revealing that AI models may not generalize well beyond their training parameters [67]. AI can guide the optimization of protein stability, specificity, and activity, thereby enhancing the precision of drug delivery, minimizing off-target effects, and improving the therapeutic index. However, preclinical testing often reveals off-target toxicity or stability failures that are not captured by AI models, highlighting the gap between computational design and biological reality [[68], [69], [70], [71]]. Besides, reproducibility challenges in experimental studies and unequal access to large-scale protein datasets underscore the current limitations of AI-driven approaches. AI-guided optimization of functional proteins also faces ethical and translational challenges, including data bias, reproducibility issues, and model interpretability. Failures in translation from computational design to biological performance highlight the need for robust experimental and ethical validation. By optimizing microbial or cell-based protein production, AI can reduce environmental impact and enhance therapeutic precision, paving the way for eco-efficient and patient and patient-specific nanomedicine [[72], [73], [74], [75], [76], [77]]. Such discrepancies highlight the urgent need for iterative validation frameworks, where AI outputs are continuously refined through experimental feedback. Addressing these limitations will be essential for translating AI-designed functional proteins from laboratory success to clinical utility.

Table 2 provides a comparative overview of AI applications in protein design with direct relevance to theranostics. The Table systematically categorizes AI strategies into predictive modeling (e.g., AlphhaFold, RosettaFold), functional engineering (e.g., AI-guided mutagenesis and drug conjugation), and de novo design approaches (e.g., generative models for peptide and enzyme design). It mentions the core AI algorithm, the biological target or function, and its translational utility in diagnostics, therapeutics, or integrated theranostic platforms. By mapping AI tools to their respective functions and end-user applications, this table highlights both the diversity and specificity of AI-driven protein design, emphasizing the shift toward personalized, multifunctional, and sustainable therapeutic strategies.

Table 2.

Comparative overview of AI applications in protein design for theranostics.

Application Area	AI Technique/Platform	Scientific Function	Theranostic Relevance	Examples of Recent Advances and Application	Ref.
Protein Structure Prediction	Alpha Fold, RoseTTAFold	Predicts accurate 3D structures of proteins from primary sequences using DL	Enables identification of binding pockets, epitope mapping, and the structural basis for function in diagnostics and targeted therapy	Alpha Fold can predict structures of cancer-associated proteins (e.g., BRCA1 variants) to aid rational drug design	[78]
Molecular Docking & Binding Affinity Estimation	Deep Dock, GNINA (CNN-enhanced), Auto Dock-GPU	Predicts drug-target interactions and binding strength using high-throughput virtual screening	Accelerates drug candidate identification for targeted nanomedicine or enzyme therapeutics	DL-enhanced docking identified inhibitors for SARS-CoV-2 Mpro within days of its structural resolution	[79,80]
Protein Engineering and Mutagenesis	ProGen, ProteinMPNN, Transformer-based models	AI-directed mutagenesis for increased protein stability, activity, or binding affinity	Optimizes therapeutic enzymes, antibodies, and cytokines for theranostic applications	Engineered enzymes with AI Enhanced catalytic efficiency in tumor microenvironment-specific pathways	[81,82]
De Novo Protein and Peptide Design	ESMFold, GAN-based Generative Models	Generates novel protein scaffolds, therapeutic peptides, or synthetic receptors	Facilitates creation of entirely new therapeutic agents with diagnostic capabilities	AI designed de novo peptide inhibitors against amyloid aggregates with imaging capacity	[83,84]
Multifunctional Therapeutic Conjugates	Reinforcement Learning (RL)	Designs proteins with combined therapeutic and imaging (theranostic) functions	Enables simultaneous drug delivery and real-time disease monitoring	AI-guided antibody-drug conjugates (ADCs) with fluorescent or MRI-contrast agents	[85,86]

Abbreviation: AI: Artificial Intelligence; RL: Reinforcement Learning; ADCs: antibody-drug conjugates; MRI: Magnetic Resonance Imaging; ProGen: Protein Generator; Protein MPNN: Protein Massage Passing Neural Network; RoseTTAFold: Rosetta-based Transform for Protein Folding; ESMFold: Evolutionary Scale Modeling Fold; GAN: Generative Adversarial Network; BRCA1: Breast Cancer Type 1 Susceptibility Gene; Auto Dock-GPU: Automated Docking using Graphics Processing Unit.

3.3. AI in de novo protein design

De novo protein design is a transformative method to develop novel proteins without reliance on existing templates, to address specific therapeutic and diagnostic challenges [87]. AI has revolutionized this field by utilizing advanced generative models, such as VAEs, GANs, and diffusion models, to explore the vast and complex space of possible protein sequences [[88], [89], [90]].

AI-driven generative models enable the de novo generation of protein sequences with customized functional properties [[91], [92], [93], [94], [95], [96]]. These models predict sequences that not only fold into stable 3D structures, but also can carry out the desired biochemical functions. The designed protein sequences can be computationally validated by predicting their structure via structure prediction tools such as AlphaFold or RoseTTAFold. Coupling these predictive models with DL techniques facilitate the design of proteins for an unprecedented array of applications, including enzyme engineering, novel ligand binding, and advanced catalytic activity [97].

AI-designed proteins are increasingly being employed to target disease-specific biomarkers. By generating proteins with high specificity and affinity for unique disease markers, AI has the potential to develop advanced therapeutic and diagnostic agents for widespread conditions such as cancer, neurodegenerative disease, and infectious disease. For instance, protein-based biosensors have been engineered to detect low-abundance biomarkers in complex biological samples, providing an opportunity for early disease detection. Moreover, AI-designed therapeutic proteins, such as novel cytokines and monoclonal antibodies, have shown promise in personalized medicine, offering a targeted treatment with fewer off-target effects [[98], [99], [100]]. Therefore, the integration of AI into the de novo protein design methodology bridges the gap between theoretical molecular insights and practical medical applications, paving the way for next-generation protein therapeutics and diagnostics. As generative models continue to advance, their potential to accelerate drug discovery and address global health challenges also grows exponentially, representing a new frontier in theranostics.

4. Case studies: from AI diagnostics to integrated theranostics

The application of AI-driven protein engineering and sustainable nanomedicine has already led to meaningful process in theranostics. Rather than re-explaining the mechanistic aspects of AI algorithms already discussed in Section 3, this section covers real-world examples and translational progress in AI-enabled diagnostics, oncology and infectious disease management, as well as multifunctional theranostic proteins and nanocarriers. Importantly, vaccine candidates are also included, as they represent a major class of protein-based therapeutics in which AI-driven protein design enables the rational optimization of antigen structure, stability, and immunogenicity, aligning directly with the central theme of this review.

Table 3 provides a comparative overview of the major case studies discussed in this section, highlighting the distinct methodological approaches, clinical translation stages, and practical challenges associated with AI-driven protein engineering and sustainable nanomedicine. Rather than duplicating mechanistic details already addressed in Section 2, the table emphasizes how AI can contribute to different domains including cancer diagnosis, infectious disease management, multifunctional theranostic proteins, and therapeutic protein development.

Table 3.

Comparative summary of AI-Driven protein engineering and nanomedicine case studies.

Domain/Case Study	Methodologies Applied	Clinical Stage	Key Challenges	Ref.
Cancer Diagnostics (HER2, PSA, AFP, CEA Biosensors)	AI-enhanced biosensor design, continuous monitoring algorithms	Early clinical validation, translational studies	Integration into routine diagnostics; minimizing false positives/negatives; regulatory approval	[[101], [102], [103]]
Infectious Disease Management (Antimicrobial Peptides (AMPs) & Vaccines)	De novo protein/peptide design; AI-guided antigen & epitope prediction	Preclinical to early-stage vaccine trials	Balancing efficacy and toxicity; ensuring broad-spectrum activity; rapid adaptation top emerging pathogens	[[104], [105], [106]]
Multifunctional Proteins for Theranostics	Monoclonal antibody conjugates, cytokine-fusion peroteins, enzyme-activated systems	Preclinical models, limited clinical feasibility studies	Stability in vivo; complexity of duak diagnostic/therapeutic functions; cost-effective scalability	[[107], [108], [109]]
De Novo Designed AMPs	Generative AI for peptide libraries; toxicity reduction modeling	Preclinical efficacy studies	Resistance development; delivery mechanisms; cost of large-scale synthesis	[110,111]
AI-Generative Vaccine Candidates	Computational epitope mapping, structural modeling, immune-response prediction	Early preclinical to Phase I clinical testing	Transitional gap from in silico to in vivo; immune variability across populations	[112]
AI-Driven Multi-functional Therapeutic Proteins	Hybrid molecules combining imaging and therapy; AI-based optimization of binding/activation	Preclinical proof-of-concept	Off-target effects; complex regulatory pathways for combination diagnostics/therapeutics	[108,109]
AI-Assisted Therapeutic Proteins for Cancer	Rational design of cytokines, bispecific, checkpoint modulators	Advanced clinical evaluation in oncology trials	Safety-efficacy balance; patient stratification; real-world deployment logistics	[113,114]
Cancer Diagnostics (CA 19–9)	CLIA, IHC, AI-based diagnostic systems	Application in Pancreatic cancer detection	AI models integrating CA 19-9 with imaging modalities for improved accuracy	[115]
Cancer Diagnostics (CA 15–3)	CLIA, IHC, AI-enhanced diagnostic platforms	Used in Breast Cancer monitoring	AI tools have been developed to enhance detection sensitivity	[116]
Cancer Diagnostics (Beta-hCG)	CLIA, IHC, AI-enhanced diagnostic platforms	Applied in testicular cancer	AI tools have been developed to enhance detection sensitivity	[115]

Abbreviations: AI: Artificial Intelligence; AMPs: Antimicrobial Peptides; HER2: Human Epidermal Growth Factor Receptor2; PSA: Prostate-Specific Antigen; AFP: Alpha-Fetoprotein; CEA: Carcinoembryonic Antigen; Beta-hCG: beta-human chronic gonadotropin.

4.1. Smart biosensors and imaging agents

AI-assisted biosensor and imaging technologies exemplify how ML and computational modeling (CM) can elevate molecular diagnostics from conventional detection toward predictive, adaptive systems. In biosensor design, AI algorithms are increasingly used to optimize recognition elements such as antibodies aptamers, and engineered proteins for enhanced binding affinity, stability, and specificity. DL models can analyze large biomolecular datasets to identify structural motifs most suitable for target recognition and signal transduction. For example, HER2-specific biosensors for breast cancer and PSA-based biosensors for prostate cancer achieved ultra-sensitive detection limits (down to femtomolar concentrations), improving patient stratification and minimizing false positives. These biosensors integrated AI-optimized surface chemistry and nanomaterial interfaces, allowing real-time quantification of biomarkers even in complex biological fluids [117,118]. Beyond oncology-driven demonstrations, AI-assisted biosensors have been widely developed for general disease diagnostics and biomarker monitoring. Across electrochemical, optical, and nanosensor-based platforms, AI is primarily applied to enhance signal processing, feature extraction, and classification accuracy in complex biological environments. ML algorithms enable automated noise suppression, sensor drift correction, and adaptive calibration, significantly improving the robustness and reproducibility of biosensor outputs for early diagnosis and real-time health monitoring. These AI-driven strategies are particularly important for point-of-care diagnostics, where rapid, user-independent decision-making is required, and they highlight the transition of biosensors from static analytical tools toward intelligent, self-adaptive diagnostic systems [[119], [120], [121], [122]]. For example, an AI-assisted design strategy was employed in a recent research, to produce surface plasmon resonance (SPR) fiber optic biosensor for the sensitive detection of the immunoglobulin G (IgG) biomarker. In here, AI was used to optimize key sensor design parameters, enhancing light-matter interaction and improving signal responsiveness to biomolecular binding events. The use of an optical fiber platform enabled miniaturization, remote sensing capability, and compatibility with real-time analysis, while integrating AI-driven modeling with SPR fiber optics led to improved sensitivity and detection performance of biosensor compared to conventionally designed systems [123].

