Muutke küpsiste eelistusi

Molecular Modelling: Principles and Applications 2nd edition [Pehme köide]

4.37/5 (74 hinnangut Goodreads-ist)
  • Formaat: Paperback / softback, 768 pages, kõrgus x laius x paksus: 234x170x41 mm, kaal: 1260 g
  • Ilmumisaeg: 29-Jan-2001
  • Kirjastus: Pearson
  • ISBN-10: 0582382106
  • ISBN-13: 9780582382107
Teised raamatud teemal:
Molecular Modelling: Principles and Applications 2nd editionSuurem pilt
  • Formaat: Paperback / softback, 768 pages, kõrgus x laius x paksus: 234x170x41 mm, kaal: 1260 g
  • Ilmumisaeg: 29-Jan-2001
  • Kirjastus: Pearson
  • ISBN-10: 0582382106
  • ISBN-13: 9780582382107
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780582382107.html
Märksõnad:

This important new edition is for graduate students studying Molecular Modelling, Computational Chemistry within Chemistry,  Medicinal Chemistry and Biochemistry.   Postgraduates and researchers in academia and in the chemical and pharmaceutical industries. This new edition introduces background theory and techniques of molecular modelling, also illustrates applications in studying physical, chemical and biological phenomena.  It includes simple numerical examples and numerous explanatory figures and a colour plate section. 

Preface to the Second Edition xiii
Preface to the First Edition xv
Symbols and Physical Constants xvii
Acknowledgements xxi
Useful Concepts in Molecular Modelling
1(25)
Introduction
1(1)
Coordinate Systems
2(2)
Potential Energy Surfaces
4(1)
Molecular Graphics
5(1)
Surfaces
6(2)
Computer Hardware and Software
8(1)
Units of Length and Energy
9(1)
The Molecular Modelling Literature
9(1)
The Internet
10(14)
Mathematical Concepts
24(2)
Further Reading
24(1)
References
24(2)
An Introduction to Computational Quantum Mechanics
26(82)
Introduction
26(4)
One-electron Atoms
30(4)
Polyelectronic Atoms and Molecules
34(7)
Molecular Orbital Calculations
41(10)
The Hartree-Fock Equations
51(14)
Basis Sets
65(9)
Calculating Molecular Properties Using an initio Quantum Mechanics
74(12)
Approximate Molecular Orbital Theories
86(1)
Semi-empirical Methods
86(13)
Huckel Theory
99(3)
Performance of Semi-empirical Methods
102(6)
Some Common Acronyms Used in Computational Quantum Chemistry
104(1)
Further Reading
105(1)
References
105(3)
Advanced ab initio Methods, Density Functional Theory and Solid-state Quantum Mechanics
108(57)
Introduction
108(1)
Open-shell Systems
108(2)
Electron Correlation
110(7)
Practical Considerations When Performing ab initio Calculations
117(5)
Energy Component Analysis
122(2)
Valence Bond Theories
124(2)
Density Functional Theory
126(12)
Quantum Mechanical Methods for Studying the Solid State
138(22)
The Future Role of Quantum Mechanics: Theory and Experiment Working Together
160(5)
Alternative Expression for a Wavefunction Satisfying Bloch's Function
161(1)
Further Reading
161(1)
References
162(3)
Empirical Force Field Models: Molecular Mechanics
165(88)
Introduction
165(3)
Some General Features of Molecular Mechanics Force Fields
168(2)
Bond Stretching
170(3)
Angle Bending
173(1)
Torsional Terms
173(3)
Improper Torsions and Out-of-plane Bending Motions
176(2)
Cross Terms: Class 1, 2 and 3 Force Fields
178(3)
Introduction to Non-bonded Interactions
181(1)
Electrostatic Interactions
181(23)
Van der Waals Interactions
204(8)
Many-body Effects in Empirical Potentials
212(2)
Effective Pair Potentials
214(1)
Hydrogen Bonding in Molecular Mechanics
215(1)
Force Field Models for the Simulation of Liquid Water
216(5)
United Atom Force Fields and Reduced Representations
221(4)
Derivatives of the Molecular Mechanics Energy Function
225(1)
Calculating Thermodynamic Properties Using a Force Field
226(2)
Force Field Parametrisation
228(3)
Transferability of Force Field Parameters
231(2)
The Treatment of Delocalised π Systems
233(1)
Force Fields for Inorganic Molecules
234(2)
Force Fields for Solid-state Systems
236(4)
Empirical Potentials for Metals and Semiconductors
240(13)
The Interaction Between Two Drude Molecules
246(1)
Further Reading
247(1)
References
247(6)
Energy Minimisation and Related Methods for Exploring the Energy Surface
253(50)
Introduction
253(5)
Non-derivative Minimisation Methods
258(3)
Introduction to Derivative Minimisation Methods
261(1)
First-order Minimisation Methods
262(5)
Second Derivative Methods: The Newton-Raphson Method
267(1)
Quasi-Newton Methods
268(2)
Which Minimisation Method Should I Use?
