Integrating Machine Learning with Mesoscale Simulation for Engineering the Synthesis of Amphiphilic Polymeric Nanoparticles: A New Paradigm for Drug Loading, Stability, and Controlled Release

Mesoscale simulations and machine learning are converging to reshape the design of amphiphilic polymeric nanoparticles for drug delivery. Dissipative particle dynamics and coarse-grained molecular dynamics generate virtual libraries of self-assembled structures, while graph neural networks, variational autoencoders, and physics-informed networks turn that data into predictive and generative tools. This review presents an integrated framework in which simulation-trained surrogates accelerate forward prediction, generative models propose synthesizable polymer structures from target properties, and active learning efficiently explores vast formulation spaces. Critical barriers-the disconnect between polymerization kinetics and self-assembly, scarcity of systematic experimental data, model interpretability, and the challenge of biological complexity are examined. A roadmap toward digital twins of the entire synthesis-to-release chain and self-driving laboratories is outlined, indicating that the fusion of machine learning and mesoscale simulation is no longer optional but a prerequisite for the next generation of nanocarrier engineering.

کلیدواژه ها:

mesoscale simulation ، machine learning ، amphiphilic polymeric nanoparticles ، self-assembly ، drug loading ، dissipative particle dynamics ، inverse design