Revolutionizing Drug Development

Harnessing AI and Computational Biology

Häftad, Engelska, 2026

1 741 kr

Beställningsvara. Skickas inom 7-10 vardagar. Fri frakt över 249 kr.

Beskrivning

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.

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

Produktinformation

Utgivningsdatum:2026-01-21
Mått:216 x 276 x 21 mm
Vikt:980 g
Format:Häftad
Språk:Engelska
Antal sidor:354
Förlag:Elsevier Science
ISBN:9780443340598

Utforska kategorier

Kemi inom Naturvetenskap och teknik
Biovetenskap inom Naturvetenskap och teknik
Artificiell intelligens inom Data och IT

Mer om författaren

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.

Innehållsförteckning

Artificial intelligence and accelerated computing in drug discovery: An updated overview2. AI strategies for drug discovery and bioactivity prediction: Opportunities and challenges3. AI technologies for drug repurposing: Methods and applications4. Data science and databases in drug discovery: Technical development and applications5. Graph neural networks for drug discovery: Protocols and applications6. Deep learning and generative models for drug discovery: Techniques and current achievements7. AI-enabled personalized medicine: Strategies and challenges8. AI technologies for advancing R&D in the pharmaceutical industry: Current development and challenges10. AI-powered drug development for treating neurological disorders11. AI applications in drug discovery for promoting longevity12. Transforming Drug Development with AI-driven Models13. AI technologies for precision and personalized medicine14. AI technologies for smart pharmacology15. AI-powered Immunopharmacological strategies to combat inflammatory diseases16. AI and nanotechnology for advancing drug development17. Drug repositioning using tensor decomposition18. Integrating Artificial Intelligence and machine learning technologies in antiviral drug discovery: Strategies and challenges19. Artificial intelligence in the discovery of new antibiotics20. AI-designed drugs: Clinical development and future directions