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@article{199928,
        author = {K. Hamsika Sri and V. Aravinda Krishna and A. Kuraloviya and Edapalapati Venkata Naga Nithin and Shreeya Rajesh Tumme and Pragyan Kumar Mishra and Amaan Ahmad},
        title = {INTEGRATION OF ARTIFICIAL INTELLIGENCE IN NANOPARTICLE-MEDIATED DRUG DELIVERY: DESIGN, STRATEGY, PREDICTIVE MODELING AND THERAPEUTIC OUTCOMES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {785-799},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199928},
        abstract = {Nanoparticle-mediated drug delivery systems (NDDS) have emerged as transformative platforms for targeted therapeutics. However, conventional empirical approaches to nanoparticle formulation are resource-intensive and often suboptimal. Artificial intelligence (AI) and machine learning (ML) offer unprecedented opportunities to accelerate and improve the design, optimization, and clinical translation of NDDS. This comprehensive review synthesizes current literature on the integration of AI methodologies including deep learning, graph neural networks, reinforcement learning, and generative adversarial networks into nanoparticle drug delivery research, with emphasis on design strategies, predictive modeling frameworks, and therapeutic outcome improvements A systematic literature search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases (2010–2024). Studies reporting AI-assisted nanoparticle design, formulation optimization, toxicity prediction, drug release modeling, or clinical outcome AI-integrated approaches demonstrated 25–40% improvements in encapsulation efficiency, 33–65% enhancements in tumor targeting specificity, and significant reductions in formulation development timelines (from years to months). Deep learning models achieved >90% accuracy in predicting drug release kinetics, while GNN-based approaches showed superior performance. AI integration into nanoparticle drug delivery represents a paradigm shift from empirical trial-and-error to data-driven precision nanomedicine. Continued development of interpretable AI models, standardized datasets, and regulatory frameworks will be essential for clinical translation.},
        keywords = {artificial intelligence; nanoparticle; drug delivery; machine learning; deep learning; QSAR; predictive modeling; nanomedicine; therapeutic optimization;},
        month = {May},
        }