Aug 2026· Health Nanotechnology· Vol 2· 0 citations· 140 references
TL;DR
This review provides a structured overview of AI applications in nanotechnology, including data-driven nanomaterial discovery and inverse design using machine learning (ML) and generative models, optimization of nanoparticle synthesis through Bayesian optimization and self-driving laboratories, targeted drug delivery and personalized nanomedicine enabled by predictive ML models.
Abstract
The convergence of artificial intelligence (AI) and nanotechnology has substantially transformed the discovery, design, synthesis, characterization and biomedical application of nanomaterials. This review provides a structured overview of AI applications in nanotechnology, including data-driven nanomaterial discovery and inverse design using machine learning (ML) and generative models, optimization of nanoparticle synthesis through Bayesian optimization and self-driving laboratories, targeted drug delivery and personalized nanomedicine enabled by predictive ML models, deep learning for nanoscale imaging, spectroscopy and real-time particle tracking, AI-accelerated simulation of nanofluids and complex nanosystems, together with the emerging challenges, opportunities and future directions of AI-driven nanotechnology. We discuss the capabilities and limitations of widely used AI methods, including artificial neural networks, random forests, support vector machines, reinforcement learning, generative adversarial networks, variational autoencoders, graph neural networks and large language models, highlighting their suitability for different nanotechnology applications. In addition, the review examines key challenges that currently limit broader translation of AI-enabled nanotechnologies, including limited availability of standardized high-quality datasets, model interpretability, reproducibility, validation across independent datasets and regulatory considerations. Finally, we discuss emerging research directions, including autonomous experimentation, multiscale AI frameworks, AI-assisted nanorobotic systems and closed-loop therapeutic platforms, emphasizing that these represent promising future opportunities requiring further technological development and rigorous experimental and clinical validation.
Artificial intelligence (AI) and nanotechnology have emerged as two transformative scientific domains whose convergence is accelerating the development of next-generation nanomaterials with enhanced functionality, precision, and sustainability. AI-driven computational intelligence enables rapid material discovery, predictive modelling, autonomous experimentation, and process optimisation, thereby significantly reducing the time, cost, and complexity associated with conventional nanomaterial research. Simultaneously, advances in nanotechnology have expanded the possibilities for engineering materials with exceptional electrical, optical, mechanical, catalytic, and biomedical properties. The integration of machine learning, deep learning, computer vision, and data-driven optimisation with nanoscale material design has opened new avenues for applications in healthcare, energy storage, environmental remediation, electronics, aerospace, and smart manufacturing. Furthermore, intelligent digital platforms facilitate real-time quality assessment, defect prediction, and performance optimisation across the nanomaterial life cycle. Despite remarkable progress, challenges related to data quality, model interpretability, scalability, standardisation, and ethical deployment remain significant barriers to widespread industrial implementation. This paper presents a comprehensive academic investigation into the synergistic relationship between AI and nanotechnology, highlighting recent innovations, emerging methodologies, application domains, current limitations, and future research opportunities that are expected to shape intelligent nanomaterial development for sustainable scientific and industrial advancement.
Amjid Nadeem, Brajesh Kumar Mishra, J. Raja et al.· International journal of com...· 0 citations
Nanotechnology and Artificial Intelligence (AI) have separately been
revolutionizing the world of scientific and industrial innovation. The combination of both has
resulted in fast material discovery, improved diagnostic abilities, intelligent therapeutic systems, and advanced computational systems. However, the current literature has presented a gap
in structured evaluations of the impact of AI methodologies on nanoscale research and the role
of nanoscale materials in the development of next-generation AI platforms
The structured review approach was adopted in this study to identify peer-reviewed
articles from prominent scientific databases (Scopus, Web of Science, IEEE Xplore, PubMed)
between 2015 and 2025. A pre-defined search strategy and inclusion-exclusion criteria were
used. The articles were screened to evaluate the comparison of AI models, nanomaterial applications, and performance trends.
