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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Hardcover. Etat : new. Hardcover. 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 learningProtein structure predictions with AlphaFold to predict the effects of point mutationsModeling and optimization of the catalytic activity of enzymesProperty 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783527352159
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Buch. Etat : Neu. Neuware -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. 240 pp. Englisch. N° de réf. du vendeur 9783527352159
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Buch. Etat : Neu. Neuware -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. 240 pp. Englisch. N° de réf. du vendeur 9783527352159
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