Revolutionizing Drug Development
Harnessing AI and Computational Biology
- 1st Edition - January 21, 2026
- Latest edition
- Editor: Jen-Tsung Chen
- Language: English
Revolutionizing Drug Development: Harnessing AI and Computational Biology presents cutting-edge artificial intelligence (AI) tools, such as machine- and deep-learning models and ge… Read more
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Description
Description
Revolutionizing Drug Development: Harnessing AI and Computational Biology presents cutting-edge artificial intelligence (AI) tools, such as machine- and deep-learning models and generative AI, to assist structure-based drug design and clinical trial design and integrate with drug development programs. This book summarizes technical advancements of AI-based technologies and computational biology approaches, highlighting their applications in developing new drugs through discovery, repurposing, and designing, for advancing R&D in the pharmaceutical industry and benefiting precision medicine. This book serves as an ideal reference for students, teachers, professors, and researchers in biological and biomedical sciences, particularly the topics related to bioinformatics, systems biology, pharmacology, and drug development. The readers can efficiently and precisely overview this burning field, which might inspire their future directions of research in drug development and AI-based digital biology.
Key features
Key features
- Provides cutting-edge AI technologies in drug development
- Presents ways to utilize AI models for drug discovery, repurposing, and design
- Summarizes current achievements of AI models for accelerating drug development programs
Readership
Readership
Students, teachers, professors, researchers, and experts in the fields of bioinformatics, computational biology, systems biology, drug discovery, and drug development
Table of contents
Table of contents
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 drug repurposing: Methods and applications
4. Data science and databases in drug discovery: Technical development and applications
5. Graph neural networks for drug discovery: Protocols and applications
6. Deep learning and generative models for drug discovery: Techniques and current achievements
7. AI-enabled personalized medicine: Strategies and challenges
8. AI technologies for advancing R&D in the pharmaceutical industry: Current development and challenges
10. AI-powered drug development for treating neurological disorders
11. AI applications in drug discovery for promoting longevity
12. Transforming Drug Development with AI-driven Models
13. AI technologies for precision and personalized medicine
14. AI technologies for smart pharmacology
15. AI-powered Immunopharmacological strategies to combat inflammatory diseases
16. AI and nanotechnology for advancing drug development
17. Drug repositioning using tensor decomposition
18. Integrating Artificial Intelligence and machine learning technologies in antiviral drug discovery: Strategies and challenges
19. Artificial intelligence in the discovery of new antibiotics
20. AI-designed drugs: Clinical development and future directions
2. AI strategies for drug discovery and bioactivity prediction: Opportunities and challenges
3. AI technologies for drug repurposing: Methods and applications
4. Data science and databases in drug discovery: Technical development and applications
5. Graph neural networks for drug discovery: Protocols and applications
6. Deep learning and generative models for drug discovery: Techniques and current achievements
7. AI-enabled personalized medicine: Strategies and challenges
8. AI technologies for advancing R&D in the pharmaceutical industry: Current development and challenges
10. AI-powered drug development for treating neurological disorders
11. AI applications in drug discovery for promoting longevity
12. Transforming Drug Development with AI-driven Models
13. AI technologies for precision and personalized medicine
14. AI technologies for smart pharmacology
15. AI-powered Immunopharmacological strategies to combat inflammatory diseases
16. AI and nanotechnology for advancing drug development
17. Drug repositioning using tensor decomposition
18. Integrating Artificial Intelligence and machine learning technologies in antiviral drug discovery: Strategies and challenges
19. Artificial intelligence in the discovery of new antibiotics
20. AI-designed drugs: Clinical development and future directions
Product details
Product details
- Edition: 1
- Latest edition
- Published: February 1, 2026
- Language: English
About the editor
About the editor
JC
Jen-Tsung Chen
Jen-Tsung Chen is a Professor of Cell Biology at the National University of Kaohsiung in Taiwan, where he teaches cell biology, genomics, proteomics, plant physiology, and plant biotechnology. His research spans bioactive compounds, chromatography techniques, plant molecular biology, bioinformatics, systems pharmacology, and broader themes in biotechnological plant disease management, plant biotic stress responses, nanotechnology for combating pests and pathogens, ethnopharmacology, and systems biology. An active scholar, Dr. Chen serves on the editorial boards of several international journals and has guest‑edited numerous special issues. He has also authored and edited books with major international publishers on topics including drug discovery, herbal medicine, medicinal biotechnology, nanotechnology, bioengineering, plant functional genomics, plant speed breeding, CRISPR‑based genome editing, and artificial intelligence. Recognized for his scientific impact and editorial leadership, Dr. Chen was listed among Elsevier and Stanford University’s “World’s Top 2% Scientists” in 2023 and 2024 and received the Springer Nature Editor of Distinction Award in 2025.
Affiliations and expertise
Professor, Department of Life Sciences, National University of Kaohsiung, Kaohsiung, Nanzih District, TaiwanView book on ScienceDirect
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