270(3)
Application of Energy Minimisation
273(6)
Determination of Transition Structures and Reaction Pathways
279(16)
Solid-state Systems: Lattice Statics and Lattice Dynamics
295(8)
Further Reading
300(1)
References
301(2)
Computer Simulation Methods
303(50)
Introduction
303(4)
Calculation of Simple Thermodynamic Properties
307(5)
Phase Space
312(3)
Practical Aspects of Computer Simulation
315(2)
Boundaries
317(4)
Monitoring the Equilibration
321(3)
Truncating the Potential and the Minimum Image Convention
324(10)
Long-range Forces
334(9)
Analysing the Results of a Simulation and Estimating Errors
343(10)
Basic Statistical Mechanics
347(1)
Heat Capacity and Energy Fluctuations
348(1)
The Real Gas Contribution to the Virial
349(1)
Translating Particle Back into Central Box for Three Box Shapes
350(1)
Further Reading
351(1)
References
351(2)
Molecular Dynamics Simulation Methods
353(57)
Introduction
353(1)
Molecular Dynamics Using Simple Models
353(2)
Molecular Dynamics with Continuous Potentials
355(9)
Setting up and Running a Molecular Dynamics Simulation
364(4)
Constraint Dynamics
368(6)
Time-dependent Properties
374(8)
Molecular Dynamics at Constant Temperature and Pressure
382(5)
Incorporating Solvent Effects into Molecular Dynamics: Potentials of Mean Force and Stochastic Dynamics
387(5)
Conformational Changes from Molecular Dynamics Simulations
392(2)
Molecular Dynamics Simulations of Chain Amphiphiles
394(16)
Energy Conservation in Molecular Dynamics
405(1)
Further Reading
406(1)
References
406(4)
Monte Carlo Simulation Methods
410(47)
Introduction
410(2)
Calculating Properties by Integration
412(2)
Some Theoretical Background to the Metropolis Method
414(3)
Implementation of the Metropolis Monte Carlo Method
417(3)
Monte Carlo Simulation of Molecules
420(3)
Models Used in Monte Carlo Simulations of Polymers
423(9)
`Biased' Monte Carlo Methods
432(1)
Tackling the Problem of Quasi-ergodicity: J-walking and Multicanonical Monte Carlo
433(5)
Monte Carlo Sampling from Different Ensembles
438(4)
Calculating the Chemical Potential
442(1)
The Configurational Bias Monte Carlo Method
443(7)
Simulating Phase Equilibria by the Gibbs Ensemble Monte Carlo Method
450(2)
Monte Carlo or Molecular Dynamics?
