Applied Artificial Intelligence for Drug Discovery: The 2026 Springer Reference
Description
Generative AI has moved well past hype in pharmaceutical R&D: predictive models now shape target identification, molecule design, and even clinical trial protocols. Applied Artificial Intelligence for Drug Discovery, edited by Antonio Lavecchia (Springer, January 2026), is the reference that captures this shift for professionals who need the science, not the sales pitch — and it complements a growing wave of clinical literature this year, from AI's new chapter in the GOLD pulmonology report to its unglamorous-but-critical early roles in oncology R&D covered at AACR 2026.
Why buy this book?
For pharmaceutical researchers, academic medical libraries, and drug discovery teams, this volume bridges computational method and pharmacological application — essential for anyone evaluating AI tools against real R&D pipelines rather than marketing claims.
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Q&A
What areas of drug discovery does this book cover?
Target identification, molecule design and optimization, predictive toxicology, and AI-assisted clinical trial design, among other stages of the pharmaceutical R&D pipeline.
Who is the editor?
Antonio Lavecchia, published by Springer Cham in January 2026 — the most current reference available on this topic.
Is this book aimed at researchers or clinicians?
Primarily pharmaceutical researchers and drug discovery scientists, though the AI-methodology chapters are also relevant to clinicians tracking AI adoption in medicine more broadly.
How current is the research in this volume?
Published January 2026, it reflects the latest generation of generative and predictive AI models applied to pharmaceutical R&D, replacing earlier editions that predate the generative AI wave.
Where can I buy Applied Artificial Intelligence for Drug Discovery?
Directly from CLNZ Books, with worldwide shipping and secure card or PayPal checkout — view the book here.