Published Paper Details:

Artificial Intelligence (AI) is revolutionizing the landscape of drug delivery by offering innovative solutions to overcome the limitations of traditional methods. The integration of AI into drug delivery systems presents unprecedented opportunities to enhance the precision, efficiency, and personalization of therapeutic interventions. Machine learning (ML) and deep learning (DL) algorithms are at the forefront of this transformation, optimizing drug formulation, predicting drug-target interactions, and designing advanced delivery vehicles. These intelligent systems process vast datasets--including molecular properties, patient-specific factors, and clinical outcomes--to facilitate the development of targeted therapies with fewer adverse effects. A key application of AI in drug delivery is the design of nanocarriers. AI-driven techniques enable rapid screening and optimization of nanoparticle characteristics such as size, shape, and surface properties, essential for achieving optimal bioavailability and targeted delivery. Furthermore, AI-powered simulations and modeling predict the pharmacokinetics and pharmacodynamics of drug-nanocarrier complexes, accelerating preclinical evaluations. In personalized medicine, AI tailors drug delivery systems to individual patient profiles by integrating genetic, lifestyle, and environmental data. This approach allows for the creation of personalized drug regimens that maximize therapeutic efficacy while minimizing toxicity. AI-powered biosensors and wearable devices are also being developed to monitor real-time drug release and patient responses, enabling dynamic dose adjustments and improving treatment outcomes. Despite these advancements, challenges such as data privacy concerns, regulatory hurdles, and the need for interdisciplinary collaboration must be addressed to ensure the successful translation of AI-driven drug delivery systems from research to clinical practice. Continued research and development, alongside evolving regulatory frameworks, are crucial for fully realizing AI's transformative potential in drug delivery.