The review shows that AI methods, such as Convolutional Neural Networks, Graph
Neural Networks, and Transformers, greatly improve nanoscale design, prediction, and characterization. AI-assisted nanotechnology advances the accuracy of simulations, minimizes the
number of experiments, and facilitates optimal drug delivery routes, biosensing, and material
properties prediction. On the other hand, nanoscale materials advance energy-efficient AI
hardware, neuromorphic computing, and high-performance computing infrastructure.
The results clearly show that there is a mutual technological front where AI advances nanotechnology and nanoscale materials advance energy-efficient AI hardware. The
current limitations include a lack of data, a lack of interpretability of AI models, a lack of
benchmarking standards, and the need for ethical and regulatory frameworks.
The field of AI and nanotechnology is a rapidly growing interdisciplinary area
with great potential for scientific and societal impact.
Arsheen Kaur, Malti Rani, Himali Sarangal et al.· Recent Advances in Computer...· 0 citations
Artificial intelligence (AI) is rapidly reshaping pharmaceutics and drug delivery, offering computational alternatives to traditional trial-and-error approaches in formulation design, nanocarrier engineering, and manufacturing. This review examines the current applications of machine learning, deep learning, reinforcement learning, and generative AI architectures across the pharmaceutical product lifecycle, from target identification and lead optimization to dosage form development and clinical translation. Particular attention is given to AI-driven prediction of excipient compatibility, formulation stability, and bioavailability, as well as the design of smart nanocarriers, stimuli-responsive delivery systems, and 3D-printed dosage forms enabled by graph neural networks, transformer-based models, and Bayesian optimization techniques. The review also traces the historical evolution of computational pharmaceutics, from early rule-based prediction systems to current high-throughput, automation-integrated platforms, and highlights emerging tools such as digital twins, federated learning frameworks, and AI-supported nanorobotics for targeted intracellular delivery. Despite these advances, significant challenges persist, including limited data availability and standardization, algorithmic opacity and the demand for explainable AI, and the absence of harmonized regulatory frameworks for validating AI-driven decision-making in drug development. The review further considers data quality and interpretability concerns specific to nanomedicine, alongside the regulatory and ethical considerations necessary for clinical adoption. Future directions emphasize the integration of multi-omics data, real-time adaptive drug delivery systems, and quantum-AI convergence to advance personalized medicine. Addressing these barriers through interdisciplinary collaboration, standardized data repositories, and transparent regulatory guidance will be essential to translating AI-enabled pharmaceutical innovations from computational design into safe, effective clinical therapeutics.
Keywords: Artificial intelligence; machine learning; drug delivery; nanomedicine; formulation design; personalized medicine; deep learning; pharmaceutics
Ritika Sharma, Harmeet Singh· Journal of Drug Delivery and...· 0 citations
ABSTRACT Cancer nanomedicine offers a versatile platform for improving therapeutic index, but its clinical translation remains limited by unpredictable in vivo behavior, heterogeneous biological contexts, and inefficient design paradigms. Artificial intelligence (AI) is emerging as an integrative framework that links data‐driven modeling with nanomedicine design, biological transport, and clinical decision‐making. In this review, we discuss AI‐guided strategies for material design, targeting, payload optimization, and in vivo delivery, with particular attention to protein corona‐mediated biological identity, microenvironment‐responsive activation, and biodistribution modeling. We further examine the translational requirements for AI‐enabled nanomedicine, including data standardization, preclinical learning workflows, clinical stratification and risk‐based governance. Finally, we outline future directions centered on transferable learning architectures, dynamic multiscale modeling and patient‐aware therapeutic strategies. Together, these advances suggest that AI can move cancer nanomedicine beyond empirical formulation toward a more predictive, biologically informed, and clinically responsive discipline.
