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Computer Chemistry and Molecular Design

样章
作者：
范波涛 张瑞生 姚建华
定价：
68.00元
ISBN：
978-7-04-018296-5
版面字数：
540千字
开本：
16开
全书页数：
490页
装帧形式：
精装
重点项目：
暂无
出版时间：
2009-03-31
物料号：
18296-00
读者对象：
学术著作
一级分类：
自然科学
二级分类：
化学
三级分类：
通用

《Computer Chemistry and Molecular Design》内容为：This book covers two parts: Computer Chemistry and Molecular Design. It presented several approaches employed in molecular design step by step. Molecular graph and presentation as the start point, it introduced several methods of Computer Chemistry concerned with molecular design: property recognition of chemical structure, artificial neural network and data analysis.

Computer-aided molecular design has the relationship with drug discovery, pesticide design, food chemistry, environment chemistry and so on. This book can be a reference for the people who is engaged or interested in computer-aided molecular design.

  • Front Matter
  • 1 Numeric Analysis
    • 1.1 Linear Algebraic Equations and Matrix.
      • 1.1.1 Square Matrix
      • 1.1.2 Elementary Operations on Matrices
      • 1.1.3 Inverse Matrices
    • 1.2 Di®erential Equations
      • 1.2.1 Cauchy Problem
      • 1.2.2 Discrete One-Step Methods for Equations of First Order Principles
      • 1.2.3 Discrete Methods with Multiple Steps for Di®erential Equations of Order
    • Problems
  • 2 Minimization
    • 2.1 One-dimensional Minimization
      • 2.1.1 Golden Section Search
      • 2.1.2 Parabolic Interpolation: Brent's Method
    • 2.2 Multidimensional Minimization
      • 2.2.1 Downhill Simplex (Nelder et Mead, 1965)
      • 2.2.2 Direction Set Method (Powell's Method)
      • 2.2.3 Conjugate Directions
      • 2.2.4 Powell's Quadratically Convergent Method: Find N Conjugate Directions
      • 2.2.5 Gradient and the Steepest Descent Method.
      • 2.2.6 Newton-Raphson Method
    • Problems
  • 3 Ab Initio
    • 3.1 SchrÄodinger Equation
    • 3.2 Hartree-Fock Theory
    • 3.3 Post-SCF Methods
      • 3.3.1 Con¯guration Interaction
      • 3.3.2 Moller-Plesset Perturbation Theory
    • 3.4 Gaussian
      • 3.4.1 Capabilities
      • 3.4.2 Overview of Geometry Optimizations
      • 3.4.3 Model Chemistries
      • 3.4.4 Basis Sets
      • 3.4.5 Limitations
    • 3.5 Example of Applications
    • Problem
  • 4 Density Functional Theory and Applications
    • 4.1 Theoretical Aspects
      • 4.1.1 Hohenberg-Kohn Theorems
      • 4.1.2 Kohn-Sham Theory
      • 4.1.3 Exchange-Correlation Functionals
    • 4.2 Comparison with Traditional Ab Initio Methods
    • 4.3 Applications
      • 4.3.1 Atoms
      • 4.3.2 Clusters.
      • 4.3.3 Carbonyl Complexes
      • 4.3.4 Other Applications.
    • Problems
  • 5 Semi-Empirical Models
    • 5.1 Extended HÄuckel Theory (EHT)
    • 5.2 CNDO Method
    • 5.3 INDO Method
    • 5.4 MINDO Method
    • 5.5 MNDO Method
    • 5.6 AM1 and PM3 Methods
    • 5.7 Example of Applications
      • 5.7.1 Proposed Problem
      • 5.7.2 Calculations
      • 5.7.3 Conclusion
  • 6 Molecular Mechanics
    • 6.1 Stereochemistry
      • 6.1.1 Constitution Isomers and Tautomers
      • 6.1.2 Stereoisomers
    • 6.2 Principle of Molecular Mechanics
    • 6.3 Mathematical Expressions of Energy Terms
      • 6.3.1 Bond Stretching Energy
      • 6.3.2 Angle Bending Energy
      • 6.3.3 Torsion Energy
      • 6.3.4 Improper Torsions and Out-of-plane Bending Motions
      • 6.3.5 Cross Terms: Class 1, 2 and 3 Force Fields
      • 6.3.6 Energy of Non-binding Interactions
    • 6.4 Force Fields
      • 6.4.1 Parameterization Methods
      • 6.4.2 SYBYL and TRIPOS Force Field
      • 6.4.3 KOLLMAN Force Field
      • 6.4.4 JUMNA and FLEX Force Field
    • 6.5 Application Example
      • 6.5.1 Problem to be Solved
    • Problems
  • 7 Molecular Dynamics
    • 7.1 Molecular Dynamics with Simple Models
    • 7.2 Molecular Dynamics with Continuous Potentials
    • 7.3 Verlet Algorithm.
