Machine Learning-Driven Rational Design in Nanomedicine

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.

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