Revolutionizing Drug Development
Harnessing AI and Computational Biology
Will be published approx. on 1. January 2026
Book
Paperback/Softback
230 pages
978-0-443-34059-8 (ISBN)
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.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Dimensions
Height: 276 mm
Width: 216 mm
Thickness: 21 mm
Weight
980 gr
ISBN-13
978-0-443-34059-8 (9780443340598)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
02/2026
Elsevier
€162.99
Available for download
Person
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 techniques, 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 diverse topics including drug discovery, herbal medicine, medicinal biotechnology, nanotechnology, bioengineering, plant functional genomics, plant 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' and also received the Springer Nature Editor of Distinction Award in 2025.
Content
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