Data Science, AI, and Machine Learning in Drug Development 1st Edition

Capture the future of pharmaceutical R&D with Data Science, AI, and Machine Learning in Drug Development, 1st Edition by Harry Yang. This compelling guide pulls together cutting-edge techniques and real-world perspectives to show how data-driven methods are reshaping discovery, preclinical research, and clinical trials across the global life sciences sector.

Explore clear, practical explanations of AI architectures, machine learning pipelines, and statistical modeling tailored to drug development challenges — from target identification and biomarker discovery to trial design and regulatory decision support. Yang balances technical rigor with accessible writing, making complex concepts useful for data scientists, biostatisticians, clinical researchers, and pharma leaders alike. Case studies and applied examples emphasize reproducible workflows, interpretability, and ethical considerations that matter in regulated environments.

Imagine accelerating timelines, improving patient stratification, and making smarter go/no-go decisions: this book equips teams across North America, Europe, Asia-Pacific, and emerging markets with the frameworks and tools needed to translate data into actionable insights. Whether you’re building AI-enabled pipelines, advising on regulatory submissions, or upskilling your research group, the strategies here help you deliver measurable impact.

Engaging, authoritative, and forward-looking, Harry Yang’s 1st Edition is an essential reference for anyone committed to harnessing data science and AI to transform drug development. Add it to your professional library today and start turning complex data into better medicines for patients worldwide.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.