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Artículo: AMZ-B0DSWH8333
Advanced Computational Approaches to Model Protein Folding in Python
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Paperback
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0.45 kg
0.45 kg
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Amazon
Amazon
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USA
USA
Sobre este producto
- Empower your protein research with a uniquely comprehensive framework designed to simulate and predict complex folding pathways, all powered by detailed, end-to-end Python code. This advanced reference seamlessly integrates computational physics, machine learning, and structural bioinformatics to guide you through every step of algorithmic protein modeling, providing clear explanations and directly usable Python scripts.Inside these pages, you’ll find:• Graph-Based Reinforcement Learning: Harness node-edge interactions to model real-time contact formation and residue-specific actions. • Enhanced Markov Chain Monte Carlo: Employ specialized transition kernels and acceptance criteria to traverse massive conformational landscapes with speed and precision. • Sparse Autoencoder & Variational Methods: Discover how dimensionality reduction, combined with high-fidelity reconstructions, can dramatically accelerate folding simulations. • Diffusion-Driven & Quantum-Inspired Approaches: Explore progressive methodologies that rethink how energy landscapes and quantum state representations can reshape traditional fold predictions. • Hybrid Co-Folding & Multi-Objective Optimization: Tackle protein–protein complexes and balance stability with functional efficacy under a single computational umbrella.Whether you are optimizing disulfide bonds, modeling membrane proteins under implicit solvent, or leveraging real-world crosslink mass spectrometry data to refine loop conformations, each chapter delivers step-by-step instructions and real-world Python code to jumpstart your own explorations. Embrace a world where advanced algorithms and open-source frameworks converge, fueling next-generation insights and breakthroughs in protein science.
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