Similarly, AI-drive pattern recognition is revolutionizing imaging design. Computational models trained on large imaging datasets can differentiate subtle phenotypic differences invisible to human interpretation, enabling earlier detection of malignancies [124]. In parallel, AI-guided molecular docking and quantum-based simulations assist in tailoring fluorophores, contrast agents, and radioligands with improved tissue penetration, biodistribution, and signal-to-noise ratios [125]. In addition to clinical biomarker detection, AI-integrated biosensors have shown strong potential for pathogen identification and public health-oriented diagnostics. Recent studies emphasize the use of supervised and unsupervised learning algorithms to assist biosensors in detecting foodborne pathogenic bacteria by recognizing subtle electrochemical and optical signal patterns within complex sample matrices. AI-enhanced biosensors demonstrate faster classification, reduced false positives, and improved sensitivity compared to conventional approaches, supporting real-time and on-site diagnostic applications [126,127]. For instance, the AI-assistance was used to design a smartphone-based colorimetric biosensing platform for the rapid, sensitive, and visual detection of pathogenic bacteria. The biosensor exhibited colorimetric signal changes generated upon bacterial recognition, which were captured using a smartphone camera and analyzed through artificial intelligence algorithms. The integration of AI-based image processing and pattern recognition to this system led to overcome the common limitations of naked-eye colorimetric assays, such as subjective interpretation, variable lighting conditions, and low sensitivity. The AI model enabled quantitative analysis of color intensity and distribution, allowing accurate bacterial detection even at low concentrations. Importantly, the smartphone-based architecture supported portability, low cost, and real-time analysis, making the platform well suited for point-of-care and on-site diagnostics [128].

Recent advances also highlight multiplexed and wearable biosensing systems supported by AI analytics. For instance, continuous monitoring platforms integrating biomarkers such as AFP for hepatocellular carcinoma and CEA for colorectal cancer employ neural networks for dynamic baseline correction and drift compensation. These systems moved beyond one-time measurements toward longitudinal, patient-specific profiling [129]. Importantly, these developments underscore that AI does not simply automate existing diagnostic methodologies; rather, it transforms diagnostics into adaptive, learning systems capable of self-optimization. By integrating multi-omics data, imaging outputs, and patient-specific variables, AI-guided diagnostics enhance both sensitivity and clinical decision-making accuracy. This synergy between computational intelligence and molecular engineering lays the foundation for the integrated theranostic frameworks discussed in later subsections.

4.2. AI and theranostics

4.2.1. AI application in engineering proteins, antibodies, and peptides

AMPs are short, naturally occurring polypeptides that exhibit potent activity against pathogens responsible for bacterial, viral, fungal, and parasitic infections [130]. Traditional AMPs are often derived from natural sources, but their therapeutic application has been hindered by challenges such as instability, cytotoxicity, and susceptibility to enzymatic degradation [131]. The AI-driven de novo design of AMPs may provide a transformative approach to overcome these limitations by enabling the rational generation of synthetic peptides with improved therapeutic properties. Through ML and DL modeling, large peptide libraries can now be screened in silico to predict physicochemical properties such as charge distribution, hydrophobicity, folding behavior, and membrane affinity. These computational approaches enable the identification of novel AMPs capable of combating drug-resistant pathogens, including MRSA, Pseudomonas aeruginosa, and Klebsiella pneumoniae. Importantly, AI not only accelerates the discovery of effective peptide candidates but also assists in optimizing their pharmacokinetic (PK) profiles and minimizing off-target interactions, which are critical for clinical translation [[132], [133], [134]].

Another rapidly advancing domain involves AI-assisted antibody and protein engineering. DL algorithms can predict antigen-antibody interaction sites and propose targeted mutations to improve binding affinity and specificity. For instance, transformer-based sequence models trained on large-scale antibody antigen datasets have demonstrated high accuracy in predicting complementary-determining region (CDR) conformations. This capability enables the rational design of therapeutic antibodies with superior target recognition, reduced immunogenicity, and enhanced serum stability. Additionally, AI-guided optimization supports the engineering of bispecific antibodies and antibody drug conjugates (ADCs), allowing simultaneous engagement of multiple antigens or co-delivery of cytotoxic agents to specific cellular targets an approach increasingly explored for cancer and viral infections [[135], [136], [137]].

AI also play transformative role in vaccine design and immunotherapeutics. Advanced models combining structural bioinformatics and neural network prediction enable accurate identification of B-cell and T-cell epitopes, facilitating the rational design of subunit and multi-epitope vaccines. This was particularly evident during the COVID-19 pandemic, where AI-driven antigen and epitope prediction significantly accelerated design workflows and improved antigenicity assessment. Furthermore, computational platforms integrating AI-based molecular docking, codon optimization, and immune response simulation have enhanced the precision of antigen expression and improved immune potency [138,139]. Recent developments have also expanded toward AI-guided peptide-protein conjugates and fusion proteins for integrated theranostic applications. Examples include enzyme-based therapeutic proteins that can be activated selectively within infected or tumor tissues, and cytokine fusion proteins that combine immune simulation with diagnostic imaging capabilities. Such multifunctional systems illustrate the convergence of molecular design and intelligent computation, laying the groundbreaking for responsive, feedback-driven theranostic interventions [140].

4.2.2. AI and multifunctional and smart protein platforms

Multi-functional proteins are engineered molecules that combine diagnostic and therapeutic functions within a single entity. AI-driven design plays a critical role in creating multifunctional and engineered protein platforms that perform dual roles of theranostics with high specificity and efficiency [141].

The imaging part of dual-function proteins typically involves incorporating an imaging agent, such as fluorescent dyes, radioactive isotopes, or magnetic nanoparticles, into the protein structure [142]. This enables non-invasive visualization of biological processes and disease progression using imaging techniques like magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), or optical imaging [143,144]. These proteins can be targeted to specific tissues or tumors, allowing clinicians to detect the location, size, and stage of a disease such as cancer with high precision. The ability to monitor disease progression and treatment response in real time is a crucial aspect of these proteins in the diagnostic domain [145]. For example, monoclonal antibodies conjugated with imaging agents allow simultaneous tumor targeting and real-time visualization, providing clinicians with precise special and temporal information on therapeutic delivery. Similarly, enzyme-based therapeutics can be programmed to activate exclusively at pathological sites, such as tumor microenvironments or infected tissues, minimizing off-target effects and enhancing therapeutic selectivity. Cytokine fusion proteins represent another sophisticated strategy, coupling immune modulation with diagnostic imaging, thereby providing simultaneous insights into immune activation and therapeutic efficacy [107,[146], [147], [148]]. Recent advances demonstrate how AI-driven protein design further enhances these strategies. RFdiffusion-generated de novo functional folds serve as highly specific targeting modules for tumor-associated receptors, while ProteinMPNN-designed sequences, validated for stability and structural fidelity, function as ligand modules or responsive therapeutic payloads within polymeric or lipid-based nanocarriers. These AI-engineered protein platforms improve the precision, adaptability, and therapeutic effectiveness of next-generation theranostic nanoformulations, thereby enabling more personalized and efficient biomedical interventions [149].

AI facilitates the rational design of these multifunctional systems at multiple levels. ML models predict optimal linker chemistry for drug or imaging agent conjugation, estimate biodistribution and pharmaceutics, and simulate protein folding and stability under physiological conditions. Generative design algorithms can propose novel fusion protein architectures or nanoparticle coatings that optimize both therapeutic payload delivery and imaging contrast. By integrating patient-specific omics data and imaging information, AI also enables adaptive personalization, tailoring nanocarriers or protein constructs to the molecular profile of individual patients [150]. Beyond molecular design, AI-driven optimization enhances dynamic monitoring and feedback control. Multifunctional nanocarriers can be combined with biosensors to track real-time biomarker levels, drug release, and treatment response. AI models then process this data to optimize dosing schedules or trigger responsive therapeutic activation, effectively creating a self-regulating theranostic system. Such feedback-driven platforms hold promise for complex diseases such as cancer, autoimmune disorders, and infectious diseases, where treatment efficacy is highly variable and context-dependent [151]. These examples illustrate how multifunctional proteins and nanocarriers, when integrated with AI-guided design and predictive modeling, bridge the gap between diagnostics and therapy. By precise, real-time and adaptive interventions, AI accelerates the refinement of these systems for clinical translation. Ultimately, this convergence of intelligent design, molecular engineering, and responsive therapeutics represents a new paradigm in personalized theranostics [[152], [153], [154]].

4.2.3. Therapeutic proteins and disease treatment

AI-based methods, including DL, RL, and GANs, could facilitate the identification of novel AMPs by predicting sequence-function relationships and optimizing peptide structures for improved efficacy and reduced toxicity [155]. Large-scale screening of peptide libraries using AI models has accelerated the discovery of potent candidates that exhibited broad-spectrum activity against drug-resistant bacteria such as methicillin-resistant Staphylococcus aureus (MRSA) and carbapenem-resistant Enterobacteriaceae (CRE) [156]. Additionally, AI-assisted chemical modifications could enhance AMP stability by incorporating non-natural amino acids, cyclic structures, and peptide-mimetic scaffolds, thus extending their half-life and bioavailability. Another critical advantage of AI-designed AMPs lies in their potential for synergistic interactions with conventional antibiotics, reducing the likelihood of resistance development [157]. Predictive AI models can analyze bacterial resistance mechanisms and suggest AMP-antibiotic combinations that could enhance therapeutic outcomes while minimizing adverse effects [158]. Furthermore, AI-guided protein engineering could enable the development of cell-penetrating AMPs for tackling intracellular infections, expanding their utility beyond conventional antimicrobial agents.

The emergence of novel infectious diseases, including zoonotic viruses and antibiotic-resistant pathogens, has underscored the need for rapid and accurate vaccine development. Indeed, vaccine development represents a prominent application of AI-driven protein design, as vaccine antigens are protein-based constructs whose structure, stability, and immunogenicity can be rationally optimized using AI-based modeling and prediction. AI-driven approaches have revolutionized vaccine research by streamlining antigen selection, immune response prediction, and vaccine formulation [159]. ML models can analyze vast genomic datasets to identify conserved antigenic regions across different strains of pathogen, ensuring broad-spectrum protection and long-lasting immunity. In this context, one of the key applications of AI in vaccine development is epitope-based vaccine design, wherein AI algorithms can predict B-cell and T-cell epitopes that will elicit strong immune responses [160]. This approach was used in the rapid development of vaccines for emerging infectious threats such as COVID-19, where AI-driven platforms facilitated the identification of spike protein epitopes for messenger ribonucleic acid (mRNA) vaccine formulation [161]. Additionally, AI-powered structural biology tools an assist in designing a stable antigen configuration that enhances immunogenicity and minimizes immune evasion [162]. AI-generated vaccine candidates have also used protein engineering techniques to improve vaccine stability, delivery, and adjuvant selection. AI-assisted protein folding prediction, such as that enabled by AlphaFold, provide precise modeling of vaccine antigens, optimizing their conformation for effective immune recognition [163]. Furthermore, AI-driven nanoparticle engineering could play a crucial role in developing nanovaccines to enhance antigen presentation and immune activation [164]. In addition to vaccine design, AI could contribute to predictive modeling for vaccine efficacy and safety. Computational simulation an assess potential immune responses, identify risk factors for adverse effects, and optimize dosage regimens. AI-driven surveillance systems can continuously monitor pathogen evolution, allowing for real-time updates to vaccines to counter emerging variants [165].

Several classes of therapeutic proteins, including monoclonal antibodies, enzymes, cytokines, and bispecific antibodies, are advancing toward clinical oncology. For instance, trastuzumab remains a benchmark therapy for HER2-positive breast cancer, while enzyme-prodrug systems and cytokine-fusion proteins are in active preclinical development. Although AI contributes to this progress by enabling rapid optimization of protein properties, the focus here is on the tangible translational progress of these agents, encompassing including clinical trials and patient monitoring systems [166].

