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 Scienceis 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 Scienceprovides 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 Scienceis 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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