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.
* 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
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1. Real World Kinetics via Simulations.
Section 2: Quantum Mechanical Methods
2. Explicitly Correlated Approaches for Electronic Structure Computations.
3. Hybrid Methods: ONIOM (QM:MM) and QM/MM.
4. On the Selection of Domains and Pairs in Local Correlation Treatments.
Section 3: Molecular Modeling Methods
5. Simulations of Temperature and Pressure Unfolding Peptides and Proteins with Replica Exchange Molecular Dynamics.
6. Hybrid Explicit/Implicit Solvation Methods.
Section 4: Advances in QSAR/QSPR
7. Variable Selection QSAR and Model Validation.
8. Machine Learning in Computational Chemistry.
9. Molecular Similarity: Advances in Methods, Applications, and Validations in Virtual Screening and QSAR.
Section 5: Applications of Computational Methods
10. Cytochrome P450 Enzymes: Computational Approaches to Substrate Prediction.
11. Recent Advances in Design of Small-Molecule Ligands to Target Protein-Protein Interactions.
12. Accelerating Conformational Transitions in Biomolecular Simulations.
13. Principal Component Analysis: A Review of its Application on Molecular Dynamics Data.
14. Solvent Effects on Organic Reactions from QM/MM Simulations.
15. Structure-Based Design of New Anti-Bacterial Agents.
16. Recent Evaluations of High Throughput Docking Methods for Pharmaceutical Lead Finding
Consensus and Caveats.
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.