Shengbin Liu, Zhi Liu, Haixing Shi et al.· MedComm· 0 citations
The integration of nanotechnology, biotechnology, and artificial intelligence (AI) represents a transformative interdisciplinary approach for advancing biomedical and environmental applications. This study adopts a PRISMA-guided systematic literature review combined with conceptual framework development to synthesize current evidence on the convergence of these three technological domains and to propose an integrated Nano–Bio–AI framework. Relevant peer-reviewed literature was systematically identified, screened, and synthesized to examine the complementary roles of nanoscale engineering, biological system manipulation, and computational intelligence in addressing contemporary healthcare and environmental challenges.
The synthesized evidence indicates that nanotechnology enhances targeted drug delivery, diagnostic sensitivity, and controlled therapeutic release through engineered nanoscale materials. Biotechnology contributes bio-responsive systems, genetic engineering, and molecular manipulation techniques that enable precise biological interactions and adaptive therapeutic responses. Artificial intelligence complements these capabilities by applying machine learning and predictive analytics to large-scale biomedical and environmental datasets, thereby accelerating drug discovery, improving disease prediction, optimizing molecular interactions, and supporting evidence-based decision-making.
The proposed Nano–Bio–AI framework demonstrates how the synergistic integration of these technologies can support precision medicine through patient-specific therapeutic design and intelligent environmental management through pollutant detection, biodegradation, and ecosystem monitoring. The review also identifies key implementation challenges, including heterogeneous data integration, nanotoxicity, AI ethics, biosafety, and regulatory governance. Rather than providing empirical validation, the study offers a conceptually grounded framework derived from systematic evidence synthesis to guide future computational, experimental, and clinical research. Overall, the Nano–Bio–AI framework provides a comprehensive foundation for next-generation biomedical innovation and sustainable environmental technologies.
David Sunday Araoti· Journal of Artificial Intell...· 0 citations
Background
Nanobiotechnology integrates nanoscale materials with biological systems, enabling significant advances in targeted drug delivery, biosensing, molecular diagnostics, regenerative medicine, and environmental monitoring. Despite these advances, the complexity of nano–bio interactions and the multidimensional design space of nanomaterials present substantial challenges to conventional experimental approaches. Artificial intelligence (AI), particularly machine learning and deep learning, has emerged as a powerful tool for modelling, predicting, and optimising nano–bio systems, thereby accelerating innovation and improving decision-making across biomedical and environmental applications.
Objective
This review provides a comprehensive overview of AI-integrated nanobiotechnology, with particular emphasis on AI-assisted nanomaterial design, precision medicine, smart diagnostic technologies, cancer therapeutics, environmental and agricultural applications, and food safety. It also critically examines current ethical, biosafety, regulatory, and commercialisation challenges while identifying emerging research directions.
Methods
I conducted a comprehensive review of peer-reviewed literature published between 2019 and 2026 on the application of artificial intelligence in nanobiotechnology. The review synthesises evidence relating to AI-driven nanomaterial optimisation, intelligent drug delivery systems, nano-biosensors, precision medicine, environmental monitoring, and regulatory developments. Where appropriate, supplementary questionnaire findings are incorporated to provide additional insights into stakeholder perceptions of AI-assisted nanobiotechnology.
Results
The reviewed literature demonstrates that AI has substantially improved the prediction of nano–bio interactions, accelerated nanomaterial design and optimisation, enhanced the performance of intelligent drug delivery systems, and strengthened the analytical capabilities of nano-enabled diagnostic platforms. AI has also expanded opportunities for environmental monitoring, agricultural nanobiotechnology, and pollutant detection through intelligent sensing and predictive modelling. Despite these advances, challenges related to data quality, model interpretability, biosafety assessment, regulatory harmonisation, and ethical governance continue to limit large-scale clinical and industrial implementation.
Conclusion
AI-integrated nanobiotechnology represents a rapidly evolving multidisciplinary field with considerable potential to transform precision medicine, smart diagnostics, environmental sustainability, and advanced healthcare. Continued progress will depend on high-quality data generation, explainable AI models, robust biosafety validation, and internationally harmonised regulatory frameworks that promote safe, transparent, and responsible innovation.
David Sunday Araoti· Journal of Artificial Intell...· 0 citations
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