Artificial Intelligence in Drug Discovery

AI Meets the Drug Discovery Pipeline

Artificial Intelligence in Drug Discovery brings together the tools, methods, and practical questions shaping one of the most active areas in modern chemical research. Edited by Nathan Brown, this volume focuses on how artificial intelligence and machine learning are being used to tackle drug discovery problems that have often been difficult to solve: predicting properties, designing molecules, planning syntheses, and supporting simulation work.

The result is a book that balances overview and application. It is broad enough to orient readers who are new to the subject, yet substantial enough to remain useful to researchers who want to understand how AI methods are being applied in real discovery settings.

What the Book Covers

• AI and machine learning tools used in drug discovery
• Design of new molecular structures
• Synthesis planning and related strategy questions
• Simulation and predictive workflows
• Current challenges and opportunities in the field

Who It Will Suit

This is a strong fit for students, postgraduates, and established researchers in academia or industry who want a serious, chemistry-focused view of AI in drug discovery. It sits comfortably in the space between foundational reading and specialist reference.

A Well-Positioned Reference for a Fast-Moving Field

Part of the Drug Discovery Series, this volume reflects the pace of change in AI-enabled research while keeping the emphasis on practical scientific use. It is a thoughtful choice for readers who want a structured look at what AI can already do in drug discovery—and where the field is still developing.