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Book synopsis

Reactive Publishing

Explore the intersection of reinforcement learning and computational drug discovery in this practical guide for researchers, data scientists, and computational chemists.

This book provides a hands-on introduction to applying reinforcement learning techniques in Python to accelerate molecular design and optimization. Readers will learn core algorithms including policy gradients, Q-learning, actor-critic methods, and deep RL agents, with clear explanations of how they are adapted for chemistry-specific challenges.

Key topics include:
• De novo molecule generation using RL frameworks
• Lead optimization and multi-objective property design
• Implementation of RL agents for molecular simulation and evaluation
• Practical Python code examples and case studies drawn from real-world drug discovery scenarios

Whether you are new to reinforcement learning or an experienced practitioner looking to apply these tools to pharmaceutical research, this book offers the foundational knowledge and code resources needed to build effective RL solutions for molecular optimization.

Perfect for professionals and students working at the cutting edge of AI-driven drug discovery.

Book details

• Full title: Reinforcement Learning for Drug Discovery and Molecular Optimization with Python
• Author: Takehiro Kanegi, Livia Arden, Alice Schwartz
• Language: English
• Binding: Paperback
• Number of pages: 466
• EAN: 9798181338371
• ID: 52857580
• Publisher: Independently published
• Weight: 560 g
• Dimensions: 229 × 152 × 29 mm
• Date of publishing: 12. June 2026

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