452(5)
The Marsaglia Random Number Generator
453(1)
Further Reading
454(1)
References
454(3)
Conformational Analysis
457(52)
Introduction
457(1)
Systematic Methods for Exploring Conformational Space
458(6)
Model-building Approaches
464(1)
Random Search Methods
465(2)
Distance Geometry
467(8)
Exploring Conformational Space Using Simulation Methods
475(1)
Which Conformational Search Method Should I Use? A Comparison of Different Approaches
476(1)
Variations on the Standard Methods
477(2)
Finding the Global Energy Minimum: Evolutionary Algorithms and Simulated Annealing
479(4)
Solving Protein Structures Using Restrained Molecular Dynamics and Simulated Annealing
483(6)
Structural Databases
489(1)
Molecular Fitting
490(1)
Clustering Algorithms and Pattern Recognition Techniques
491(6)
Reducing the Dimensionality of a Data Set
497(2)
Covering Conformational Space: Poling
499(2)
A `Classic' Optimisation Problem: Predicting Crystal Structures
501(8)
Further Reading
505(1)
References
506(3)
Protein Structure Prediction, Sequence Analysis and Protein Folding
509(54)
Introduction
509(4)
Some Basic Principles of Protein Structure
513(4)
First-principles Methods for Predicting Protein Structure
517(5)
Introduction to Comparative Modelling
522(1)
Sequence Alignment
522(17)
Constructing and Evaluating a Comparative Model
539(6)
Predicting Protein Structures by `Threading'
545(2)
A Comparison of Protein Structure Prediction Methods: CASP
547(2)
Protein Folding and Unfolding
549(14)
Some Common Abbreviations and Acronyms Used in Bioinformatics
553(2)
Some of the Most Common Sequence and Structural Databases Used in Bioinformatics
555(1)
Mutation Probability Matrix for 1 PAM
556(1)
Mutation Probability Matrix for 250 PAM
557(1)
Further Reading
557(1)
References
558(5)
Four Challenges in Molecular Modelling: Free Energies, Solvation, Reactions and Solid-state Defects
563(77)
Free Energy Calculations
563(1)
The Calculation of Free Energy Differences
564(5)
Applications of Methods for Calculating Free Energy Differences
569(5)
The Calculation of Enthalpy and Entropy Differences
574(1)
Partitioning the Free Energy
574(3)
Potential Pitfalls with Free Energy Calculations
577(3)
Potentials of Mean Force
580(5)
Approximate/`Rapid' Free Energy Methods
585(7)
Continuum Representations of the Solvent
592(1)
The Electrostatic Contribution to the Free Energy of Solvation: The Born and Onsager Models
593(15)
Non-electrostatic Contributions to the Solvation Free Energy
608(1)
Very Simple Solvation Models
609(1)
Modelling Chemical Reactions
610(12)
Modelling Solid-state Defects
622(18)
Calculating Free Energy Differences Using Thermodynamic Integration
630(1)
Using the Slow Growth Method for Calculating Free Energy Differences
631(1)
Expansion of Zwanzig Expression for the Free Energy Difference for the Linear Response Method
631(1)
Further Reading
632(1)
References
633(7)
The Use of Molecular Modelling and Chemoinformatics to Discover and Design New Molecules
640(87)
Molecular Modelling in Drug Discovery
640(2)
Computer Representations of Molecules, Chemical Databases and 2D Substructure Searching
642(5)
3D Database Searching
647(1)
Deriving and Using Three-dimensional Pharmacophores
648(11)
Sources of Data for 3D Databases
659(2)
Molecular Docking
661(6)
Applications of 3D Database Searching and Docking
667(1)
Molecular Similarity and Similarity Searching
668(1)
Molecular Descriptors
668(12)
Selecting `Diverse' Sets of Compounds
680(7)
Structure-based De Novo Ligand Design
687(8)
Quantitative Structure-Activity Relationships
695(11)
Partial Least Squares
706(5)
Combinatorial Libraries
711(16)
Further Reading
719(1)
References
720(7)
Index 727
Dr. Andrew Leach is a Group Leader in Computational Chemistry and Informatics at Glaxo Wellcome Research and Development.