Machine Learning in Protein Science

This unique practical reference for protein scientist shows how to harness the power of machine learning for quick and efficient full quantum mechanical calculations of protein structures and properties.

1

Autorentext
Jinjin Li is a professor at the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University in Shanghai, China. Having obtained her Ph.D. degrees from Shanghai University, she performed postdoctoral work at the University of Illinois, USA and was a Senior Research Fellow at the University of California, USA. Professor Li has authored over 200 publications and four monographs. She is also a long-standing editorial board member and reviewer for several international academic journals.

Yanqiang Han is an assistant professor at the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University in Shanghai, China. He obtained his Ph.D. degrees from Shanghai University. He has authored over 30 publications in the field of computational biology and machine learning and is a reviewer for several international academic journals.

Inhalt
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