Nanomedicine represents a transformative frontier in therapeutic innovation, driven by the precision engineering of nanoparticles for advanced drug delivery and disease management. Predicting drug efficacy and toxicity remains a critical challenge in accelerating the translation of nanomedicines from laboratory research to clinical application. Machine learning offers powerful capabilities in extracting meaningful insights from high-dimensional biological datasets, optimizing nanoparticle design, and forecasting clinical outcomes with improved reliability. This book chapter explores advanced machine learning methodologies including supervised, unsupervised, and hybrid AI-driven frameworks that enable robust predictive modeling for both therapeutic effectiveness and nano-toxicity risk assessment. Emphasis is placed on multi-omics integration, interpretable AI, and real-world case studies that demonstrate computational models identifying nanoparticle–biological interactions, pharmacokinetics, biodistribution, and cellular responses. The discussion highlights existing gaps in safety profiling, model generalizability, and regulatory acceptance, while presenting future directions for adaptive, clinically aligned decision-support systems. Strengthening predictive confidence through data-driven intelligence contributes significantly to safer and more effective nanomedicine development. This chapter serves as a comprehensive reference for researchers seeking to implement machine learning strategies that advance precision nanotherapeutics and support the rapid evolution of next-generation medical technologies.
Nanomedicine has emerged as a transformative field within modern healthcare, offering innovative solutions for targeted drug delivery, diagnostic imaging, and treatment of a variety of diseases [1]. At its core, nanomedicine involves the application of nanotechnology to the development of therapeutic and diagnostic agents, utilizing nanoparticles to improve drug delivery systems [2]. These nanoparticles, with their unique physicochemical properties such as size, surface area, and charge, can interact with biological systems in ways that conventional drugs cannot [3]. As a result, nanomedicine offers the potential for more efficient drug delivery, enhanced bioavailability, reduced side effects, and improved patient outcomes [4]. The combination of nanotechnology and medicine presents a promising future for personalized treatment regimens, allowing for tailored therapeutic strategies based on an individual’s unique biological characteristics [5].
While the potential of nanomedicine is vast, the development of safe and effective nanotherapeutics is not without its challenges [6]. One of the primary obstacles is the inability to accurately predict the efficacy and toxicity of nanoparticles before they enter clinical trials [7]. Traditional preclinical models, although valuable, often fail to fully simulate the complexity of human biological systems, leading to a high failure rate in clinical trials [8]. Nanoparticles, due to their unique properties, can behave unpredictably when interacting with biological systems. For instance, factors such as surface charge, shape, and functionalization can significantly influence how nanoparticles are taken up by cells, how they distribute within the body, and how they affect cellular processes [9]. These complexities necessitate the use of advanced computational techniques to predict how nanoparticles will behave in vivo and to ensure their safety and effectiveness in humans [10].
Machine learning (ML) has emerged as a powerful tool to address these challenges in nanomedicine [11]. ML algorithms can analyze large, complex datasets and extract patterns from them, allowing researchers to predict the behavior of nanoparticles in biological environments [12]. Unlike traditional methods, ML models can process vast amounts of data from multiple sources, including genomic, proteomic, and transcriptomic data, to generate predictive insights [13]. These models can be trained to predict not only the efficacy of nanoparticles in delivering therapeutic agents but also their potential toxicity, enabling researchers to identify harmful side effects before they occur in clinical trials [14]. By integrating data from various biological levels, ML models offer a comprehensive understanding of how nanoparticles interact with cells, tissues, and organs, leading to more reliable predictions and safer nanomedicine development [15].
