Annual Reports in Computational Chemistry

Annual Reports in Computational Chemistry

1st Edition - March 21, 2005

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  • Editor: David Spellmeyer
  • eBook ISBN: 9780080460307

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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
* The topics covered include quantum chemistry, molecular mechanics, force fields, chemical education, and applications in academic and industrial settings
* Each chapter reviews the most recent literature on a specific topic of interest to computational chemists

Readership

For researchers and students interested in computational chemistry.

Table of Contents

  • Quantum Mechanical Methods (T.D. Crawford).

    1. An Introduction to the State-of-the-Art in Quantum Chemistry (F. Jensen).

    2. Time-Dependent Density Functional Theory in Quantum Chemistry (F. Furche, K. Burke).

    3. Computational Thermochemistry: A Brief Overview of Quantum Mechanical Approaches (J.M.L. Martin).

    4. Bond Breaking in Quantum Chemistry (C.D. Sherrill).

    Molecular Modelling Methods (C. Simmerling).

    5. A Review of the TIP4P, TIP4P-Ew, TIP5P, and TIP5P-E Water Models (T.J. Dick, J.D. Madura).


    6. Molecular Modeling and Atomistic Simulation of Nucleic Acids (T.E. Cheatham, III).


    7. Empirical Force Fields for Proteins: Current Status and Future Directions (A.D. MacKerell, Jr.).


    8. Non-Equilibrium Approaches to Free Energy Calculations (A.E. Roitberg).


    9. Calculating Binding Free Energy in Protein-ligand Interaction (K. Raha, K.M. Merz, Jr.).

    Advances in QSAR/QSPR (Y. Martin).


    10. Computational Prediction of ADMET Properties: Recent Developments and Future Challenges (D.E. Clark).



    Applications of Computational Methods (H. Carlson).


    11. Filtering in Drug Discovery (C.A. Lipinski).


    12. Structure-Based Lead Optimization (D. Joseph-McCarthy).


    13. Targeting the Kinome with Computational Chemistry (M.L. Lamb).

    Chemical Education (T. Zielinski).


    14. Status of Research-based Experiences for First-and Second-Year Undergraduate Students (J.D. Evanseck, S.M. Firestine).


    15. Crossing the Line: Stochastic Models in the Chemistry Classroom (M.M. Francl).


    16. Simulation of chemical Concepts, Systems and Processes using Symbolic Computation Engines: from Computer-Assisted Problem-Solving Approach to Advanced Tools for Research (J. Rittenhouse, M. Scarlete).

    Emerging Science (R. Wheeler).


    17. The Challenges in Developing Molecular-Simulations of Fluid Properties for Industrial Applications (R.D. Mountain, A.C. Chaka).


    18. Computationally Assisted Protein Design (S. Park, J.G. Saven).







Product details

  • No. of pages: 263
  • Language: English
  • Copyright: © Elsevier Science 2005
  • Published: March 21, 2005
  • Imprint: Elsevier Science
  • eBook ISBN: 9780080460307

About the Editor

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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