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Annual Reports in Computational Chemistry
- 1st Edition, Volume 4 - October 30, 2008
- Editors: Ralph A. Wheeler, David Spellmeyer
- Language: English
- Hardback ISBN:9 7 8 - 0 - 4 4 4 - 5 3 2 5 0 - 3
- eBook ISBN:9 7 8 - 0 - 0 8 - 0 9 3 2 7 8 - 1
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 discip… Read more
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Request a sales quoteAnnual 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.
* 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
* 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
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
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
- No. of pages: 272
- Language: English
- Edition: 1
- Volume: 4
- Published: October 30, 2008
- Imprint: Elsevier Science
- Hardback ISBN: 9780444532503
- eBook ISBN: 9780080932781
RW
Ralph A. Wheeler
Affiliations and expertise
Department of Chemistry & Biochemistry, Duquesne University, Pittsburgh, PA, USADS
David Spellmeyer
David Spellmeyer is a Biotechnology Executive and Entrepreneur with over 30 years of broad experience in the life sciences industry. He is Principal at Interlaken Associates where he works closely with both early-stage companies and venture capital firms to build and lead strong pre-clinical R&D scientific teams focused on establishing scientific proof-of-concept for novel innovations. David is also an adjunct Associate Professor of Pharmaceutical Chemistry at the University of California San Francisco (UCSF). He has been actively involved in the entrepreneurship and innovation ecosystem supporting founders, students, post-docs, and faculty, serving as a mentor in programs at UCSF, California Life Sciences Institute’s FAST programs, California State University’s CSUPERB, UC Davis MentorNet, and as a reviewer for NIH SBIR/STTR Study Sections. David has recently served as CSO at Circle Pharma, an Executive-in-Residence at Pandect Biosciences, head of Quality for a diagnostics company, and an executive advisor for several startups. Prior to building Interlaken Associates, he held positions at DuPont Pharma (BMS), Chiron (Novartis), Signature BioScience, Nodality, and IBM Research. David works very closely with business development teams and has completed over 20 non-dilutive strategic corporate partnerships, several mergers, acquisitions, and joint ventures and participated in several rounds of venture financing. He received his BS in computer science and chemistry from Purdue University and his PhD in theoretical organic chemistry from UCLA and completed his post-doctoral training in pharmaceutical chemistry at UCSF.
Affiliations and expertise
Department of Pharmaceutical Chemistry, School of Pharmacy, University of California San Francisco, San Francisco, CA, USA