    • 7.4 Variations of Verlet Algorithm
    • 7.5 Predictor-Corrector Integration Methods
    • 7.6 Choosing the Time Step
    • 7.7 Multiple Time Step Dynamics
    • 7.8 Constraint Dynamics
    • 7.9 Steps of Molecular Dynamics Simulation
    • 7.10 Time-dependent Properties
    • 7.11 Example of Applications
    • Problems
  • 8 Monte Carlo and Conformational Analysis
    • 8.1 Monte Carlo
      • 8.1.1 Calculations of Properties
      • 8.1.2 Theory of Metropolis MC(MMC)
      • 8.1.3 Implementation of MMC.
      • 8.1.4 Simulation of Molecules
    • 8.2 Conformational Analysis
      • 8.2.1 Conformational Search
      • 8.2.2 Model-building Approaches
      • 8.2.3 Random Search Methods.
      • 8.2.4 Distance Geometry
      • 8.2.5 Other Approaches
    • Problems
  • 9 Molecular Graph and Presentation
    • 9.1 Generality in Molecular Graph
    • 9.2 Molecular Description
      • 9.2.1 Computer Representation of Two-dimensional Chemical Structures
      • 9.2.2 Connection Table
    • 9.3 Sub-Structures and FREL
      • 9.3.1 Extraction of FREL
      • 9.3.2 Correlation of FREL with Properties
    • 9.4 Canonical Numbering
    • 9.5 Three-dimensional Representation of Molecular Structures
    • 9.6 XML Representation
      • 9.6.1 Principles of XML
      • 9.6.2 XML for Structuring the Information
    • Problem
  • 10 Ring Perception
    • 10.1 Graphs and Searching Methods
      • 10.1.1 Graphs and Molecular Graphs
      • 10.1.2 Depth-First Search
      • 10.1.3 Breadth-First Search
    • 10.2 Complete Ring Systems
      • 10.2.1 De¯ned Terms
      • 10.2.2 Number of Rings in SSSR
    • 10.3 Zamora's Algorithm
    • 10.4 Elimination Technique
    • Problems
  • 11 Constitutional Equivalence
    • 11.1 Morgan Algorithm
    • 11.2 Munk Algorithm
    • 11.3 RÄucker Algorithm
    • 11.4 Varmuza Algorithm
    • 11.5 Fan's Method
    • 11.6 Application to Canonical Numbering
    • Problem
  • 12 Molecular Relative Symmetry
    • 12.1 Relative Symmetry
    • 12.2 Determination of Dissimilarity
      • 12.2.1 Atom Focus
      • 12.2.2 Bond Focus
    • 12.3 Calculations
    • 12.4 Examples
    • 12.5 Application Examples: Empirical Rules
      • 12.5.1 Empirical Rules for Gem Methyl Groups Adjacent to a sp2 Carbon Site
      • 12.5.2 Empirical Rules for Gem Methyl Groups Borne by a sp3 Atom
    • Problem
  • 13 Isomorphism and 3D CSS Searches
    • 13.1 Ullman's Algorithm
    • 13.2 Crandell and Smith's Algorithm
    • 13.3 Clique-Detection Algorithm
    • 13.4 Lesk's Algorithm
    • 13.5 Set-Reduction Algorithm
    • Problems
  • 14 Chemical Information Management and Exploration
    • 14.1 Concept
    • 14.2 Source of Chemical Information
      • 14.2.1 Chemical Abstracts (CA) File
      • 14.2.2 SCI Search
      • 14.2.3 Chinese Scienti¯c Citation Database (CSCD).
