Machine Learning in Protein Science

Efficient Prediction of Protein Structures and Properties

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Beschreibung

<p><b>Harness the power of machine learning for quick and efficient calculations of protein structures and properties</b></p><p><i>Machine Learning in Protein Science</i> 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.</p><p><i>Machine Learning in Protein Science</i> provides comprehensive coverage of topics including:</p><ul><li>Machine learning models and algorithms, from deep neural network (DNN) and transfer learning (TL) to hybrid unsupervised and supervised learning</li> <li>Protein structure predictions with AlphaFold to predict the effects of point mutations</li> <li>Modeling and optimization of the catalytic activity of enzymes</li> <li>Property calculations (energy, force field, stability, protein-protein interaction, thermostability, molecular dynamics)</li> <li>Protein design and large language models (LLMs) of protein systems</li></ul> <p><i>Machine Learning in Protein Science</i> 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.</p>

Artikeldetails

EAN

9783527352159

Sprache

Englisch

Einband / Art

Buch

Maße

248 x 174 x 18 mm

Erscheinungsjahr

2026

Verlag / Hersteller

Wiley-VCH GmbH