Artificial Intelligence-Driven Drug Discovery

Principles, Methods, and Applications
Erscheinungsjahr 2026
ISBN: 978-981-9226-95-5
Verlag: Springer

Buch, Englisch, 400 Seiten, Format (B × H): 178 mm x 254 mm

Reihe: Advances in Experimental Medicine and Biology

This book provides a comprehensive overview of how cutting-edge artificial intelligence technologies are transforming modern drug design. It examines the impact of deep learning, generative modeling, and advanced protein language models across the full spectrum of therapeutic development, from small molecules discovery to peptide engineering and antibody design. Building on breakthroughs such as AlphaFold, the chapters illustrate how AI enables accelerated virtual screening, efficient ADMET prediction, and de novo molecular generation with improved specificity and affinity. In addition to introducing the core machine learning concepts underpinning these advances, the book highlights real world applications and emerging strategies that are reshaping the future of pharmaceutical innovation. It serves as an essential reference for graduate students, researchers and practitioners seeking to understand and leverage AI-driven methodologies in drug discovery.

Zielgruppe

Research

Autoren/Hrsg.

Zhang, Jian

Zheng, Shuangjia

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Pharmakologie, Toxikologie
• Naturwissenschaften Chemie Physikalische Chemie Quantenchemie, Theoretische Chemie
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Chapter 1. AI-Driven Drug Design: New Hopes and Challenges.- Chapter 2. AI-Driven Drug Design: A Brief History of Artificial Intelligence.- Chapter 3. AI-Driven Drug Design: Diffusion Models for Protein Design.- Chapter 4. AI-Driven Drug Design: Large Scale Language Models for Biomedical Research.- Chapter 5. AI-Driven Drug Design: Diffusion-Based Generative Models.- Chapter 6. AI-Driven Drug Design: Reinforcement Learning and Guided Generation.- Chapter 7. AI-driven Diverse Protein Conformational States Sampling and Drug Design.- Chapter 8. AI-Driven Drug Design:AI-Driven Prediction of Drug ADMET Properties.- Chapter 9. AI-Driven Drug Design: Deep Learning for Virtual Screening.- Chapter 10. AI-Driven Drug Design: Structure based de novo drug design through AI generative models.- Chapter 11. AI-Driven Drug Design: Protein Language Model and Application.- Chapter 12. AI-Driven Drug Design: De novo Protein Binder Design with Generative Models.- Chapter 13. AI-Driven Drug Design:Large-scale Pretrained Antibody Language Model.- Chapter 14. AI-Driven Drug Design: Antibody-Antigen Complex Structure Prediction.- Chapter 15. AI-Driven Drug Design: Genertive AI for Antibody Design.- Chapter 16. AI-Driven Drug Design: Peptide Design with Generative AI.- Chapter 17. AI-Driven Drug Design: Large-Scale Foundation Models and Applications for Life Science.- Chapter 18. AI-Driven Drug Design: Foundation Models for Drug Discovery.- Chapter 19. AI-Driven Drug Design: Future Trends in AI and Potential.

Über Autor(innen)

Jian Zhang is a Professor at the Shanghai Jiaotong University School of Medicine, China. His research interests include the development of AI algorithom on target identification and drug design, as well as first-in-class drug discovery. He has served on the associated editor of RSC Medicinal Chemistry since 2022 and on the editorial board of Medicinal Research Review and Aging Cell since 2025.

Shuangjia Zheng  is a Assistant Professor at the Shanghai Jiaotong University, China. His research focuses primarily on the intersection of generative artificial intelligence and drug design. He has been selected as a Shangsi Exploration Scholar, a recipient of the Asian Young Scientists Fund Project, a Forbes Asia 30 Under 30 list, and a member of the Shanghai Morning Light Program.