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Viser: Machine Learning in Protein Science - Efficient Prediction of Protein Structures and Properties
Machine Learning in Protein Science Vital Source e-bog
Jinjin Li og Yanqiang Han
(2025)
Machine Learning in Protein Science
Efficient Prediction of Protein Structures and Properties
Jinjin Li og Yanqiang Han
(2025)
Sprog: Engelsk
om ca. 15 hverdage
Detaljer om varen
- 1. Udgave
- Vital Source E-book
- Udgiver: John Wiley & Sons (Januar 2025)
- Forfattere: Jinjin Li og Yanqiang Han
- ISBN: 9783527842353
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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Print: 10 sider kan printes ad gangen
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Detaljer om varen
- Hardback: 240 sider
- Udgiver: John Wiley & Sons, Incorporated (November 2025)
- Forfattere: Jinjin Li og Yanqiang Han
- ISBN: 9783527352159
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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