Jinjin Li, Yanqiang Han

Machine Learning in Protein Science (eBook, PDF)

Efficient Prediction of Protein Structures and Properties

• Format: PDF

Produktbeschreibung

Harness the power of machine learning for quick and efficient calculations of protein structures and properties

Machine Learning in Protein Science is a unique and practical reference that shows how to employ machine learning approaches for full quantum mechanical (FQM) calculations of protein structures and properties, thereby saving costly computing time and making this technology available for routine users.

Machine Learning in Protein Science provides comprehensive coverage of topics including:

• Machine learning models and algorithms, from deep neural network (DNN) and transfer learning (TL) to hybrid unsupervised and supervised learning
• Protein structure predictions with AlphaFold to predict the effects of point mutations
• Modeling and optimization of the catalytic activity of enzymes
• Property calculations (energy, force field, stability, protein-protein interaction, thermostability, molecular dynamics)
• Protein design and large language models (LLMs) of protein systems

Machine Learning in Protein Science is an essential reference on the subject for biochemists, molecular biologists, theoretical chemists, biotechnologists, and medicinal chemists, as well as students in related programs of study.

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Produktdetails

• Verlag: Wiley-VCH
• Seitenzahl: 242
• Erscheinungstermin: 7. Oktober 2025
• Englisch
• ISBN-13: 9783527842360
• Artikelnr.: 75630014

Autorenporträt

Jinjin Li is a Professor at the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University in Shanghai, China. She performed postdoctoral work at the University of Illinois, USA and was a Senior Research Fellow at the University of California, USA.

Yanqiang Han is an Assistant Professor at the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University in Shanghai, China.

Inhaltsangabe

Introduction
Fundamentals of Theoretical Calculations on Protein Systems
Protein Structure Prediction by Artificial Intelligence
Methods and Tools for Predicting Protein Folding from Free Energy Change upon Mutation
Deep Neural Network-assisted Full-System Quantum Mechanical (FQM) Calculations of Proteins
Transfer Learning-assisted Full-System Quantum Mechanical (FQM) Calculations of Proteins
Protein Interaction Prediction with Artificial Intelligence
Protein Function Annotation with Machine Learning
Machine Learning-driven ab initio Protein Design
Large Language Models of Protein Systems
Outlook