Download PDF by Ralph A. Wheeler, David C. Spellmeyer: Annual Reports in Computational Chemistry, Vol. 4

By Ralph A. Wheeler, David C. Spellmeyer

ISBN-10: 0444532501

ISBN-13: 9780444532503

Annual reviews in Computational Chemistry is a brand new periodical supplying well timed and demanding experiences of vital issues in computational chemistry as utilized to all chemical disciplines. subject matters lined comprise quantum chemistry, molecular mechanics, strength fields, chemical schooling, and purposes in educational and business settings. each one quantity is prepared into (thematic) sections with contributions written via specialists. concentrating on the latest literature and advances within the box, every one article covers a particular subject of significance to computational chemists. Annual stories in Computational Chemistry is a "must" for researchers and scholars wishing to stick up to date on present advancements in computational chemistry.

* huge assurance of computational chemistry and up to date details
* issues lined contain bioinformatics, drug discovery, protein NMR, simulation methodologies, and purposes in educational and business settings
* each one bankruptcy experiences the latest literature on a selected subject of curiosity to computational chemists

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Extra resources for Annual Reports in Computational Chemistry, Vol. 4

Example text

Biomol. Struct. 2002, 31, 45–71. 64. M. Protein families and their evolution—A structural perspective. Ann. Rev. Biochem. 2005, 74, 867–900. 65. B. On the evolution of protein folds: Are similar motifs in different protein folds the result of convergence, insertion, or relics of an ancient peptide world? J. Struct. Biol. 2001, 134(2–3), 191–203. 66. , et al. CATH—A hierarchic classification of protein domain structures. Structure 1997, 5(8), 1093–108. 67. , et al. Evolution of the protein repertoire.

Discovery of an allosteric site in the caspases. Proc. Natl. Acad. Sci. USA 2004, 101, 12461–6. 75. B. A computational method for the analysis and prediction of protein: Phosphopeptide-binding sites. Protein Sci. 2005, 14, 131–9. 76. Halgren, T. New method for fast and accurate binding-site identification and analysis. Chem. Biol. Drug Des. 2007, 69, 146–8. 77. T. Extra precision Glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein–ligand complexes. J. Med. Chem 2006, 49, 6177–96.

Specifically, modeling protein–DNA interactions serves as the example for each of these formulations. The third section summarizes recent applications of machine learning to biomolecular modeling. The final section discusses current trends and future directions of machine learning applications to biomolecular modeling. 2. MACHINE LEARNING PROBLEM FORMULATIONS Machine learning can be broken down into a number of problem formulations. The three major categories comprise supervised, unsupervised and reinforcement Machine Learning for Protein Structure and Function Prediction 43 learning.

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Annual Reports in Computational Chemistry, Vol. 4 by Ralph A. Wheeler, David C. Spellmeyer


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