Molecular Dynamics (MD) simulations have emerged as a vital tool in optimizing drug delivery for cancer therapy, offering detailed atomic-level insights into the interactions between drugs and their carriers. Unlike traditional experimental meth ods, which can be resource-intensive and time-consuming, MD simulations provide a more efficient and precise approach to studying drug encapsulation, stability, and release processes. These simulations are essential for designing effective drug carriers and gaining a deeper understanding of the molecular mechanisms that influence drug behavior in biological systems. Recent research has highlighted the broad applicability of MD simulations in assessing different drug delivery systems, such as functionalized carbon nanotubes (FCNTs), chitosan-based nanoparticles, metal-organic frameworks (MOFs), and human serum albumin (HSA). FCNTs are known for their high drug-loading capacity and stability, while biocompatible carriers like HSA and chitosan are favored for their biodegradability and reduced toxicity. Case studies involving anticancer drugs, including Doxorubicin (DOX), Gemcitabine (GEM), and Paclitaxel (PTX), showcase how MD simulations can improve drug solubility and optimize controlled release mechanisms. Although the computational complexity of these simulations presents challenges, advances in high-performance computing and machine learning techniques are driving significant progress. These innovations are facilitating the development of more targeted and efficient cancer therapies. By combining MD simulations with experimental validation, researchers are enhancing predictive models and accelerating the creation of next-generation drug delivery systems.