AI could become indispensable in the development of therapeutic proteins for cancer treatment. AI-driven ML algorithms can be used to design, optimize, and predict the behavior of therapeutic proteins, enabling the creation of more effective and personalized treatments [167]. AI algorithms are able to process vast amounts of data related to protein structure, function, and interactions [168]. This allows for the design of proteins with improved properties, such as higher specificity for cancer cells and better stability in the body. AI can also predict how proteins will interact with other molecules, enabling the optimization of therapeutic proteins before they are synthesized and tested [169]. Monoclonal antibodies (mAbs) are among the most widely employed therapeutic proteins in oncology [170]. These engineered antibodies are designed to specifically bind to cancer cell antigens, triggering an immune response that targets and destroys cancer cells. A notable example of monoclonal antibody therapy is trastuzumab (Herceptin), which targets the HER2 receptor on certain types of breast cancer cells [171]. mAbs can also be conjugated with chemotherapeutic drugs or radioactive isotopes, forming immunoconjugates that deliver treatment directly to the cancer cells [172]. The dual-functionality of mAbs-acting both as diagnostic and therapeutic tools exemplify the principle of theranostics [173]. By attaching imaging agents to monoclonal antibodies, clinicians can track the antibody's distribution and monitor the tumor response to treatment, enhancing the overall treatment results.

Enzyme-based therapy involves another class of therapeutic proteins that target cancer cells with precision [174]. These enzymes are often designed to activate prodrugs specifically at the tumor site. One of the most well-known enzymes is cytosine deaminase, which activates the prodrug 5-fluorocytosine into the toxic chemotherapeutic drug 5-fluorouracil [175,176]. By engineering enzymes that specifically act on tumor-related substrates, these therapies can provide a targeted approach to chemotherapy, reducing systemic toxicity and improving treatment outcomes. The addition of a diagnostic agent, such as imaging dyes conjugated to the enzyme, allows clinicians to visualize the site of enzyme activity in real-time. This enables continuous monitoring of therapeutic progress, demonstrating the power of AI-driven design in optimizing enzyme-based therapy [177].

Cytokines are endogenous signaling proteins that regulate immune responses, making them invaluable in cancer immunotherapy [178]. Interleukins and interferons are the commonly used cytokines that can simulate the immune system to attack cancer cells. For example, interleukin-2 (IL-2) is used to simulate T-cells to recognize and destroy tumor cells [179]. Similarly, interferons enhance the anti-tumor activity of immune cells by increasing their ability to recognize and attack cancer cells. AI technologies can be used to refine the design of these fusion proteins, ensuring more effective immune activation and treatment monitoring.

Bispecific antibodies are engineered to bind to two different antigens at the same time [180] that allows bispecific antibodies to target both a tumor antigen and a T-cell receptor, bringing the immune system's T-cells into close proximity with cancer cells [181]. This interaction enhances the immune system's ability to recognize and eliminate cancer cells. Besides, it could be used in combination with diagnostic imaging techniques to track the distribution and effectiveness of the therapy in real-time [180]. By incorporating AI, researchers can design bispecific antibodies that are optimized for specific cancer types and predict how they will perform in the body, enhancing their therapeutic potential.

Gene therapy involves the introduction or modification of genetic material to treat cancer and other diseases [182]. One application involves RNA interference (RNAi), where molecules such as small interfering RNA (siRNA) or short hairpin RNA (shRNA) are used to silence genes that promote cancer cell growth [183]. For example, engineered proteins like nucleases or CRISPR/Cas9 systems can be used to specifically target and edit oncogenes, providing a direct and personalized approach for cancer treatment [184]. AI can play a crucial role in optimizing the delivery of these therapeutic proteins [167]. By analyzing patient-specific genetic data, AI can identify the best RNAi candidates and design agents for effective gene silencing. The integration of AI with RNA-based therapeutics could provide a more precise and tailored treatment option for cancer patients [185].

AI can facilitate the development of personalized therapy by analyzing the genetic and molecular profiles of both the patient and the cancer cells [186]. AI can predict which therapeutic proteins are most likely to be effective for any given patient by analyzing large datasets, thus improving treatment outcomes. This approach ensures that the therapy can be tailored to the individual's unique cancer characteristics, minimizing unnecessary side effects and improving efficacy. The integration of AI with diagnostic imaging and biomarker analysis could allow for real-time monitoring of the therapeutic activity of proteins [187]. AI can process data from various imaging modalities such as MRI, CT scans, and PET scans to assess how well the protein therapy is working and whether any adjustment is needed [188,189]. This dynamic approach could allow clinicians to track patient progress more effectively and make informed decisions about treatment modification.

AI can also be used in predictive modeling, simulating how therapeutic proteins will behave in the body [190]. These models consider factors such as protein stability, immune system interactions, and tumor microenvironment characteristics, enabling researchers to predict the success of a given therapy before clinical trials begin. This predictive capability can significantly reduce the time and cost associated with developing new therapies.

Accordingly, in infectious disease management, AI-driven tools could facilitate the design and optimization of AMPs, improving their structural properties and effectiveness against resistant pathogens [191]. Additionally, AI models can predict immunodominant epitopes, enabling the development of next-generation vaccines with enhanced stimulation of immune responses. In cancer diagnostics, AI-powered biosensors offer highly sensitive and rapid detection of biomarkers, improving early diagnosis and enabling precise monitoring of therapeutic responses [192]. By analyzing large datasets and identifying complex molecular patterns, AI enhances the accuracy and efficiency of diagnostic techniques, ensuring timely intervention and personalized treatment. Furthermore, AI-driven approaches can support the engineering of multifunctional proteins for theranostic applications, integrating both diagnostic and therapeutic functions into a single molecular entity. These proteins could serve as powerful tools for targeted disease management, allowing simultaneous imaging and therapy, minimizing side effects, and enhancing treatment efficacy. The integration of AI in these areas is expected to drive innovation in precision medicine, enabling more effective and sustainable healthcare solutions (Fig. 4).

Fig. 4.

AI could revolutionize infectious disease management, cancer diagnosis, and the development of multifunctional proteins for theranostic applications. In infectious disease management, AI could facilitate the rational design of AMPs by optimizing their structure and function to combat resistant pathogens. Additionally, AI-driven models could predict immunodominant epitopes, aiding in the generation of effective vaccine candidates with enhanced immune response potential. In cancer diagnosis, AI-powered biosensors could enable the rapid and precise detection of cancer biomarkers and therapeutic proteins, improving early diagnosis and personalized treatment. Additionally, AI can support the development of multifunctional proteins for theranostics, integrating diagnostic and therapeutic capabilities into a single molecular platform for targeted disease management. Produced by Blender 3.5.

AI-enabled strategies could bridge the gap between molecular-level insights and clinical application, advancing protein-based medicines for complex neurological diseases where conventional discovery approaches remain limited. For instance, in Alzheimer's disease, AI-based molecular modeling and protein structure prediction facilitate the rational engineering of antibodies, enzymes, and peptide-based biologics that target pathogenic amyloid-β aggregates, tau protein misfolding, and neuroinflammatory mediators. ML algorithms further support the optimization of protein binding affinity, selectivity, and blood–brain barrier permeability, addressing key translational challenges in central nervous system therapeutics. Beyond Alzheimer's disease, AI-assisted protein engineering accelerates the identification and refinement of disease-modifying proteins for Parkinson's disease, amyotrophic lateral sclerosis, and other neurodegenerative disorders by enabling target prioritization, structure-guided protein design, and in silico assessment of safety and efficacy [193]. In neurodegenerative disorders such as infantile ascending hereditary spastic paralysis (IAHSP), caused by mutations in the ALS2 (Alsin) protein, AI-generated structural models from databases such as AlphaFoldDB enable detailed mapping of disease-associated variants onto three-dimensional protein conformations. These structure-informed insights facilitate the rational design of therapeutic proteins and protein-based modulators by identifying destabilized domains, altered interaction interfaces, and mutation-sensitive regions that are amenable to correction or stabilization. AI-assisted structural annotation further supports the engineering of Alsin-derived protein constructs with improved folding, enhanced intracellular stability, and restored functional interactions, which are critical for targeting neuronal survival pathways. Therefore, the integration of AI-predicted protein structures into therapeutic development pipelines expands the feasibility of protein-based interventions for rare neurological diseases, accelerating target validation, variant interpretation, and precision protein engineering in clinical contexts where traditional structure determination methods are impractical [194].

In the case of other types of diseases, such as rheumatoid arthritis, multiple sclerosis, viral infections, and neurodegenerative disorders, AI-assisted antibody design accelerates therapeutic candidate selection and reduces developmental failure rates by predicting developability attributes such as aggregation propensity and manufacturability [195]. In a research study, high-accuracy structural models generated by DL frameworks were used for characterizing the organization, conformational dynamics, and protein–protein interaction interfaces that govern inflammasome assembly and activation of the NLRP3 (NOD-like receptor family, pyrin domain-containing protein 3), as a central mediator of innate immune activation implicated in chronic inflammatory and autoimmune diseases such as rheumatoid arthritis, inflammatory bowel disease, and neuroinflammatory disorders. These structural characterizations supported the rational engineering of inhibitory proteins, antibodies, and peptide-based modulators with improved specificity toward NLRP3 signaling components. By enabling in silico evaluation of binding modes, stability, and conformational accessibility, AI-assisted structure prediction reduced experimental uncertainty and accelerated the optimization of protein-based therapeutics aimed at modulating pathological inflammation [196].

Besides, AI-based protein structure prediction was successfully applied to proteins lacking experimentally determined structures, enabling detailed structural characterization of biologically relevant targets and facilitating downstream therapeutic protein development. For example, the three-dimensional configuration of human fatty acid transport protein 1 (FATP1) was predicted using AlphaFold 2, revealing the domain architecture and conformational features of this membrane-associated acyl-CoA ligase, which is crucial for long-chain fatty acid translocation and cellular lipid metabolism. The use of AI-predicted models provided a structural foundation that was otherwise unavailable due to the absence of complete crystallographic data, allowing subsequent molecular dynamics refinement and dynamic assessment of domain properties that inform functional mechanisms. Such in silico structural explanation could support the rational design of therapeutic proteins or biologics that modulate transporter activity in diseases associated with metabolic dysregulation. This approach highlights the broader application of AI-driven structure prediction in expanding the scope of protein therapeutics to targets beyond well-studied systems, accelerating both mechanistic understanding and therapeutic engineering efforts where traditional structural methods are limited [197].

While the benefits of dual-function proteins for theranostics are significant, several challenges still remain regarding their development and clinical translation [198,199]. Stability is a critical factor, because both the imaging and therapeutic components must remain functional over time and under physiological conditions. Additionally, the efficacy of dual-function proteins must be optimized to ensure that both the diagnostic and therapeutic aspects work effectively in tandem. This requires careful design and testing to ensure that the protein can achieve both high imaging sensitivity and therapeutic potency without compromising one function for the other. Another challenge lies in the production and manufacturing scalability of these proteins. Producing them in large quantities with consistent quality is essential if they will have widespread clinical use. Moreover, regulatory hurdles need to be overcome, because dual-function proteins combine both diagnostic and therapeutic elements, requiring a more complex approval process from regulatory agencies. These challenges must be addressed through continual research, development, and clinical trials.

5. Sustainable nanomedicine: principles and innovations

The advances in AI-driven protein design, as discussed in Section 3, have laid the foundation for a new era of precision medicine, enabling the rational design of biomolecules with optimized therapeutic potential. However, translating these molecular innovations into clinically viable solutions requires an equally transformative approach compared to drug delivery that arranges sustainability alongside efficacy [200]. Sustainable nanomedicine emerges as a critical discipline at this intersection, integrating green chemistry principles, AI-optimized design strategies, and theranostic innovation to develop eco-friendly and highly efficient nanocarriers [201]. This section discusses some key aspects of sustainable nanomedicine, including its foundational principles, AI-driven optimization, applications in theranostics, and frameworks for sustainability assessment.

5.1. Sustainability in nanomedicine

Sustainability in nanomedicine is a multidisciplinary concept that combines environmental responsibility, resource efficiency, and the long-term viability of nanocarriers in biomedical science [202]. As nanotechnology continues to develop drug delivery, diagnostics, and therapy, there is a growing need to ensure that these advances do not contribute to environmental pollution, bioaccumulation, or excessive resource consumption. Sustainable nanomedicine aims to develop eco-friendly nanocarriers that maintain high efficacy while minimizing harmful effects on human health and ecosystems [203]. This requires a holistic approach that includes raw material selection, green synthesis techniques, biodegradability, and regulatory compliance.

