This book explores how machine learning is transforming nanomedicine, with a focus on the rational design of lipid nanoparticles (LNPs) for mRNA-based therapies. Moving beyond traditional, labor-intensive workflows, it highlights AI-driven methods—such as supervised learning, data augmentation, and deep learning—for predictive modeling and in silico screening.



Key topics include chemoinformatics, molecular fingerprinting, and strategies to optimize LNP transfection efficiency and biocompatibility. Real-world applications, including mRNA vaccines and personalized nanomedicines, illustrate the convergence of computational biology and pharmaceutical engineering. It also addresses the ethical considerations and regulatory challenges surrounding AI-driven drug development. This book is intended for researchers, pharmaceutical scientists, computational biologists, and professionals in the biotechnology industry who seek to leverage AI-driven methodologies in nanomedicine development.