Data Science, AI, and Machine Learning in Drug Development

Data Science, AI, and Machine Learning in Drug Development offers a forward-looking perspective on how emerging technologies are transforming pharmaceutical research and innovation. This comprehensive book bridges the gap between data science techniques and real-world drug development applications, making it a vital resource for professionals across pharma, biotech, and healthcare.

Covering key topics such as predictive modeling, biomarker discovery, clinical trial optimization, and regulatory considerations, the book provides both foundational knowledge and advanced insights. Real case studies and practical examples help readers understand how to apply AI and machine learning tools effectively across the entire drug development pipeline.

Ideal for pharmaceutical scientists, data scientists, clinical researchers, and bioinformaticians, this book equips readers with the skills to accelerate research timelines, reduce trial costs, and improve therapeutic outcomes through data-driven strategies. With the pharmaceutical industry rapidly evolving, this guide is a crucial reference for staying ahead in digital drug development.