One of the key aspects of sustainability in nanomedicine is the choice of materials used in nanocarrier production. Traditional nanocarriers often rely on synthetic polymers, inorganic nanoparticles, or chemically modified lipids, some of which pose risks of toxicity and environmental persistence. To address these concerns, researchers are increasingly turning to biodegradable nature-derived materials such as polysaccharides, proteins, and lipids, which offer excellent biocompatibility and minimal long-term environmental impacts [204]. For example, chitosan and alginate-based nanocarriers have shown promising results in drug delivery due to their biodegradability and ability to enhance drug stability and absorption [205]. Similarly, lipid-based nanoparticles, including solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs), could provide an environmentally friendly alternative to synthetic polymer-based carriers [206].

One of the main methods used for producing sustainable nanomaterials is the adoption of green synthesis approaches. Conventional nanoparticle synthesis often involves hazardous organic solvents, high energy consumption, and the generation of toxic byproducts. Green chemistry principles include the use of solvent-free, water-based, or bio-assisted synthesis methods to reduce the environmental impact (Fig. 5) [207]. For instance, the biosynthesis of nanoparticles using plant extracts, bacterial, or fungal cells has gained attention as a sustainable alternative to chemical synthesis [208]. These biological systems can produce nanoparticles with a controlled size and morphology while eliminating the need for toxic reagents. Additionally, microwave-assisted and ultrasound-assisted synthesis techniques are energy-efficient methods that also reduce waste generation and processing time.

Fig. 5.

Schematic illustration comparing green and conventional synthesis routes for nanoparticle production. Conventional synthesis typically employs hazardous organic solvents, high temperatures, and multi-step reactions that generate toxic by-products and increase energy consumption. In contrast, green synthesis follows the principles of green chemistry, utilizing environmentally benign precursors, solvent-free or water-based systems, and biological agents such as plant extracts, bacteria, or fungi as reducing and stabilizing agents. These eco-friendly approaches yield nanoparticles with controlled size and morphology while minimizing waste, energy use, and ecological footprint. The figure highlights key distinctions in reactant sources, reaction media, energy inputs, and by-product management, emphasizing the sustainability advantages of bio-assisted and low-energy synthetic protocols. Reprinted with permission from Ref. [209]. Copyright 2024, Springer Nature.

Biodegradability and clearance of nanocarriers from the human body are also fundamental to sustainability in nanomedicine. Non-biodegradable nanomaterials, such as some metallic or carbon-based nanoparticles, can accumulate in the body and in the environment, raising concerns about their long-term effects [210]. To moderate this, researchers have focused on producing nanoparticles that degrade into non-toxic byproducts after fulfilling their therapeutic or diagnostic function. Stimulus-responsive nanocarriers, which only break down under specific physiological conditions such as pH changes or enzymatic activity, have been developed to provide biodegradability and controlled drug release [211]. For example, pH-sensitive polymeric micelles can dissolve in the acidic tumor microenvironment, ensuring drug release at the desired site while preventing unwanted accumulation in healthy tissues [212]. The large-scale production of nanocarriers requires careful management of raw materials, energy, and minimization of waste. By optimizing synthesis methods, reducing solvent use, and recycling excess materials, scientists can significantly decrease the environmental footprint of nanomedicine [213]. Additionally, AI-driven predictive models are being used to optimize formulations, reducing the need for trial-and-error experiments and minimizing material wastage [214]. These computational approaches could allow for rapid identification of the best nanocarrier compositions, ensuring maximum efficiency with minimal resource consumption.

Sustainability in nanomedicine also extends to ethical and regulatory agencies that promote the development of safer and more environmentally friendly medical products [215]. Nanocarrier formulations are required to meet evolving guidelines related to biocompatibility, toxicity, and environmental impact. Institutions such as the Food and Drug Administration and European Medicines Agency have introduced more rigorous safety assessments for nanoparticles, with particular emphasis on their long-term interactions with biological systems [216,217]. Additionally, ethical concerns surrounding nanomedicine, including the potential for unexpected ecological consequences and differences in access to the sustainable healthcare solutions, must be addressed through responsible research and regulatory governance.

AI could play a transformative role in advancing sustainable nanomedicine by optimizing resource efficiency, minimizing hazardous waste, and enhancing the environmental compatibility of nanomaterials [218]. The integration of AI-driven methodologies enables the design, synthesis, and therapeutic application of nanomedicines with a reduced environmental impact while maintaining therapeutic efficacy. Utilizing ML, DL, and CM techniques, AI can facilitate the discovery of biodegradable and biocompatible nanocarriers, optimizes energy-efficient manufacturing processes, and enhance lifecycle assessments to ensure minimal environmental harm [219]. Despite the comprehensive nature of the described technical approaches, their practical implementation has largely remained theoretical. Some recent studies have proposed ways to bridge these gaps, for instance, Xia and Wang et al. (2023) emphasized the potential of AI tools to reorganize biomedical design while remarking some limitations in translating computational predictions into practical laboratory results [220]. Shi and Hu (2023) demonstrated the AI-accelerated discovery of self-assembled peptides, providing a practical framework for developing nanocarriers that could be scaled up for commercial applications [221]. Bai et al. (2024) addressed the role of generative AI in guiding biomaterials innovation and discussed some strategies for overcoming translational barriers, including regulatory considerations and manufacturing scalability, thereby linking AI methodologies to real-world implementation challenges [222].

AI can significantly reduce the environmental impact of nanomedicines by facilitating the development of green synthesis approaches. As mentioned earlier, traditional chemical synthesis of nanomaterials often relies on hazardous solvents, energy-intensive processes, and non-biodegradable materials, leading to long-term environmental risks [223]. AI-driven predictive modeling could identify reaction conditions that optimize nanoparticle synthesis while minimizing toxic byproducts [224]. By analyzing vast chemical datasets, AI can recommend environmentally friendly precursors and catalysts that would replace harmful reagents with sustainable alternatives [225]. Furthermore, AI-guided process automation could enable precise control of reaction parameters, reduce energy consumption, material waste, and the emission of greenhouse gases in nanomedicine manufacturing.

AI could also enhance sustainability by enabling the design of biodegradable and eco-friendly nanocarriers. AI-driven analysis of large-scale molecular datasets enables the prediction of nanomaterial biodegradability and toxicity, facilitating the selection of materials that are compatible with biological and environmental degradation pathways. For instance, AI-driven molecular modeling can screen libraries of biopolymers, lipids, and natural polysaccharides that have good biocompatibility while maintaining drug delivery efficacy [226]. This approach could reduce the long-term accumulation of synthetic nanomaterials in ecosystems, and mitigate the potential environmental toxicity associated with persistent nanomaterial residues. Additionally, AI-powered algorithms could optimize the surface modification of nanocarriers to improve their breakdown in the physiological environment, ensuring controlled degradation without any harmful byproducts [227].

Energy efficiency is another crucial aspect where AI could significantly reduce the environmental impact of nanomedicine. AI-driven automation in nanomaterial fabrication could ensure precision in production, reducing energy consumption and optimizing the use of raw materials. AI-powered robotic systems could precisely regulate reaction conditions in real time, reducing batch-to-batch variability and eliminating excessive reagent consumption [228]. Additionally, AI-integrated computational fluid dynamics (CFD) simulation could optimize the design of reactors, improving mass transfer efficiency and reducing energy consumption in nanomedicine synthesis. These advances would not only minimize production costs but also enhance the overall sustainability of nanomedicine manufacturing [229].

Beyond synthesis and manufacture, AI is revolutionizing sustainable drug delivery strategies by enhancing drug formulation and minimizing pharmaceutical waste [36]. AI-powered PK and pharmacodynamic (PD) modeling enables the precise design of nanocarriers to improve drug loading efficiency and optimize controlled release mechanisms [230]. By predicting the most effective drug release profiles, AI could minimize the need for excessive amounts of drug, reducing the overall consumption of pharmaceutical compounds and their environmental damage. AI-guided personalized medicine supports patient-specific drug formulation, limiting overuse and reducing unnecessary pharmaceutical waste [231]. This personalized strategy could significantly reduce the accumulation of unused or expired medications in the environment.

The application of AI in waste management and recycling could further strengthen sustainability in nanomedicine. AI algorithms could optimize the recycling and repurposing of nanomaterials, ensuring that waste from nanomedicine production is efficiently processed and reused. ML models can predict the recyclability of different nanoparticles and suggest methods to recover valuable components from nanocarrier formulations [232]. AI-powered waste management strategies could also facilitate the development of closed-loop manufacturing systems, where byproducts from one stage of production are reused in the synthesis process, significantly reducing material waste and environmental pollution.

As AI continues to evolve, its role in fostering environmentally responsible nanomedicine will become increasingly indispensable. By integrating AI-driven approaches at every stage of nanomedicine development from material selection and synthesis to drug delivery and disposal, investigators can significantly reduce the ecological footprint of advanced nanomedicine. Therefore, the convergence of AI and sustainable nanomedicine supports therapeutic innovation that aligns medical advancement with long-term environmental sustainability [233].

5.2. AI-optimized nanocarrier design

The integration of AI into nanocarrier design could revolutionize drug delivery by enabling the optimization of nanoparticle properties, enhancing therapeutic efficiency, and minimizing material waste [218]. AI-driven approaches (Table 4), including ML, DL, and CM, are powerful tools for predicting nanocarrier behavior, optimizing formulations, and accelerating the development process [36]. Through the analysis of large datasets and advanced algorithms, AI enables scientists to design nanocarriers with tailored physicochemical properties, including improved drug loading, stability, targeted delivery, and controlled release (Fig. 6).

Table 4.

AI Approaches for Nanocarrier Design Optimization and their Measurable Performance Indicators.

AI Approach	Design Focus	Nanocarrier Parameters Optimized	Measurable Metrics	Ref.
Machine Learning (ML)	Predictive modelling of nanoparticle synthesis parameters (e.g., size, surface charge)	Particle size, zeta potential, polymer concentration, solvent type	Size distribution (nm), zeta potential (mV), model error metrics (R2, MSE, MAPE)	[234]
Deep Learning (DL)	Automatic extraction of morphology & shape from imaging; synthesis recipe-shape/size prediction	Morphology (spherical, rod, disc), shape classification, reaction recipe parameters, ligand type	Accuracy of shape classification (%), mean absolute error of size (nm), segmentation metrics	[235]
Generative AI (GANs, VAEs)	De novo design of nanocarrier architectures/materials for optimized performance	Polymer/lipid composition, hybrid ratios, chemical descriptors of ligands/precursors	Chemical stability, drug loading capacity, in silico predicted binding/interaction energy	[74]
Multi-objective Optimization	Balancing multiple objectives: efficacy vs biocompatibility, biodistribution, toxicity	Hemocompatibility, immune-response, biodistribution metrics, cell viability, IC50	In vitro cytotoxicity (% viability), in vivo tumor accumulation, pharmacokinetics parameters	[236]
Reinforcement Learning (RL)	Adaptive synthesis control and reaction optimization	Temperature, pH, catalyst concentration	Reaction yield (%), energy consumption (kJ/mol), synthesis time (min)	[237,238]
Bayesian Optimization (BO)	Parameter tuning with minimal experimental iterations	Precursor ratio, reaction time, surfactant content	Mean optimization cycles, convergence rate, prediction uncertainty	[239]
Support Vector Machines (SVM)	Classification of formulation, performance relationships	Nanoparticle type, surface functionalization, encapsulation efficiency	Classification accuracy (%), ROC AUC, F1-score	[79,240]
RF	Ensemble learning for toxicity and biocompatibility prediction	Material type, size, charge density	Feature importance ranking, model R2, RMSE, sensitivity/specificity	[241]
Neural Network Ensemble (NNE)	Hybrid prediction of physicochemical and biological properties	Surface chemistry, drug release kinetics	Predictive correlation coefficient, time-resolved release error (MAE)	[242]
CNNs	Image-based analysis of nanoparticle morphology	SEM/TEM images, aspect ratio, defect distribution	Morphological recognition accuracy (%), pixel-level error rate	[74]
Transfer Learning (TL)	Reusing pretrained models across nanocarrier systems	Feature maps, layer weights, cross-domain parameters	Cross-domain accuracy (%), data efficiency (gain factor)	[243]
Explainable AI (XAI)	Interpretable prediction of design performance links	Feature weights, sensitivity indices	SHAP/LIME scores, feature contribution rank, human-readable justification	[244]

Abbreviations: AI: Artificial Intelligence; ML: Machine Learning; DL: Deep Learning; GAN: Generative Adversarial Network; VAE: Variational Autoencoder; RL: Reinforcement Learning; BO: Bayesian Optimization; SVM: Support Vector Machine; RF: Random Forest; NNE: Neural Network Ensemble; CNN: Convolutional Neural Network; TL: Transfer Learning; XAI: Explainable Artificial Intelligence; MAE: Mean Absolute Error; MSE: Mean Squared Error; MAPE: Mean Absolute Percentage Error; RMSE: Root Mean Squared Error; R²: Coefficient of Determination; IC₅₀: Half-maximal Inhibitory Concentration; PK: Pharmacokinetics; ROC AUC: Receiver-Operating-Characteristic Area Under Curve; SHAP: Shapley Additive explanations; LIME: Local Interpretable Model-agnostic Explanations.

