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 methodssuch as supervised learning, data augmentation, and deep learningfor 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.

Details a practical ML pipeline for mRNA drug deliveryfrom data prep to model optimization Contains real-world case studies using advanced ML like Random Forest and XGBoost Includes ethical, regulatory, and translational insights for responsible innovation

Autorentext

Krish W. Ramadurai is a DPhil student in the Department of Engineering Science at the University of Oxford, a member of St. Hilda's College, and a member of the Multimodal Medical Data Integration & Analysis (MultiMeDIA) Lab at the Institute of Biomedical Engineering (IBME). His research focuses on developing multi-modal AI frameworks for drug development, leveraging hybrid mechanistic-AI models and functional modeling approaches to enhance the translatability and efficacy of next-generation therapeutics. Krish is also a Partner at AIX Ventures, where he oversees technical diligence, deal sourcing, and portfolio operations across the firm's artificial intelligence, healthcare, and life sciences practices. He has led and managed over 45 early- and growth-stage investments, generating a cumulative portfolio enterprise value exceeding $20 billion across multiple top-decile performing funds. Krish has directly supported over 25 pioneering scientific advancements, including the world's first AI-designed drug to enter human clinical trials and the first therapeutic discovered using a 3D-bioprinted tissue model. Additionally, he is a Harvard- and Oxford-trained scientist and biomolecular engineer, and a former researcher at Harvard University and MIT. At Harvard's Belfer Center for Science and International Affairs and the Taubman Center for State and Local Government, he collaborated closely with former United States Secretary of Defense, Dr. Ash Carter, and Nobel Laureate Economist Dr. Michael Kremer, notably contributing to USAID's Development Innovation Ventures Fund. Krish has authored several books on applied engineering and medicine featured by Barnes & Noble, the National Institutes of Health, and leading university libraries worldwide. Krish has served as chairman, director, and board member for over a dozen leading AI companies. He has advised numerous initiatives, including Nucleate, the Defense Innovation Unit (DIU), and the Defense Advanced Research Projects Agency (DARPA). His thought leadership has been featured in leading global media outlets, including The Wall Street Journal, Venture Capital Journal, Yahoo Finance, TechCrunch, Business Insider, Axios, the World Economic Forum, and Nikkei Asia.

Abhirup Banerjee is a Royal Society University Research Fellow and Principal Investigator at the Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford. His research lies at the intersection of cardiovascular science, artificial intelligence, and computational modelling, with a focus on digital twins, geometric machine learning, and multimodal data integration for cardiac diagnostics and interventions. He leads the Multimodal Medical Data Integration & Analysis (MultiMeDIA) Lab, where his team develops personalised, predictive models of cardiac anatomy and function using large-scale imaging and physiological datasets. A key aspect of his work involves reconstructing patient-specific 3D/4D cardiac structures from coronary angiography, cardiac MRI, and ECG. His AI-driven pipelines for coronary reconstruction, infarction modelling, and atrial fibrillation mapping are designed for real-time clinical use and have been patented in collaboration with Oxford University Innovation. Dr Banerjee's approach to cardiovascular science is rooted in the application of advanced computational methodologies, including variational autoencoders, point cloud networks, graph-based attention models, and statistical shape analysis. His work exemplifies a commitment to interdisciplinary innovation, translating cutting-edge algorithms into clinically meaningful tools. He has authored over 80 peer-reviewed publications and serves on the Editorial boards of several international journals. His research has been widely presented at leading scientific meetings, contributing to the advancement of data-driven approaches in cardiovascular medicine. He is also actively engaged in public outreach, regularly participating in science exhibitions, open days, and community engagement events to promote awareness of biomedical engineering and digital health.

Inhalt

An Introduction to mRNA Vaccines in Cancer Nanomedicine.- Machine-Learning Enhanced In Silico Screening: A Methodological Approach.- Supervised Machine Learning Implementation & Results.- Semi-Supervised Machine Learning Implementation & Results.- Discussion of Modeling Techniques, Practical Implications, and Prospective Developments.