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Annual Reports in Computational Chemistry - 1st Edition - ISBN: 9780444532503, 9780080932781

Annual Reports in Computational Chemistry, Volume 4

1st Edition

Editors: Ralph Wheeler David Spellmeyer
Hardcover ISBN: 9780444532503
eBook ISBN: 9780080932781
Imprint: Elsevier Science
Published Date: 20th November 2008
Page Count: 272
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Table of Contents

Section 1: Bioinformatics (Section Editor: Wei Wang)

  1. Structural Perspectives on Protein Evolution Eric Franzosa and Yu Xia

  2. Introduction

  3. Determinants of Evolutionary Rate

  4. Theoretical Advances

  5. Empirical Results: Single Proteins

  6. Empirical Results: Higher Order Properties

  7. Summation Acknowledgements References

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

  9. Introduction

  10. Selectivity

  11. Druggability

  12. Conclusion References

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

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

  2. Introduction

  3. Machine Learning Problem Formulations

  4. Applications in Protein Structure and Function Modeling

  5. Discussion and Future Outlook Acknowledgements References

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

  7. Introduction

  8. Building Structural Models

  9. Prediction of Binding Affinities

  10. Prediction of Binding Rates

  11. Dynamics within Native Complexes and During Complex Formation

  12. Summary Points References

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

  2. Introduction

  3. Fitting Protein Titration Curves

  4. Conclusion and Outlook References

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

  6. Introduction

  7. Theory

  8. Applications and Challenges

  9. Summary and Outlook Acknowledgements References

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

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

  2. Introduction

  3. Implicit Solvent Framework

  4. Conclusions and Outlook Acknowledgments References

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

  6. Introduction

  7. Internal Motions and Flexibility

  8. Overall Tumbling and Rotational Diffusion

  9. Conclusions Acknowledgements References


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


Researchers and students interested in computational chemistry


No. of pages:
© Elsevier Science 2008
20th November 2008
Elsevier Science
Hardcover ISBN:
eBook ISBN:

Ratings and Reviews

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