Fig. 6.

The beneficial application of AI for optimized nanocarrier design. . AI algorithms integrate experimental and patient-specific data to (i) refine synthesis parameters such as precursor concentration, temperature, and reaction time to achieve controlled nanocarrier formation; (ii) predict and optimize nanocarrier physicochemical properties, including particle size, surface charge, hydrophobicity, and drug encapsulation efficiency; (iii) model drug–nanocarrier interactions to forecast drug loading capacity, chemical stability, and release kinetics; (iv) enable real-time monitoring and adaptive drug delivery by dynamically adjusting release profiles in response to environmental stimuli such as pH, temperature, or enzymatic activity; and (v) analyze patient-specific biological data to support personalized therapeutic strategies. Collectively, AI-driven frameworks accelerate nanocarrier development while improving delivery precision, therapeutic efficacy, and safety. Produced by Blender 3.5.

One of the primary applications of AI in nanocarrier design is predictive modeling. Traditional nanocarrier development relies on extensive experimental testing, which is time-consuming and resource-intensive. AI algorithms can be trained on vast datasets of nanoparticle formulations and experimental outcomes, to predict the most effective composition and structure for each specific drug delivery application. For example, AI can analyze the relationship between particle size, surface charge, and drug encapsulation efficiency, allowing researchers to optimize nanocarrier properties with precision [245]. Support vector machines (SVM), neural networks, and genetic algorithms are commonly used to identify the optimal combination of materials and synthesis conditions [246].

Another crucial area where AI could enhance nanocarrier design is the prediction of drug-nanocarrier interactions. AI models can simulate how different drugs interact with different nanomaterials, by considering factors such as hydrophobicity, molecular weight, and chemical stability [247]. By predicting drug loading capacity, release kinetics, and biodistribution, AI could enable the design of more efficient and stable nanocarriers. Molecular docking and molecular dynamics simulations, powered by AI, could help visualize drug-nanocarrier interactions at the atomic level, ensuring better drug performance and controlled release profiles [248].

AI-driven optimization of nanocarrier synthesis could further contribute to sustainability and efficiency in nanomedicine. Conventional synthesis methods frequently depend on trial-and-error optimization, contributing to material waste and inconsistencies in nanoparticle properties. AI algorithms could refine synthesis parameters such as reaction time, temperature, and precursor concentrations to ensure reproducible and scalable nanoparticle production. Techniques like Bayesian optimization and RL can help to fine-tune synthesis conditions, improving yields and reducing environmental impact [249]. Additionally, AI-powered automation of nanocarrier synthesis facilitates high-throughput screening of different formulations, significantly accelerating the discovery of novel nanomedicines.

Personalized nanomedicine is another area where AI could make a transformative impact. By analyzing patient-specific data, AI models can design nanocarriers tailored to individual genetic profiles, disease characteristics, and treatment responses. AI-driven predictive analysis can identify the most suitable nanocarrier composition and drug dosage for personalized therapy, enhancing treatment outcomes while minimizing side effects. For instance, AI can assist in designing lipid nanoparticles (LNPS) for mRNA-based therapeutics by optimizing the lipid composition to enhance cellular uptake and stability [250].

Furthermore, AI could enable real-time monitoring and adaptive drug delivery systems. Smart nanocarriers, integrated with AI-powered biosensors, could respond dynamically to physiological changes in the body. For example, AI-enhanced stimulus-responsive nanoparticles could adjust the drug release rate based on pH, temperature, or enzyme activity at the target site [251]. This intelligent drug delivery approach could ensure that therapeutic agents are released exactly when and where they are needed, improving efficacy and reducing systemic toxicity.

AI could play a crucial role in nanocarrier toxicity assessment and regulatory compliance [247]. AI-driven predictive toxicology models enable early evaluation of nanocarrier safety using large-scale nanoparticle–biological interaction datasets [252,253]. AI-driven risk assessment could help researchers to identify potential immunogenicity, cytotoxicity, and long-term effects, facilitating the development of safer nanomedicines [254]. Additionally, AI-assisted regulatory frameworks could ensure compliance with evolving safety and efficacy standards set by agencies such as the Food and Drug Administration (FDA) and European Medicines Agency (EMA). AI-driven methods could simplify the optimization of key nanocarrier parameters, enhancing their performance while reducing resource-intensive trial-and-error experimentation. AI algorithms, particularly ML models, could be used to optimize nanocarrier size, shape, surface chemistry, and drug release profiles, which are critical factors that influence therapeutic efficacy and safety [255,256]. The size and shape of the nanoparticles manage their biodistribution, cellular uptake, and circulation time, so that smaller nanoparticles have better tumor penetration, while specific shapes, such as rods or discs, can enhance cellular interactions [257]. ML tools can analyze vast datasets to tailor surface modification, such as PEGylation (coating with polyethylene glycol (PEG)) or ligand conjugation, to improve targeting and reduce immunogenicity [258].

ML enables dynamic, real-time optimization of nanocarrier design by analyzing experimental data and synthesis parameters through predictive models and adaptive feedback loops [22,259] that allow iterative improvements during nanocarrier formulation, adapting to variations in experimental conditions and ensuring reproducibility [260]. AI-driven prediction of nanocarrier performance in preclinical and clinical contexts enables early identification of limitations and timely design optimization. AI-based nanocarrier optimization simultaneously enhances therapeutic precision and reduces environmental impact, providing a foundation for sustainable and high-performance nanomedicine and theranostic systems [218].

5.3. Nanocarriers in theranostics

The convergence of nanotechnology, AI, and theranostics has significantly advanced personalized medicine by facilitating simultaneous diagnosis and therapy within a single optimized nanoplatform [261]. One of the primary applications of AI-assisted smart nanocarriers is in real-time diagnosis and therapy. Traditional imaging techniques, such as MRI, CT scans, and fluorescence imaging, rely on contrast agents to enhance visualization [144,262]. However, conventional contrast agents often show poor biocompatibility, rapid clearance, and off-target accumulation. AI-powered models can optimize nanoparticle surface modification, size distribution, and functional coating, ensuring better imaging contrast and prolonged circulation in the bloodstream. Nanocarriers functionalized with gold nanoparticles (AuNPs) can be used for CT, iron oxide nanoparticles (IONPs) for MRI, and quantum dots (QDs) for fluorescence imaging, providing superior signal intensity and more precise disease detection [262]. AI-driven image processing and feature extraction algorithms could further enhance diagnostic accuracy by differentiating between healthy and diseased tissues, allowing for early-stage diagnosis and improved prognosis.

AI-assisted theranostic nanocarriers can also be engineered for targeted and responsive drug delivery. These smart nanocarriers can be functionalized with ligands, peptides, or antibodies that will specifically bind to biomarkers overexpressed on diseased cells, ensuring selective accumulation at pathological sites while minimizing systemic toxicity. AI models can analyze biochemical signals, tumor microenvironment properties, and cellular receptor distributions to refine targeting strategies [188]. Additionally, real-time monitoring systems integrated into nanocarriers can provide continuous feedback on drug efficacy, enabling adaptive treatment designs. For instance, biosensors embedded within nanoparticles could detect variations in pH, temperature, or enzyme levels within diseased tissues, and allowing dynamically triggered on-demand drug release to maximize therapeutic effects [263].

A major breakthrough in AI-powered theranostics is the development of controlled release mechanisms that can precisely regulate drug delivery over time [264]. Traditional drug delivery systems often release their payload extensively, leading to burst kinetics, suboptimal results, and systemic side effects. AI-enabled stimuli-responsive nanocarriers facilitate condition-specific drug release, improving therapeutic precision [265]. For example, pH-sensitive polymeric nanoparticles (PNPs) were degraded in the acidic tumor microenvironment, ensuring that cytotoxic drugs remain inactive until they reach the tumor site [266]. Similarly, thermo-responsive liposomes released their therapeutic contents when exposed to localized hyperthermia, minimizing damage to healthy tissues [267]. AI-driven predictive models can optimize release kinetics and dosing, ensuring that therapeutic agents are delivered at the right concentration, time, and location for maximum effect [268].

Furthermore, AI-driven theranostic nanomedicine could revolutionize multi-drug delivery systems, particularly in complex diseases such as cancer. Combination therapy, which involves the simultaneous administration of multiple drugs, is often necessary to overcome drug resistance and improve treatment success rates. AI-based computational models could analyze drug interactions, degradation profiles, and cellular uptake dynamics, enabling the design of intelligent nanocarriers that can deliver multiple therapeutic compounds in a precisely controlled manner [268]. This approach increases the possibility of synergistic effects and reduces the probability of resistance.

Table 5 presents a comparative analysis of AI-guided strategies for nanocarrier design, focusing on their application in sustainable and theranostic nanomedicine. It classified AI approaches based on key aspects such as material selection, biodegradability prediction, stability modeling, and responsiveness to stimuli. The table highlights how ML and DL algorithms could be employed to optimize physicochemical properties, reduce toxicity, and enhance targeting efficiency. Furthermore, it summarizes some AI-enabled nanocarrier systems developed for diagnostic-imaging integration, controlled drug release, and environmental safety.

Table 5.

Comparative analysis of AI-Guided nanocarrier design for sustainable theranostic applications.

Design Focus	AI Methodology	Sustainability Strategy	Theranostic Application	Innovative Example and Impact	Ref.
Biodegradable Nanocarrier Optimization	Random Forest, SVM Models	Predicts biodegradability from chemical structures and identifies green polymers	Enhances biocompatibility and controlled drug releases	AI identified optimal PLA-PEG formulations for degradation in physiological pH environments	[240]
Toxicity and Eco-Toxicological Risk Prediction	Deep Neural Networks (DNN), Quantitative Structure-Activity Relationship (QSAR) models	Forecasts acute/chronic toxicity and environmental persistence of nanocarriers	Minimizes off-target toxicity and environmental damage	QSAR + DNN pipeline screened >500 nanocarriers, filtering out those with potential organotoxicity	[[269], [270], [271]]
Smart Stimulus-Responsive Nanoparticles	Bayesian Optimization, Reinforcement Learning	Minimizes systemic drug exposure via localized, intelligent release mechanisms	Carriers respond to pH, redox, or enzymes at disease site for theranostic targeting	AI-optimized pH-sensitive polymer coated nanoparticles could selectively deliver drug + contrast agent to tumors	[272]
Integrated Imaging-Drug Delivery Systems	Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs)	Design dual-function nanocarriers by correlating structure to imaging signal and drug load	Provides simultaneous diagnosis and therapy in real-time (true theranostics)	CNN-designed gold nanorods for dual PET and photothermal therapy in breast cancer	[273,274]
Green Material Selection (SVM)	Transformer Models, Text Mining, AI-Based Polymer Design	Identifies renewable and biodegradable materials from literature and reaction databases	Enables eco-conscious nanocarrier synthesis with minimal synthetic waste	AI suggested cellulose derivatives from agricultural waste as viable drug delivery matrices	[[275], [276], [277]]
Optimization of particle size and ligand surface charge	SVM	Reduced use of reducing agents; eco-synthesis	CT-enhanced image-guided photothermal therapy	Enhanced biocompatibility; precise control of targeting	[154,201,247]
Adaptive control of synthesis and surface passivation	RL	Energy-efficient synthesis via feedback control	Optical biosensing and therapy	Real-Time optimization; dynamic tuning	[278]
Multi-objective optimization (size, pore volume, release rate)	Bayesian Optimization (BO)	Green dol-gel synthesis; recyclable precursors	Dual-mode drug and imaging agent loading	Balanced efficacy and biocompatibility	[74,239,279]
Prediction of drug binding affinity and porosity	Graph Neural Networks (GNNs)	Predictive toxicity and biodegradation modeling	MRI/PET dual imaging	High-throughput screening of MOF libraries	[280,281]
Model transfer for drug release kinetics	Transfer Learning (TL)	Sustainable lipid sources; low-energy processing	Targeted antioxidant delivery & NIR imaging	Reduced need for new experimental data	[282,283]
Optimization of photothermal and photodynamic properties	Evolutionary Algorithms (EAs)	Carbon-based biodegradable precursors	Real-time fluorescence and therapy	Tunable optical properties; low toxicity	[284,285]
Closed-loop adaptive drug release via biosensor feedback	Hybrid AI Framework (AI-IoT)	Minimizes dosage waste and environmental exposure	Real-time theranostic monitoring	Personalized treatment and sustainability	[286,287]

Abbreviation: AI: Artificial Intelligence; ML: Machine Learning; GAN: Generative Adversarial Networks; SVM: Support Vector Machine; RL: Reinforcement Learning; BO: Bayesian Optimization; GNN: Graph Neural Network; TL: Transfer Learning; EA: Evolutionary Algorithm; IoT: Internet of Thing; AuNP: Gold Nanoparticle; QD: Quantum Dot; MOF: Metal-Organic Framework; SLN: Solid Lipid Nanoparticle; QSAR: Quantitative Structure-Activity Relationship; DNN: Deep Neural Networks.

