Machine Learning in Protein Science: Efficient Prediction of Protein Structures and Properties - Couverture rigide

Li, Jinjin; Han, Yanqiang

Couverture rigide

ISBN 10 : 3527352155 ISBN 13 : 9783527352159

Editeur : Blackwell Verlag GmbH, 2025

Synopsis

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.

A propos du livre

À propos de l'auteur

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.

Détails de l'édition

• Éditeur: Blackwell Verlag GmbH
• Date d'édition: 2025
• Langue: anglais
• ISBN 10: 3527352155
• ISBN 13: 9783527352159
• Reliure: Relié
• Nombre de pages: 240
• Coordonnées du fabricant: JOSSEY-BASS; https://www.wiley.com/en-us; ; 111 River Street; Hoboken; NJ; 7030; Etats-Unis
• Personne responsable: GPSR Kontakt; wiley-vch@kolibri360.de; ; Boschstraße 12; Weinheim; 69469; Allemagne