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Kniha Geometric Deep Learning for Protein Engineering with Python Hayden Van Der Post

Geometric Deep Learning for Protein Engineering with Python

Autor: Hayden Van Der Post, Livia Arden, Danny Munrow
Jazyk: Angličtina
Väzba: Brožovaná
Vydavateľ: Independently published
Dostupnosť: Skladom u dodávateľa
Odosielame za 9-15 dní
36.00 €
Reactive PublishingIn the rapidly evolving intersection of artificial intelligence and biotechnology...

Informácie o knihe

Autor
Hayden Van Der Post, Livia Arden, Danny Munrow
Jazyk
Angličtina
Väzba
Kniha - Brožovaná
Vydalo
2026
Stránok
500
EAN
9798182372107
Enbook ID
52983734
Vydavateľ
Independently published
Hmotnosť
599
Rozmery
152 x 229 x 32

Kompletný popis

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.