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Annual Reports in Computational Chemistry
 
 

Annual Reports in Computational Chemistry, 1st Edition

 
Annual Reports in Computational Chemistry, 1st Edition,Ralph Wheeler,David Spellmeyer,ISBN9780444532503
 
 
 

Annual Reports in Computational Chemistry

Wheeler   &   Spellmeyer   

Elsevier Science

9780444532503

9780080932781

272

229 X 152

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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

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.

Readership

Researchers and students interested in computational chemistry

Ralph Wheeler

Affiliations and Expertise

Department of Chemistry & Biochemistry, University of Oklahoma, Norman, USA

View additional works by Ralph A. Wheeler

David Spellmeyer

Affiliations and Expertise

Nodality, Inc., CA, USA

View additional works by David C. Spellmeyer

Annual Reports in Computational Chemistry, 1st Edition

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
 
 
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