5.4. Sustainability metrics and assessments

The increasing use of nanotechnology in medicine has raised concerns in various quarters about its environmental impact, long-term safety, and regulatory considerations. As the field moves toward more sustainability, AI-powered assessment models and regulatory frameworks will play a crucial role in ensuring that nanomedicines are both effective and environmentally friendly [288]. Sustainable nanomedicine involves not only the adoption of green chemistry principles, but also comprehensive evaluation frameworks to measure the environmental and health impacts of these materials throughout their life cycle [203]. AI has emerged as a transformative tool to optimize nanocarrier design, predict toxicity profiles, and streamline regulatory compliance for eco-friendly nanomedicine. Besides, sustainability in nanomedicine also involves ensuring the biocompatibility and long-term safety of nanocarriers in human beings and patients [164]. AI-powered predictive models can analyze biological interactions, immune responses, and toxicity profiles to assess the safety of various nanomaterials. By integrating high-throughput screening data and computational simulation, AI can predict how different nanocarriers will behave in biological systems, helping researchers to design biodegradable, non-toxic, and highly efficient drug delivery systems. Moreover, AI-driven approaches could facilitate the early detection of potential side effects, allowing for the modification of nanocarrier formulations before clinical testing, thereby reducing animal testing and trial-and-error experimentation [257]. Due to the complex physicochemical properties and unpredictable biological interactions of nanocarriers, establishing standardized safety and efficacy guidelines has been challenging. AI-driven sustainability models could offer a data-driven approach by integrating ML algorithms that analyze nanomaterial properties, degradation pathways, and ecological interactions to predict potential environmental hazards [289]. It could provide a systematic approach to evaluating nanomedicine risks and benefits, ensuring that new formulations comply with international safety standards [290]. With the aid of AI, experts can identify low-impact nanocarrier formulations, minimize hazardous byproducts, and optimize waste management strategies. These AI-enhanced assessments may also enable real-time monitoring of nanomedicine distribution and accumulation in biological and environmental systems, providing valuable insights into their long-term effects. AI algorithms can analyze vast datasets from clinical trials, toxicology studies, and regulatory datasets to streamline the approval process for novel nanomedicines [291]. By automating risk assessment and regulatory reporting, AI could accelerate the transition of sustainable nanocarriers from research labs to clinical applications, reducing costs and time-to-market.

5.5. Clinical translation: challenges and regulatory perspectives

Despite some remarkable advances in AI-driven sustainable nanomedicine, the translation of these innovations into clinical practice faces substantial hurdles. First, large-scale manufacturing remains a critical bottleneck. Reproducibility, batch-to-batch consistency, and compliance with good manufacturing practice (GMP) standards are challenging for complex protein therapeutics and nanocarriers. Scaling laboratory processes up to industrial production often increase costs and introduces variability in quality [292]. Secondly, high costs and infrastructure requirements limit broad adoption. The production of advanced nanocarriers demands specialized facilities, advanced instrumentation, and high capital investment. These barriers may restrict availability to well-funded institutions and contribute to inequities in access [293]. Thirdly, regulatory approval pathways can be complex. For theranostic systems, which integrate diagnostic and therapeutic functions, dual approval processes are required. This raises unique challenges in defining standardized protocols for evaluation, long-term safety, and combined product classification (therapeutic vs. diagnostic) [294]. Fourthly, safety and standardization concerns must be taken into account. Long-term toxicity, immunogenicity, and the environmental persistence of nanocarriers remain incompletely understood. Predictive toxicology, standardized characterization methods, and post-market surveillance are needed to ensure patient safety and ecological responsibility [295]. And finally, ethical and accessibility issues cannot be ignored. Ensuring that AI-driven theranostics and sustainable nanomedicine are accessible beyond elite healthcare systems will be crucial for a global health impact [296]. Recent reports emphasize the importance of addressing these translational barriers through coordinated efforts between academia, industry, and regulatory bodies [220]. Proactive integration of clinical feasibility, regulatory compliance, and sustainability considerations will be essential for moving beyond theoretical promise to practical healthcare solutions.

6. AI-sustainable protein engineering framework

Building upon the conceptual and technological foundations established in Section 4, the AI-Sustainable Protein Engineering (AI-SPE) Framework described in Section 5 presents an integrated strategy for the design, development, and deployment of sustainable protein-based therapeutics. AI can not only accelerate protein discovery, but also promote eco-conscious, precision-driven innovation to bridge molecular biology with smart nanomedicine. By employing advanced AI algorithms, this framework would promote the convergence of protein science, green chemistry, and nanotechnology, ultimately facilitating the transformation of molecules into intelligent, sustainable theranostic tools.

6.1. AI-driven de novo protein design

De novo protein design, once constrained by limits on computational power and empirical trial-and-error methods, has been revolutionized through AI. Generative AI models, such as AlphaFold, RoseTTAFold, and ProteinMPNN, can accurately predict protein structure from amino acid sequences, enhancing our understanding of protein folding, thermodynamic stability, and functional potential [297]. These models use DL and RL to generate novel protein sequences tailored for specific biological activity or therapeutic goals. By integrating these tools, investigators can design synthetic proteins with high stability, specificity, and reduced immunogenicity.

In addition, AI can facilitate sustainable protein synthesis by optimizing biological production routes. ML algorithms can be employed to select efficient microbial hosts, design metabolic pathways, and predict expression yields with minimal resource consumption. Cell-free protein synthesis systems, enhanced by AI-driven process controls facilitate scalable and energy-efficient production without the need for complex biosensors [190]. These strategies collectively promote sustainable protein engineering for medical applications and reduce the environmental impact of therapeutic protein production (Fig. 7).

Fig. 7.

AI-driven de novo protein design for efficient and sustainable biomanufacturing. Schematic overview illustrating how AI accelerates and optimizes de novo protein design across cell-free and microbial production platforms. AI models enhance cell-free protein synthesis systems by integrating inputs such as DNA templates, nucleotides, amino acids, cofactors, and enzymes to efficiently produce functional biomolecules, including enzymes, antimicrobial peptides, and antibodies. Concurrently, AI-based prediction of expression yields enables the selection of protein sequences and regulatory elements that maximize production efficiency while minimizing resource consumption. AI-guided metabolic modeling supports the design of minimal and optimized biosynthetic pathways, reducing metabolic burden and energy requirements. In parallel, AI algorithms facilitate the selection of efficient microbial host strains with favorable growth and expression characteristics. Collectively, these AI-driven strategies promote sustainable protein engineering by improving productivity, lowering costs, and reducing the carbon footprint of therapeutic protein manufacturing. Produced by Blender 3.5.

6.2. AI-powered enzyme engineering for green chemistry

Enzymes are the essential ingredient for eco-friendly biocatalysis, and AI is central to their next-generation design [298]. Enzyme engineering plays a pivotal role in AI-driven sustainable nanomedicine, particularly in the development of enzyme-responsive theranostic systems and environmentally benign therapeutic platforms. Beyond their catalytic function in green chemistry, enzymes serve as critical biological actuators in drug delivery, prodrug activation, and stimuli-responsive nanocarriers. AI-powered enzyme engineering therefore represents a unifying strategy linking sustainable molecular synthesis with intelligent therapeutic function [[299], [300], [301]]. By analyzing sequence-structure-function relationships, AI models such as CNNs and GNNs could identify key amino acid residues for mutagenesis and predict functional outcomes [297]. This could enable the creation of enzyme variants with enhanced activity, selectivity, and thermal stability, each tailored for sustainable pharmaceutical synthesis and industrial biocatalysis (Fig. 8). These AI-powered modifications could reduce dependency on harsh solvents, lower reaction temperatures, and minimize toxic by-products as hallmarks of green chemistry [302].

Fig. 8.

AI-guided creation of enzyme variants for improved catalytic performance and sustainability. Illustration depicting the role of artificial intelligence (AI) in the rational generation and optimization of enzyme variants with superior functional properties. AI-driven design strategies enable the development of enzymes exhibiting enhanced catalytic activity and substrate selectivity, improved thermal stability under elevated temperatures, and minimized formation of toxic or undesired by-products. In addition, AI-assisted enzyme engineering reduces reliance on harsh organic solvents by tailoring enzyme structures to function efficiently under mild and environmentally benign reaction conditions. Collectively, AI-enabled enzyme variant design accelerates biocatalyst development while improving process efficiency, robustness, and sustainability across industrial and biomedical applications. Produced by Blender 3.5.

Furthermore, AI aids in designing enzyme-activated drug delivery systems. Predictive models simulate enzyme-substrate interactions and hydrolytic activity within physiological environments, guiding the design of prodrugs and responsive nanocarriers [303]. Such enzyme-triggered systems ensure that therapeutic agents are released only at target sites, minimizing systemic toxicity and maximizing therapeutic efficacy. AI-enhanced enzyme engineering thus underpins both environmentally sustainable synthesis and intelligent drug delivery mechanisms. Besides, AI-engineered enzymes complement de novo protein design (mentioned in section 5.1) and peptide/protein-based nanomedicine (mentioned in section 5.4) by enabling both sustainable therapeutic production and site-specific, enzyme-activated drug delivery.

6.3. AI-integrated multi-omics for functional protein discovery

Multi-omics includes data derived from genomics, transcriptomics, proteomics, metabolomics, and epigenomics. This approach has revolutionized our ability to study proteins in biological systems [228]. However, extracting meaningful information from this vast, complex, and high-dimensional datasets remains challenging. AI could serve as a powerful tool to integrate and analyze these datasets, uncovering novel protein functions, disease mechanisms, and therapeutic targets [250]. DL- and ML-based data-driven models enable the identification of functional protein–pathway relationships, driving progress in precision medicine and protein engineering.

AI-driven functional protein discovery focuses on identifying proteins with significant activity in cellular processes, disease pathology, and potential therapeutic applications [304]. Traditional methods for predicting protein function have relied on homology-based approaches, requiring the identification of sequence similarity to known proteins [253]. However, AI could overcome this limitation by employing DL models such as transformers, convolutional neural network, autoencoders to analyze raw sequence data and predict function even for previously uncharacterized proteins. Additionally, AI-based structural biology tools such as AlphaFold, ResettaFold, and ProteinMPNN can predict protein 3D structure with remarkable accuracy [305]. DL models, such as GNNs and VAEs, could process high-dimensional omics data to reveal protein-protein interactions, regulatory networks, and post-translational modifications. AI also could enhance biomarker discovery by analyzing omics data to identify disease-specific protein signatures, which could be used for early diagnosis, prognosis, and targeted therapy development. AI could also play a crucial role in protein ligand interaction modeling and drug discovery [255]. AI-driven molecular docking simulation and RL-based virtual screening methods could accelerate the identification of proteins that could serve as drug targets [256]. These models could predict how proteins interact with potential drug candidates, refining the selection process and reducing the need for extensive wet-lab experimentation. Additionally, AI-driven proteomics workflows could predict binding affinities, enzyme-substrate interactions, and post-translational modifications, further reorganize the drug development frameworks [297].

