AI-Driven Design of Quantum Dots for Drug Delivery and Pharmacological Applications: A Comprehensive Review

Quantum dots (QDs) are tiny semiconductor particles with unique light and electronic properties that can be adjusted by changing their size. They are widely usedin drug delivery, bioimaging, and theranostic applications. However, designing the best QDs is difficult because there are many possible combinations, makingtraditional trial-and-error methods slow and inefficient. Artificial intelligence (AI) and machine learning (ML) have improved this process by helping scientistspredict properties, design better QDs, and automate experiments. This review explains how various AI methods, including supervised learning, graph neuralnetworks, generative models, Bayesian optimisation, and active learning, are applied to QD-based drug delivery. These approaches have helped improve QDsynthesis, control drug release, and target specific areas such as tumours and the brain. AI has also supported applications in cancer treatment, neurological diseases,infections, and gene delivery. Despite these benefits, there are still challenges, such as a lack of reliable data, difficulty applying models to real-world conditions,and a limited understanding of how AI models make decisions. New technologies such as self-driving labs, advanced AI models, and quantum computing areexpected to further advance this field. Overall, combining AI with nanotechnology is making drug delivery faster, smarter, and more precise.