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{ "item_title" : "Geometric Deep Learning for Protein Engineering with Python", "item_author" : [" Hayden Van Der Post", "Danny Munrow", "Livia Arden "], "item_description" : "Reactive PublishingIn the rapidly evolving intersection of artificial intelligence and biotechnology, geometric deep learning has emerged as a powerful framework for modeling the complex 3D structures and interactions that define protein function. Geometric Deep Learning for Protein Engineering with Python provides a practical, hands-on guide to applying these cutting-edge techniques to real-world protein engineering challenges.What You'll LearnCore Principles: Master the mathematical and computational foundations of geometric deep learning, including graph neural networks, equivariant architectures, and manifold-based representations tailored to molecular data.Python Implementation: Build end-to-end pipelines using popular libraries such as PyTorch Geometric, DGL, and E3NN to process protein structures from PDB files and design novel sequences with enhanced properties.Protein Engineering Applications: Learn how to predict protein stability, binding affinity, folding dynamics, and enzyme activity. Explore case studies in therapeutic protein design, antibody engineering, and synthetic biology.Advanced Techniques: Dive into diffusion models for protein generation, geometric transformers, and hybrid approaches that combine physics-based simulations with deep learning.Who This Book Is ForPerfect for computational biologists, machine learning engineers, bioinformaticians, and researchers seeking to bridge deep learning with structural biology. Whether you're a graduate student, industry professional, or experienced Python developer looking to enter the biotech space, this book offers the technical depth and code-first approach you need.Clear explanations, fully reproducible Python code examples, and progressive exercises make complex concepts accessible without sacrificing rigor. Move beyond traditional sequence-based methods and harness the full power of 3D molecular geometry to accelerate your protein engineering projects.Start engineering the proteins of tomorrow, today.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/9/79/818/237/9798182372107_b.jpg", "price_data" : { "retail_price" : "41.99", "online_price" : "41.99", "our_price" : "41.99", "club_price" : "41.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Geometric Deep Learning for Protein Engineering with Python|Hayden Van Der Post

Geometric Deep Learning for Protein Engineering with Python

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Overview

Reactive Publishing

In the rapidly evolving intersection of artificial intelligence and biotechnology, geometric deep learning has emerged as a powerful framework for modeling the complex 3D structures and interactions that define protein function. Geometric Deep Learning for Protein Engineering with Python provides a practical, hands-on guide to applying these cutting-edge techniques to real-world protein engineering challenges.What You'll Learn
  • Core Principles: Master the mathematical and computational foundations of geometric deep learning, including graph neural networks, equivariant architectures, and manifold-based representations tailored to molecular data.
  • Python Implementation: Build end-to-end pipelines using popular libraries such as PyTorch Geometric, DGL, and E3NN to process protein structures from PDB files and design novel sequences with enhanced properties.
  • Protein Engineering Applications: Learn how to predict protein stability, binding affinity, folding dynamics, and enzyme activity. Explore case studies in therapeutic protein design, antibody engineering, and synthetic biology.
  • Advanced Techniques: Dive into diffusion models for protein generation, geometric transformers, and hybrid approaches that combine physics-based simulations with deep learning.
Who This Book Is For

Perfect for computational biologists, machine learning engineers, bioinformaticians, and researchers seeking to bridge deep learning with structural biology. Whether you're a graduate student, industry professional, or experienced Python developer looking to enter the biotech space, this book offers the technical depth and code-first approach you need.

Clear explanations, fully reproducible Python code examples, and progressive exercises make complex concepts accessible without sacrificing rigor. Move beyond traditional sequence-based methods and harness the full power of 3D molecular geometry to accelerate your protein engineering projects.

Start engineering the proteins of tomorrow, today.

This item is Non-Returnable

Details

  • ISBN-13: 9798182372107
  • ISBN-10: 9798182372107
  • Publisher: Independently Published
  • Publish Date: June 2026
  • Dimensions: 9 x 6 x 1.24 inches
  • Shipping Weight: 1.32 pounds
  • Page Count: 500

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