6.4. AI-enabled sustainable peptide and protein-based nanomedicine

The integration of AI into peptide and protein-based nanomedicine is expected to revolutionize the landscape of drug design and delivery, offering highly efficient, precision-targeted, and environmentally sustainable therapies [255]. Peptides and proteins have gained considerable attention for targeted therapy and biodegradable nanocarrier systems, owing to their intrinsic biocompatibility, molecular specificity, and functional versatility [256]. Importantly, their inherent biodegradability and minimal environmental impact render them ideal building blocks for the development of sustainable nanomedicine platforms. Despite these advantages, the clinical translation of peptide and protein-based therapeutics has historically been constrained by challenges such as poor stability, rapid enzymatic degradation, and suboptimal delivery efficiency [306]. AI-driven strategies are now overcoming these limitations by optimizing peptide and protein design, improving molecular stability and bioavailability, and engineering intelligent, self-assembling nanocarriers capable of controlled, site-specific drug release [307]. AI models can accurately predict degradation-prone regions and suggest modifications such as the incorporation of D-amino acids, cyclic peptides, backbone modifications, or non-natural amino acid residues to enhance peptide stability and prolong systemic circulation [260,261]. ML models, including DL models, RL, and generative algorithms, can enable the rational design of peptides with high binding affinity to specific molecular targets. These models could analyze extensive datasets of protein-peptide interactions, SAR, and PK profiles to predict an optimal peptide sequence characterized by good selectivity, stability, and functionality [258]. Generative AI tools such as AlphaFold, ProteinMPNN, and transformer-based architectures could facilitate de novo peptide design tailored for specific biological purposes. These technologies could dramatically reduce the time, cost, and ecological impact traditionally associated with peptide drug development, in alignment with the principles of green chemistry and sustainable biomedical innovation [308]. Such AI-guided molecular engineering could not only improve clinical efficacy but also support sustainability by reducing dosing frequency and lowering production requirements.

Furthermore, AI could facilitate the design of multifunctional nanopeptides capable of playing two roles at once, i.e. targeting disease-specific biomarkers while concurrently acting as drug carriers or immune modulators. For instance, peptide-based nanosystems were engineered by AI-guided techniques for cancer immunotherapy, with improved tumor antigen recognition and superior therapeutic efficacy [262]. Similarly, in infectious disease therapy, AI-designed antimicrobial peptides have demonstrated increased selectivity, lower potential for resistance development, improved bioavailability, and complete biodegradability as an eco-friendly alternative to conventional synthetic antibiotics [79,309]. AI models are instrumental in designing protein-based self-assembling nanostructures with high drug encapsulation efficiency, stimuli-responsive release profiles, and benign biodegradation pathways. Such smart protein nanocarriers minimize off-target effects, lower systemic toxicity, and naturally decompose into non-toxic metabolites, supporting the overarching goals of environmental stewardship and sustainable healthcare [310]. Accordingly, AI-enabled peptide- and protein-based nanomedicine is emerging as a cornerstone of sustainable next-generation theranostics, enabling improved therapeutic precision, functional stability, and environmental compatibility.

7. Bridging molecules and medicine: clinical translation

Building upon the AI-SPE framework discussed in the previous section, this section explores how AI-driven molecular designs can transition from a theoretical model to clinical applications. AI integration across drug discovery, theranostics, and precision treatment strategies enables the translation of protein-based therapeutics and sustainable nanomedicine from molecular design to clinical application. In other word, AI-driven predictive modeling, automation, and pattern recognition accelerate the development and optimization of protein-based therapies for complex diseases such as cancer and neurodegenerative disorders.

7.1. From molecular design to clinical implementation

The advent of AI-enhanced drug discovery strategies could revolutionize protein-based therapeutics by significantly reducing development timelines and enhancing molecular precision. AI-driven platforms, including DL and RL models, can analyze vast biological datasets to identify promising drug candidates, predict protein-drug interactions, and optimize molecular configurations for stability, solubility, and bioavailability [311,312]. AI algorithms can simulate protein folding and binding affinities, ensuring that novel therapeutic proteins show high specificity and minimal off-target effects.

Despite promising computational and animal-model results, translation to human trials is frequently hindered by discrepancies between predicted and observed pharmacokinetics, immunogenicity, and off-target effects. Protein folding accuracy and binding affinity predictions do not consistently translate into predictable biodistribution or long-term safety in vivo. Consequently, AI-generated candidates often require extensive empirical optimization before meeting regulatory thresholds for clinical entry [313].

AI has also been proposed as a tool to improve clinical trial design, particularly by enabling biomarker-guided patient stratification and adaptive trial protocols. While such approaches may reduce trial size and duration in principle, their regulatory trial size and duration in principle, their regulatory acceptance remains limited, as adaptive AI-driven trial designs must still comply with stringent requirements for transparency, reproducibility, and interpretability. At present, AI-assisted trial optimization is primarily used as a supportive decision-making tool, rather than a replacement for conventional trial methodologies, and its impact on accelerating regulatory approval has yet to be demonstrated at scale [314].

7.2. Hybrid systems in theranostics

AI-driven protein engineering has facilitated the conceptual design of multifunctional proteins with enhanced targeting capability, improved stability, and tunable degradation profiles [315]. When combined with nanocarriers, these proteins form hybrid theranostic systems intended to combine diagnostic and therapeutic functions within a single platform. While preclinical studies have shown encouraging targeting efficiency and imaging performance, clinical translation of such hybrid systems remains extremely limited [316].

In oncology, protein-nanocarrier hybrids have demonstrated improved tumor accumulation and reduced systemic toxicity in animal models. However, these benefits are often diminished in human systems due to biological heterogeneity, protein corona formation, and altered nanoparticle clearance pathways. Furthermore, scaling up the manufacturing of complex hybrid constructs under GMP conditions introduces additional challenges related to reproducibility, batch consistency, and quality control [317].

In neurodegenerative disease applications, AI-guided theranostic systems have been proposed to enhance blood-brain barrier penetration and enable real-time disease monitoring [318]. Nevertheless, successful blood-brain barrier transport observed in preclinical models rarely translates reliably to human patients. Long-term safety concerns, particularly regarding nanoparticle accumulation and chronic exposure, further constrain clinical progression. Most AI-designed theranostic hybrids remain at the proof-of-concept or early preclinical stage, with no established pathway to regulatory approval [319].

7.3. AI for patient stratification and personalization

Effective clinical translation of protein therapeutics and theranostic nanomedicine requires robust patient stratification and personalization strategies. AI-based models can analyze multidimensional genomic, proteomic, and clinical datasets to identify biomarker-defined patient subgroups with differential therapeutic responses [320]. Such approaches have shown value in retrospective analyses and early feasibility studies, but their prospective clinical utility remains under validation.

While AI-driven stratification may improve patient selection and reduce variability in clinical trials, its implementation faces several challenges. Biomarker reproducibility across populations, data bias, and limited standardization of omics datasets complicate model generalizability. Moreover, regulatory agencies require clear mechanistic justification for biomarker-guided treatment decisions, limiting the development of purely data-driven stratification models in clinical practice.

AI-assisted personalization strategies, including dynamic dose adjustment and real-time treatment adaptation, are similarly constrained by clinical feasibility and regulatory oversight [321]. Continuous data integration from patients introduces concerns related to data quality, interpretability, and clinical accountability. In protein-based nanomedicine, tailoring formulations and delivery mechanisms to individual patients remains largely experimental, with scalability and cost-effectiveness posing significant barriers [322]. While AI-enabled personalization represents a long-term objective, its current clinical impact remains incremental rather than transformative.

8. Cutting-Edge innovations and emerging trends

Emerging innovations and forward-looking trends are expected to shape the next generation of drug discovery and development. These advances extend existing AI-driven methodologies and incorporate transformative technologies that are redefining disease understanding and therapeutic intervention. In particular, the integration of quantum computing with AI, the convergence of AI with nanotechnology and synthetic biology, and the application of AI to eco-friendly medical technology are poised to facilitate the next generation of therapeutics, further enhancing the clinical translation of drug discovery (Fig. 9).

Fig. 9.

Future directions in AI-driven drug discovery. Schematic representation of emerging technological frontiers that extend current AI-based drug discovery strategies toward next-generation therapeutic development. The left panel illustrates the integration of AI with quantum and advanced computational approaches for accurate prediction of protein structures and behaviors, enabling the efficient identification of viable drug candidates. The right panel highlights the convergence of AI with nanotechnology and synthetic biology, facilitating the design of advanced therapeutic systems with improved biodistribution, pharmacokinetics, and controlled drug-release profiles, alongside enhanced stability, activity, and selectivity. The lower panel emphasizes the application of AI in eco-friendly medical technologies, where AI-guided optimization supports sustainable drug production through scalable processes, reduced energy consumption, minimized waste, and decreased reliance on harmful chemicals. Collectively, these innovations represent key emerging trends poised to accelerate clinical translation and redefine the future landscape of drug discovery and development. Produced by Blender 3.5.

8.1. Quantum computing meets AI to optimize protein design

As we continue to push the boundaries of protein design, quantum computing could be a groundbreaking step forward in our ability to model complex molecular dynamics. Quantum-enhanced protein modeling could allow for the simulation of molecular interactions with a level of precision that was previously unimaginable [297,323]. This advance is particularly significant for the design of therapeutic proteins, because quantum computing can deal with the vast number of variables that affect protein folding, stability, and function. When integrated with AI, quantum computing could enable accurate prediction of protein structure and behavior, which could then expedite the identification of viable drug candidates and accelerate the design of therapeutic proteins with high specificity.

AI-quantum hybrid systems are now emerging as a powerful tool to predict protein folding. These systems combine the computational power of quantum mechanics with the learning capability of AI to predict protein folding patterns with exceptional accuracy [297]. Such hybrid systems hold immense potential for enhancing the development of biologics, enzyme-based therapeutics, and proteins targeting specific disease pathways. This synergistic approach not only enables faster protein design but also offers a more precise and efficient means of developing next-generation therapeutics for complex diseases, such as cancer and neurodegenerative disorders [324].

8.2. Convergence of AI, nanotechnology, and synthetic biology

The convergence of AI, nanotechnology, and synthetic biology is also a transformative development in the field of therapeutic design. AI-driven synthetic protein design is paving the way for the creation of next-generation therapeutics that are highly specific and optimized for individual patients [164]. By employing ML algorithms, researchers can design proteins that exhibit high stability, activity, and selectivity, significantly improving the effectiveness of biologic therapy. This approach is particularly important for diseases that are difficult to treat with traditional small-molecule drugs, such as certain cancers and genetic disorders.

The combination of AI with bioengineering and nanomedicine could allow the creation of personalized healthcare solutions. Nanocarriers, which can deliver therapeutic proteins directly to target cells, could be optimized using AI to improve their pharmacokinetics, biodistribution, and drug-release profiles. These AI-enhanced nanomedicines could allow for highly targeted and efficient drug delivery, minimizing side effects and maximizing therapeutic effects. Moreover, the integration of synthetic biology with nanotechnology could promote the development of self-assembling nanostructures and biohybrid systems that could respond to specific disease markers, making them highly versatile tools for personalized medicine [325].

8.3. AI for eco-friendly medical technology

With a growing emphasis on sustainability in the pharmaceutical industry, AI is emerging as a key driver of environmentally friendly advances in medical technology. One of the key applications of AI in this domain is in biomanufacturing, where AI algorithms can be used to optimize manufacturing processes for biologic drugs. AI-enabled biomanufacturing allows for more efficient and sustainable drug production by reducing waste, energy consumption, and the use of harmful chemicals. Additionally, AI can assist in the scale-up of biomanufacturing processes, ensuring that these sustainable practices can be applied in large-scale production settings [326].

Another important development is the application of AI in green chemistry in the pharmaceutical industry. Green chemistry principles aim to minimize the environmental impact of chemical processes by reducing the use of toxic substances and improving resource efficiency [298]. AI can help to accelerate the adoption of green chemistry by enabling the design of more efficient, eco-friendly chemical processes for drug and protein synthesis. AI algorithms can predict the most sustainable reaction pathways, identify alternative reagents, and optimize reaction conditions, all of which contribute to the development of more sustainable pharmaceutical manufacturing practice [297]. This is a significant step forward in ensuring that in the future drug development not only delivers effective treatments, but does so in an environmentally responsible manner.

