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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.
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Reinforcement Learning for Drug Discovery and Molecular Optimization with Python (Paperback)
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Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798181338371
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Reinforcement Learning for Drug Discovery and Molecular Optimization with Python
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Reinforcement Learning for Drug Discovery and Molecular Optimization with Python
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Reinforcement Learning for Drug Discovery and Molecular Optimization with Python
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Reinforcement Learning for Drug Discovery and Molecular Optimization with Python (Paperback)
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Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798181338371
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Reinforcement Learning for Drug Discovery and Molecular Optimization with Python
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. 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