Reinforcement Learning for Drug Discovery and Molecular Optimization with Python
Language: English
Published by Independently Published Jun 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 46.88
Quantity: 2 available
Add to basketItem description from seller
Neuware - 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>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.…
Seller Inventory # 9798181338371
- Title
- Reinforcement Learning for Drug Discovery and Molecular Optimization with Python
- Author
- Takehiro Kanegi
- Publisher
- Independently Published Jun 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798181338371
- Item weight
- 560 grams
- Dimensions
- 229x152x29 mm
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.
"Synopsis" may belong to another edition of this title.
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