9. Challenges and opportunities

The convergence of AI, protein engineering, and sustainable nanomedicine holds transformative potential for theranostics; however, translation to real-world healthcare remains hindered by critical challenges. These encompass ethical dilemmas, regulatory complexities, technical barriers, and the persistent gap between preclinical promise and clinical reality [327]. Addressing these issues will be crucial to ensure that AI-driven innovations in protein design and nanomedicine contribute to sustainable, equitable, and trustworthy advances in global healthcare. Fig. 10 provides a schematic overview of the key challenges, opportunities, and future research directions in AI-driven protein design and sustainable nanomedicine discussed in this section.

Fig. 10.

Challenges, opportunities, and future directions in AI-driven protein design and sustainable nanomedicine for advanced theranostics.

One of the most pressing concerns is the valley death between laboratory success and clinical translation. Despite impressive in silico and in vitro outcomes, many AI-predicted protein structures and optimized nanocarriers fail in in vivo systems due to unforeseen biological complexity, off-target effects, or lack of reproducibility. Integrating experimental feedback loops, standardized validation protocols, and real-world performance data is essential to bridge this translational divide [328]. Moreover, sustainability a core theme of this review requires critical reflection. While AI offers robustness to more efficient molecular design and greener nanomanufacturing, the environmental cost of training large DL models such as AlphaFold and RoseTTAFold remains immense [[329], [330], [331]]. Acknowledging this carbon footprint and pursuing green AI initiatives that minimize energy consumption should be prioritized as part of responsible innovation.

9.1. Ethical and regulatory considerations

Ethical considerations in AI-guided protein engineering are influenced by issues of data bias, algorithmic transparency, privacy protection, and potential dual-use applications. Training datasets frequently over-represent specific protein families or patient demographics, leading to inequitable model outputs and biased therapeutic designs [332,333]. Biases in AI training may compromise therapeutic effectiveness for underrepresented populations, reinforcing inequities in healthcare access and clinical outcomes. Implementing diverse, well-curated datasets and bias-correction frameworks is vital to promote fairness in AI-driven biomedical discovery.

The “black box” nature of DL systems further complicates scientific trust and regulatory approval. When AI tools such as AlphaFold produce accurate predictions without transparent mechanistic explanations, regulatory agencies face difficulty reliability and safety. Developing explainable artificial intelligence (XAI) models and hybrid approaches that integrate mechanistic understanding can enhance interpretability and trustworthiness, facilitating smoother approval pathways [[334], [335], [336]].

Regulatory complexity remains a major translational blockage. Agencies such as the FDA and EMA enforce rigorous safety and efficacy standards, yet existing frameworks were not designed for AI-generated biomolecules or hybrid theranostic platforms. For dual-function systems that combine diagnostic and therapeutic modalities, dual or sequential approvals are often required, prolonging timelines and increasing cost. Harmonized international guidelines for AI-assisted molecular design, predictive toxicology, and long-term monitoring are urgently needed [321].

Ethical and accessibility issues must also be acknowledged. High costs, limited infrastructure, and lack of scalable GMP-compliant production restrict the availability of AI-designed proteins and nanocarriers to well-funded institutions, exacerbating global inequities. Ensuring affordability and equitable access particularly in resource-limited settings should be a key criterion in evaluating ethical readiness for clinical deployment. Finally, sustainability and environmental responsibility must become part of regulatory oversight. The incorporation of biodegradable nanocarriers, energy-efficient AI architectures, and transparent life-cycle assessments should be incentivized by policy instruments. In this way, ethical, regulatory, and ecological standards can evolve in harmony with the rapid pace of AI innovation [337].

9.2. Overcoming technical barriers

AI-driven protein engineering and sustainable nanomedicine face hurdles relating to explainability, reliability, and scalability. The lack of explainability in AI models is a major concern in protein design, as DL frameworks such as AlphaFold and RoseTTAFold generate highly accurate predictions but often act as black-box systems [[338], [339], [340]]. This lack of transparency complicates scientific validation and regulatory approval, making it difficult for researchers to fully understand how AI has reached its conclusions. Developing XAI techniques, integrating mechanistic insights with AI prediction, and employing hybrid modeling approaches could enhance trust in AI-driven biomolecule design [341].

Reliability is another challenge, because AI models often struggle with generalizability across diverse molecular structures and biological environments. AI-generated protein designs may perform well in silico but fail in vitro or in vivo, necessitating extensive experimental validation. To overcome this, integrating AI with high-throughput screening, molecular dynamics simulations, and experimental feedback loops could improve the robustness and accuracy of AI-predicted biomolecules [342].

Scalability remains a major limitation in AI-driven sustainable nanomedicine. While AI models can predict optimal nanoparticle formulations and drug interactions, transitioning these designs to large-scale manufacturing is often hindered by synthetic variability, reproducibility issues, and high production costs. Compliance with GMP standards and batch-to-batch consistency remains difficult for complex bio-nano constructs. Furthermore, high capital and infrastructure requirements hinder adoption in low-resource environments. Advances in scalable, automated synthesis and sustainable nanomanufacturing will be key to overcoming these limitations [343].

Another overlooked dimension is the computational sustainability of DL models themselves. The energy demands of training massive AI architectures contribute significantly to carbon emissions [344]. Future strategies should therefore focus on low-energy, model compression, and federated-learning approaches that reduce resource dependency without compromising predictive accuracy.

9.3. Collaboration across disciplines

AI-driven protein engineering and nanomedicine inherently demand interdisciplinary integration across computational science, molecular biology, materials chemistry, and clinical research. AI alone cannot capture the dynamic biochemical complexity of biological systems; hence, continuous collaboration between computational moderates and experimental scientists is indispensable for refining predictive accuracy and practical applicability [14]. Enhanced collaboration should extend beyond academia. Public-private partnerships are critical in translating AI-based discoveries into clinical products. Cooperative efforts among universities, pharmaceutical companies, AI start-ups, and regulatory agencies have already accelerated innovation for instance, DeepMind's AlphaFold, Insilico Medicine's generative design platforms, and BioNTech's AI-optimized mRNA therapeutics. Expanding these collaborations through open-access data sharing, transparent benchmarking, and global funding initiatives will democratize AI-driven therapeutic development [345].

Finally, sustainable innovation will depend on cross-sector coordination that aligns ethical, regulatory, and environmental priorities. By integrating clinical feasibility, sustainability metrics, and social-equity considerations from the outset, interdisciplinary teams can ensure that AI-guided protein engineering and nanomedicine evolve from theoretical potential to transformative, inclusive healthcare reality [346].

10. Future perspectives

Advances in AI-guided protein design and sustainable nanomedicine are shaping the future of therapeutics, enabling more efficient drug discovery, improved clinical translation, and personalized medicine approaches. This section highlights emerging trends and proposes some bold, testable hypotheses that could define the direction of AI, nanotechnology, and synthetic biology research over the coming decade.

10.1. The next decade of AI in protein engineering and nanomedicine

As AI continues to evolve, its role in sustainable drug design is expected to expand significantly. In the next decade, AI is predicted to become an integral part of the drug discovery process, not just for designing proteins but also for improving the overall efficacy and sustainability of therapeutic development. AI algorithms will increasingly predict the behavior of complex biological systems, identify novel drug candidates, and design therapeutic proteins that are both highly effective and environmentally friendly [347]. One of the bold hypotheses is that AI-guided protein engineering will reduce resource consumption and experimental waste by at least 50 % compared to conventional approaches [348]. This is a prediction that can be validated through systemic benchmarking studies. Moreover, AI-driven optimization of production workflows is hypothesized to enable carbon-neutral therapeutic manufacturing within the next decade [349]. AI will also play a central role in advancing synthetic biology combined with nanotechnology. Future systems may allow the creation of synthetic proteins with programmable therapeutic functions. A testable hypothesis is that AI-optimized nanocarriers combined with synthetic proteins will achieve at least a two-fold increase in target specificity while reducing off-target toxicity by 30 % in preclinical models. Such measurable outcomes will pave the way toward more personalized and eco-friendly treatment paradigms.

10.2. Towards fully AI-integrated theranostic systems

AI-integrated theranostic systems, in which diagnosis and treatment are seamlessly interconnected, are no longer a distant vision, but an emerging reality. AI's capacity to create closed-loop systems for real-time diagnosis and therapy represents a transformative step. In the coming years, fully integrated platforms are expected to not only detect disease at early stages but also continuously tailor therapeutic regimens in real-time using patient-specific data. For instance, a bold and testable hypothesis is that AI-driven multi-modal systems can leverage genomic data, medical imaging, and wearable sensors to achieve diagnostic accuracy rates exceeding 95 % in detecting early-stage cancers. After disease detection, these systems could automatically initiate the delivery of targeted therapy using AI-optimized nanocarriers or synthetic proteins, enabling a dynamic, personalized approach to treatment. Over time, the systems would learn and refine themselves, leading to progressively shorter response times and higher treatment success rates across clinical cohorts [350]. Another transformative hypothesis is that AI-enabled precision medicine workflows will cut the time from biomarker discovery to clinical trial initiation by 40 %, thereby accelerating the translation of laboratory findings into patient-ready therapies. Automation of these workflows will not only reduce costs but also improve patient outcomes by ensuring that the right therapy is delivered at the optimal time [351].

11. Conclusion

The future convergence of AI-driven protein design and sustainable nanomedicine represents a defining direction for next-generation theranostic systems, with the potential to deliver transformative advances in both diagnosis and therapy. Looking ahead, AI-SPE is expected to integrate ML, CM, and green nanotechnology to create next-generation biomolecules and nanocarriers with high precision and efficiency. AI's ability to predict protein structure, optimize their function, and guide eco-friendly synthetic methods could ensure not only highly functional but also sustainable biomolecules. Smart nanocarriers and AI-enhanced drug delivery systems could advance controlled, targeted therapy, boosting treatment efficacy and safety in precision medicine. Crucially, AI-SPE is expected to bridge the gap between molecular design and clinical application. Data-driven approaches, such as multi-omics analysis, predictions of protein-ligand interactions, and structural modeling, are expected to replace traditional trial-and-error methods, accelerating innovation while reducing costs. AI could also enable real-time disease monitoring and the development of adaptive, personalized theranostic systems, tailored to the individual patient's genetic and biochemical profile. DL could facilitate the discovery of novel protein-based drugs and sustainable nanocarriers, uncovering opportunities previously thought impossible. Future advances may also arise from the integration of AI with quantum computing devices and synthetic biology to design self-assembling nanocarriers, advanced biosensors, and intelligent drug delivery systems with precise, on-demand therapeutic release. Accordingly, these advances promise breakthroughs in treating cancer, neurodegenerative diseases, infections, and metabolic disorders. AI-powered automated pipelines will reorganize drug discovery from in silico design to clinical application, ensuring stability, selectivity, and environmental safety. However, key challenges such as AI explainability, model validation, data bias, regulatory compliance, and ethical concerns must be addressed. Rigorous standards and interdisciplinary collaboration between AI experts, molecular biologists, clinicians, and regulators are essential to ensure safety, efficacy, and equitable access to AI-designed therapeutics.

CRediT authorship contribution statement

Donya Esmaeilpour: Writing-Original draft, Conceptualization. Michael Rm Hamblin: Writing- review & editing, conceptualization. Jianling Cheng: Writing-original draft, visualization (Chapter 2). Arezoo Khosravi: Visualization. Jian Liu: visualization (Figure 2). Atefeh Zarepour: Writing-review & editing, visualization. Ali Zarrabi: Writing-review & editing, Conceptualization. Mika Sillanpaa: Writing-review & editing, conceptualization. Ehsan Nazarzadeh Zare: Writing-review & editing, Investigation. Jianling Shen: Writing-review & editing. Hassan Karimi-Maleh: Writing-review & editing, provided funding for publication costs.

Ethics approval and consent to participate

This manuscript is a review article and does not contain any original studies with human participants or animals performed by any of the authors. Therefore, ethics approval is not required.

Declaration of competing interest

Jianliang Shen is an editorial board member for Bioactive Materials and was not involved in the editorial review or the decision to publish this article. All authors declare that there are no competing interests.