Data Correlations with Chemical Structure

ACES II Anharmonic Molecular Force Fields Bench-mark Studies on Small Molecules Complete Active Space Self-consistent Field (CASSCF) Second-order Perturbation Theory (CASPT2) Configuration Interaction Core-Valence Correlation Effects Coupled-cluster Theory Density Functional Theory (DFT), Hartree-Fock (HF), and the Self-consistent Field G2 Theory Heats of Formation Hybrid Methods Hydrogen Bonding 1 M0ller-Plesset Perturbation Theory NMR Data Correlation with Chemical Structure Photochemistry Proton Affinities r 2 Dependent Wave-functions Rates of Chemical Reactions Reaction Path Following Reaction Path Hamiltonian and its Use for Investigating Reaction Mechanisms Spectroscopy Computational... 

Basis Sets Correlation Consistent Sets Carbocation Force Fields Coupled-cluster Theory Enthalpies of Hydrogenation G2 Theory Heats of Formation Hyperconjugation NMR Chemical Shift Computation Structural Applications NMR Data Correlation with Chemical Structure Proton Affinities. 

Spectral and X-ray crystallographic data are described in Ref. 1 and in Infrared Data Correlations with Chemical Structure NMR Data Correlation with Chemical Structure and NMR Refinement. For toxicology and biological properties or environmental and hazard or reaction data refer to Chemical Safety Information Databases or Environmental Information Databases. Chemical and physical properties databases will be presented in this article. 

Chemical Safety Information Databases Environmental Information Databases Infrared Data Correlations with Chemical Structure NMR Data Correlation with Chemical Structure NMR Refinement Online Databases in Chemistry Reaction Databases. 

Exploratory analysis of spectral data by PCA, PLS, cluster analysis, or Kohonen mapping tries to get an insight into the spectral data structure and into hidden factors, as well as to find clusters of similar spectra that can be interpreted in terms of similar chemical structures. Classification methods, such as LDA. PLS, SIMCA, KNN classification, and neural networks, have been used to generate spectral classifiers for an automatic recognition of structural properties from spectral data. The multivariate methods mostly used for spectra prediction (mainly NMR. rarely IR) are neural networks. Table 6 contains a summary of recent works in this field (see Infrared Data Correlations with Chemical Structure). 

Algorithms Infrared Data Correlations with Chemical Structure Infrared Spectra Interpretation by the Characteristic Frequency Approach Machine Learning Techniques in Chemistry Molecular Models Visualization Neural Networks in Chemistry NMR Data Correlation with Chemical Structure Partial Least Squares Projections to Latent Structures (PLS) in Chemistry Shape Analysis Spectroscopic Databases Spectroscopy Computational Methods Structure Determination by Computer-based Spectrum Interpretation Zeolites Applications of Computational Methods. 

INFRARED DATA CORRELATIONS WITH CHEMICAL STRUCTURE... 

When the structure of a newly synthesized compound has to be established, first the probable molecular fragments are detected using their characteristic features in spectra of different nature (IR, NMR, MS, etc.) and then an attempt to build all feasible structures is made. For this purpose, currently available expert systems are used (see Infrared Data Correlations with Chemical Structure NMR Data Correlation with Chemical Structure and Structure Determination by Computer-based Spectrum Interpretation). 

Chemometrics Multivariate View on Chemical Problems Combined Quantum Mechanics and Molecular Mechanics Approaches to Chemical and Biochemical Reactivity Environmental Chemistry QSAR Infrared Data Correlations with Chemical Structure Quantitative Structure-Activity Relationships in Drug Design Quantitative Structure-Property Relationships (QSPR). 

Density Functional Applications Density Functional Theory (DFT), Hartree-Fock (HF), and the Self-consistent Field NMR Chemical Shift Computation Ab Initio NMR Data Correlation with Chemical Structure NMR of Transition Metal Compounds. 

Infrared Data Correlations with Chemical Structure Infrared Spectra Interpretation by the Characteristic Frequency Approach Neural Networks in Chemistry NMR Chemical Shift Computation Ab Initio NMR Chemical Shift... 

Data Correlation with Chemical Structure NMR Refinement Nucleic Acid Conformation and Flexibility Modeling Using Molecular Mechanics Nucleic Acid Force Fields Nucleic Acids Qualitative Modeling Object-oriented Programming. 

In another approach (see NMR Data Correlation with Chemical Structure),three-dimensional (3D) substructure descriptors are used as a basis for the prediction. In this case the distribution function is simplified and a subsequent analysis is not required. This extension of the HOSE code is based on the ability to count the total number of steric interactions over a three-, four-, and five-bond sphere and additional parameters to describe the axial and equatorial substitution pattern for each atom. In the simple case of cis/trans ksomerism the number of interactions is calculated by means of the display coordinates. More complex situations are described by up/down bonds in this case the calculation of the number of steric interactions for each atom is performed by comparison of the query structure with carefully selected references from a library of ring skeletons. Figure 6 exemplifies the method. The discussion of the stereochemical effects is focused on the two diastereotopic methyl groups at position 4. The experimental shift values are 22.0 and 33.6 ppm. The prediction without stereodescriptors results in 27.4 ppm for both atoms (Figure 6, upper part). If the stereochemical effects are considered, the predicted shifts (21.5 and 33.5 ppm) deviate insignificantly from the experimental values (Figure 6, bottom). 

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