      • 14.2.4 Science China Database (SCD)
      • 14.2.5 SpecInfo
      • 14.2.6 CrossFire
      • 14.2.7 Cambridge Structural Database (CSD)
    • 14.3 Management of Chemical Information
      • 14.3.1 Principle
      • 14.3.2 Representation of Chemical Structure
      • 14.3.3 Chemical Structure Searching
      • 14.3.4 Examples
    • 14.4 Data Mining and Spectral Simulation
      • 14.4.1 Data Mining
      • 14.4.2 Application: IR/Raman Simulation
      • 14.4.3 Application: Mass Spectra Simulation
    • Problems
  • 15 Molecular Similarity and Diversity
    • 15.1 Approaches on Molecular Similarity and Diversity
      • 15.1.1 Components of the Molecular Similarity
      • 15.1.2 The Similarity Paradox
      • 15.1.3 Similarity/Diversity Approaches in Chemoinformatics
    • 15.2 Selection, Classi¯cation and Validation Methods.
      • 15.2.1 Classi¯cation Methods
      • 15.2.2 Selection Methods
      • 15.2.3 Validation Methods
    • 15.3 Comparative Analysis of Approaches/Descriptors-Models Comparison
      • 15.3.1 Comparison of Similarity Coe±cients
      • 15.3.2 Choice of Descriptors
      • 15.3.3 Comparison and Selection of Descriptors
    • 15.4 Applications in Chemo- and Bio- Informatics
      • 15.4.1 Combinatorial Chemistry, Chemical Libraries
      • 15.4.2 Drug Design and Medicinal Chemistry
      • 15.4.3 Synthesis Chemistry, Organic Chemistry and Catalyst
      • 15.4.4 Bioinformatics
    • Problems
  • 16 Arti¯cial Neural Networks: Biological Fundamental and Modeling
    • 16.1 Introduction
    • 16.2 Biological Fundamental
    • 16.3 Modeling.
    • 16.4 Structure of Connections
    • Problems
  • 17 Layered Network
    • 17.1 Mono-Layered Associative Network.
      • 17.1.1 Perceptron
      • 17.1.2 Linear Separation
      • 17.1.3 Limitations
      • 17.1.4 Widrow-Ho® Rule
    • 17.2 Back-Propagation Learning Algorithm.
      • 17.2.1 Example: Prediction of 13C-NMR Shifts for Methyl Substituted Cyclohexanes
    • 17.3 Radial Basis Function Networks
      • 17.3.1 Theory
      • 17.3.2 Training Algorithms
      • 17.3.3 Algorithms for the Radial Basis Functions Selection
      • 17.3.4 Design of Training Data Set for the Calibration
      • Problem
      • 17.3.5 Example of Applications
    • Problems
  • 18 Hop¯eld Network
    • 18.1 Modeling.
      • 18.1.1 Network Completely Connected
      • 18.1.2 Leaning Process in a Hop¯eld Network
      • 18.1.3 Stability of States
    • 18.2 Application to Optimization Procedures
      • 18.2.1 Energy of Hop¯eld Network
      • 18.2.2 Traveling Salesman Problem (TSP)
      • 18.2.3 3D Structural Recognition
    • 18.3 Boltzmann Machine
    • Problems
  • 19 Kohonen Network
    • 19.1 Structure of SOM
    • 19.2 The Learning Process
      • 19.2.1 Initialization
      • 19.2.2 Training
      • 19.2.3 Variants
    • 19.3 Examples and Applications
      • 19.3.1 Mapping the 3D Sphere Surface onto a SOM Plane
      • 19.3.2 Application to Visual Classi¯cation of Gas Chromatographic Liquid Phase
    • Problem
  • 20 Genetic Algorithms and Their Applications in Chemistry
    • 20.1 Optimization and Techniques
    • 20.2 Simple Genetic Algorithm
      • 20.2.1 Population and Coding
      • 20.2.2 Fitness Function
      • 20.2.3 Operators
    • 20.3 Mathematical Fundamental
    • 20.4 Application of SGA to QSAR Problem
    • 20.5 Other Application of GA to Chemistry Problems
      • 20.5.1 Conformational Searching
      • 20.5.2 Other Applications
    • Problems
  • 21 Support Vector Machine (SVM)
    • 21.1 SVM in Classi¯cation
      • 21.1.1 Basic Principle
      • 21.1.2 Some Applications
    • 21.2 Regression SVM
      • 21.2.1 Basic Principle
      • 21.2.2 Further Developments
    • 21.3 Applications
    • Problems
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