Revolutionizing Drug Development
Harnessing AI and Computational Biology
- 1st Edition - January 1, 2026
- Imprint: Academic Press
- Editor: Jen-Tsung Chen
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 4 0 5 9 - 8
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 4 0 6 0 - 4
Revolutionizing Drug Development: Harnessing AI and Computational Biology presents cutting-edge Artificial Intelligence tools, particularly machine and deep learning models, and ge… Read more
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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.
- Provides cutting edge Artificial Intelligence technologies in drug development
- Presents ways to utilize Artificial Intelligence models for drug discovery, re-purposing, and design
- Summarizes current achievements of Artificial Intelligence models for accelerating drug development programs
Students, teachers, professors, researchers, and experts in the fields of bioinformatics, computational biology, systems biology, drug discovery, and drug development
1. Artificial intelligence and accelerated computing in drug discovery: An updated overview
2. AI strategies for drug discovery and bioactivity prediction: Opportunities and challenges
3. AI technologies for revealing new targets for drug discovery: Current findings and future directions
4. AI technologies for drug repurposing: Methods and applications
5. AI-designed drugs: Clinical development and future directions
6. AI-driven synthesis of drug-like compounds: Edge methods and current findings
7. Data science and databases in drug discovery: Technical development and applications
8. AI-based software for drug discovery: Tools and applications
9. Graph neural networks for drug discovery: Protocols and applications
10. Deep learning and generative models for drug discovery: Techniques and current achievements
11. AI technologies in structure-based drug design: technological advancements and limitations
12. AI-assisted clinical trial design for drug development: Strategies and challenges
13. AI-enabled personalized medicine: Strategies and challenges
14. Integrating AI technologies with drug development programs: Strategies and challenges
15. AI technologies for advancing R&D in the pharmaceutical industry: Current development and challenges
2. AI strategies for drug discovery and bioactivity prediction: Opportunities and challenges
3. AI technologies for revealing new targets for drug discovery: Current findings and future directions
4. AI technologies for drug repurposing: Methods and applications
5. AI-designed drugs: Clinical development and future directions
6. AI-driven synthesis of drug-like compounds: Edge methods and current findings
7. Data science and databases in drug discovery: Technical development and applications
8. AI-based software for drug discovery: Tools and applications
9. Graph neural networks for drug discovery: Protocols and applications
10. Deep learning and generative models for drug discovery: Techniques and current achievements
11. AI technologies in structure-based drug design: technological advancements and limitations
12. AI-assisted clinical trial design for drug development: Strategies and challenges
13. AI-enabled personalized medicine: Strategies and challenges
14. Integrating AI technologies with drug development programs: Strategies and challenges
15. AI technologies for advancing R&D in the pharmaceutical industry: Current development and challenges
- Edition: 1
- Published: January 1, 2026
- Imprint: Academic Press
- Language: English
JC
Jen-Tsung Chen
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. Dr. Chen’s research interests include bioactive compounds, chromatography techniques, plant molecular biology, plant biotechnology, bioinformatics, and systems pharmacology. He is an active editor of academic books and journals to advance the exploration of multidisciplinary knowledge involving plant physiology, plant biotechnology, nanotechnology, ethnopharmacology, systems biology, and drug discovery. He serves as an editorial board member and a guest editor in several reputed journals. Dr. Chen published books in collaboration with international publishers on diverse topics such as drug discovery, herbal medicine, medicinal biotechnology, nanotechnology, bioengineering, plant functional genomics, plant speed breeding, CRISPR-based genome editing, and artificial intelligence. In 2023 and 2024, Elsevier and Stanford University recognized Dr. Chen as one of the “World’s Top 2% Scientists”.
Affiliations and expertise
Professor of Cell Biology, National University of Kaohsiung, Kaohsiung, Taiwan