Annual Reports in Computational Chemistry

Annual Reports in Computational Chemistry

1st Edition - October 30, 2008

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  • Editors: Ralph Wheeler, David Spellmeyer
  • eBook ISBN: 9780080932781

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Description

Annual Reports in Computational Chemistry is a new periodical providing timely and critical reviews of important topics in computational chemistry as applied to all chemical disciplines. Topics covered include quantum chemistry, molecular mechanics, force fields, chemical education, and applications in academic and industrial settings. Each volume is organized into (thematic) sections with contributions written by experts. Focusing on the most recent literature and advances in the field, each article covers a specific topic of importance to computational chemists. Annual Reports in Computational Chemistry is a "must" for researchers and students wishing to stay up-to-date on current developments in computational chemistry.

Key Features

* Broad coverage of computational chemistry and up-to-date information
* Topics covered include bioinformatics, drug discovery, protein NMR, simulation methodologies, and applications in academic and industrial settings
* Each chapter reviews the most recent literature on a specific topic of interest to computational chemists

Readership

Researchers and students interested in computational chemistry

Table of Contents

  • Section 1: Bioinformatics (Section Editor: Wei Wang)
    1. Structural Perspectives on Protein Evolution
    Eric Franzosa and Yu Xia

    1. Introduction
    2. Determinants of Evolutionary Rate
    3. Theoretical Advances
    4. Empirical Results: Single Proteins
    5. Empirical Results: Higher Order Properties
    6. Summation
    Acknowledgements
    References

    2. Predicting Selectivity and Druggability in Drug Discovery
    Alan C. Cheng

    1. Introduction
    2. Selectivity
    3. Druggability
    4. Conclusion
    References



    Section 2: Biological Modeling (Section Editor: Nathan Barker)

    3. Machine Learning for Protein Structure and Function Prediction
    Robert Ezra Langlois and Hui Lu

    1. Introduction
    2. Machine Learning Problem Formulations
    3. Applications in Protein Structure and Function Modeling
    4. Discussion and Future Outlook
    Acknowledgements
    References

    4. Modeling Protein-Protein and Protein-Nucleic Acid Interactions: Structure, Thermodynamics, and Kinetics
    Huan-Xiang Zhou, Sanbo Qin and Harianto Tjong

    1. Introduction
    2. Building Structural Models
    3. Prediction of Binding Affinities
    4. Prediction of Binding Rates
    5. Dynamics within Native Complexes and During Complex Formation
    6. Summary Points
    References


    5. Analysing Protein NMR pH-titration Curves
    Jens Erik Nielsen

    1. Introduction
    2. Fitting Protein Titration Curves
    3. Conclusion and Outlook
    References

    6. Implicit Solvent Simulations of Biomolecules in Cellular Environments
    Michael Feig, Seiichiro Tanizaki and Maryam Sayadi

    1. Introduction
    2. Theory
    3. Applications and Challenges
    4. Summary and Outlook
    Acknowledgements
    References



    Section 3: Simulation Methodologies (Section Editor: Carlos Simmerling)

    7. Implicit Solvent Models in Molecular Dynamics Simulations: A Brief Overview
    Alexey Onufriev

    1. Introduction
    2. Implicit Solvent Framework
    3. Conclusions and Outlook
    Acknowledgments
    References

    8. Comparing MD Simulations and NMR Relaxation Parameters
    Vance Wong and David A. Case

    1. Introduction
    2. Internal Motions and Flexibility
    3. Overall Tumbling and Rotational Diffusion
    4. Conclusions
    Acknowledgements
    References

Product details

  • No. of pages: 272
  • Language: English
  • Copyright: © Elsevier Science 2008
  • Published: October 30, 2008
  • Imprint: Elsevier Science
  • eBook ISBN: 9780080932781

About the Editors

Ralph Wheeler

Affiliations and Expertise

Department of Chemistry & Biochemistry, Duquesne University, Pittsburgh, PA, USA

David Spellmeyer

David Spellmeyer, PhD is an Advisor to startup and early venture companies providing technical and scientific guidance on overcoming technological, scientific and business development challenges. He brings broad business and technical expertise from companies both large (IBM, DuPont) and small (Chiron, CombiChem, Signature BioScience, Nodality). David has been involved in the development of advanced functional assays such as Nodality’s Single Cell Network Profiling (SCNP) and Signature’s label-free molecular and cellular screening systems. He has extensive experience in the management and analysis of high dimensional data (combinatorial chemistry and SCNP). He has worked closely with business development teams in establishing over 20 non-dilutive strategic corporate partnerships, 4 mergers and acquisitions, several rounds of venture financing, and two joint ventures. David received his Ph.D. in theoretical organic chemistry from UCLA. He completed his post-doctoral training in pharmaceutical chemistry at UCSF, where he remains an active Adjunct Associate Professor.

Affiliations and Expertise

Nodality, Inc., CA, USA

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