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{ "item_title" : "Machine Learning-Driven Rational Design in Nanomedicine", "item_author" : [" Krish W. Ramadurai", "Abhirup Banerjee "], "item_description" : "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.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/3/03/204/011/3032040116_b.jpg", "price_data" : { "retail_price" : "54.99", "online_price" : "54.99", "our_price" : "54.99", "club_price" : "54.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Machine Learning-Driven Rational Design in Nanomedicine|Krish W. Ramadurai

Machine Learning-Driven Rational Design in Nanomedicine : Advances in Computational Drug Delivery and in Silico Screening

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Overview

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.

This item is Non-Returnable

Details

  • ISBN-13: 9783032040114
  • ISBN-10: 3032040116
  • Publisher: Springer
  • Publish Date: January 2026
  • Dimensions: 9.21 x 6.14 x 0.18 inches
  • Shipping Weight: 0.3 pounds
  • Page Count: 73

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