Revolutionizing Drug Development: Harnessing AI and Computational Biology presents cutting-edge Artificial Intelligence tools, particularly machine and deep learning models, and generative AI, to assist structure-based drug design, clinical trial design and integrate with drug development programs. This book summarizes technical advancements of artificial intelligence (AI)-based technologies and computational biology approaches, highlighting their applications in developing new drugs through discovery, re-purposing, and designing, for advancing R&D in the pharmaceutical industry and benefiting precision medicine. It presents cutting-edge AI tools, particularly machine and deep learning models, and generative AI, to assist structure-based drug design, and clinical trial design and integrate with drug development programs. The readers of this book can efficiently and precisely overview this burning field which might inspire their future directions of research in drug development and AI-based digital biology. Revolutionizing Drug Development: Harnessing AI and Computational Biology presents knowledge of drug development using edge tools of computational biology and AI models and thus can be an ideal reference for students, teachers, professors, and researchers in biological science particularly, the topics related to bioinformatics, systems biology, and drug development.
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Dr. Jen-Tsung Chen is a Professor of Cell Biology at the National University of Kaohsiung in Taiwan. He teaches cell biology, genomics, proteomics, plant physiology, and plant biotechnology. His research spans bioactive compounds, chromatography, plant molecular biology and biotechnology, bioinformatics, and systems pharmacology. An active book and journal editor, he serves on editorial boards and has guest-edited special issues for several international journals. Dr. Chen has authored and edited books with international publishers on topics including drug discovery, herbal medicine, medicinal biotechnology, nanotechnology, bioengineering, plant functional genomics, speed breeding, CRISPR-based plant genome editing, and artificial intelligence. In 2023 and 2024, he was listed by Elsevier and Stanford University among the "World's Top 2% Scientists."
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