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portada Machine Learning in Protein Science. Efficient Prediction of Protein Structures and Properties
Formato
Libro Físico
Año
2025
N° páginas
240
Encuadernación
Tapa Dura
Dimensiones
24.4x17x1.5 cm
ISBN13
9783527352159

Machine Learning in Protein Science. Efficient Prediction of Protein Structures and Properties

Jinjin Li;Yanqiang Han (Autor) · Blackwell Verlag GmbH · Tapa Dura

Machine Learning in Protein Science. Efficient Prediction of Protein Structures and Properties - Jinjin Li;Yanqiang Han

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Reseña del libro "Machine Learning in Protein Science. Efficient Prediction of Protein Structures and Properties"

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.

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