Machine Learning in Protein Science : Efficient Prediction of Protein Structures and Properties （1. Auflage. 2025. 320 S. 30 SW-Abb., 50 Farbabb. 244 mm）

Li, Jinjin/Han, Yanqiang

WILEY-VCH（2025/11発売）

• 製本 Hardcover:ハードカバー版／ページ数 320 p.
• 言語 ENG
• 商品コード 9783527352159

Full Description

Aimed at researchers in the molecular life sciences, this unique reference summarizes current approaches for harnessing the power of machine learning for more efficient full quantum mechanical (FQM) calculations in protein systems. Application examples range from property calculations (energy, force field, stability, protein-protein interaction, thermostability, molecular dynamics) to protein structure prediction to protein design and the optimization of enzymatic activity. From a methodological point of view, the practical reference covers the most important machine learning models and algorithms, from deep neural network (DNN) and transfer learning (TL) to hybrid unsupervised and supervised learning.

Contents

Introduction
Fundamentals of Theoretical Calculations on Protein Systems
Machine Learning-driven Ab Initio Protein Design
Prediction of Protein Mutation Effects
Structure Prediction with AlphaFold
Deep Neural Network-assisted Full-System Quantum Mechanical (FQM) Calculations of Proteins
Transfer Learning-assisted Full-System Quantum Mechanical (FQM) Calculations of Proteins
Universal Protein Feature Dictionary and Framework for Protein Property Predictions
Recurrent Neural Network-assisted Thermostability Predictions of Protein Systems
Machine Learning-assisted Full-System Quantum Mechanical (FQM) Calculations of Enzymes in Industrial Environmnts
Outlook