Journal article
Automated Spectrum Simulation Methods for Carbon-13 Nuclear Magnetic Resonance Spectroscopy Based on Database Retrieval and Model-Building Strategies
Journal of chemical information and computer sciences, Vol.37(2), pp.249-257
03/01/1997
DOI: 10.1021/ci9601731
Abstract
An automated method for the prediction of carbon-13 nuclear magnetic resonance (13C NMR) chemical shifts which involves the combined use of database retrieval and multivariate calibration methods is described. A commercial database of chemical structures and experimentally measured and assigned 13C NMR spectra is used to implement this methodology. The chemical environment of each of the carbons in the database is encoded as a vector, and Euclidean distance comparisons are made between these vectors and the similarly encoded environment of a target carbon whose chemical shift is to be predicted. This procedure yields a calibration set of carbon atoms with chemical environments similar to that of the target carbon. If the environment of the closest match is very similar to that of the target carbon, the predicted chemical shift is assigned as the chemical shift of that closest match (i.e., a direct chemical shift retrieval is performed). Otherwise, a chemical shift model based on spectra-structure relationships is built using the selected calibration set of carbons. The predicted chemical shift is then calculated with the computed model. The independent variables in the model are derived from numerical structural descriptors which describe some topological, geometrical, or electronic aspect of the chemical environment of the carbon atoms in the calibration set. Multiple linear regression, partial least-squares regression, and principal component regression are compared in terms of their effectiveness for use in building the chemical shift models. A series of experiments is performed to test the accuracy of direct chemical shift retrieval versus model building and to determine the optimal settings of several parameters that affect the model-building step. Based on a test set of 38 504 carbons, an overall mean deviation of 1.85 ppm between predicted and actual chemical shifts is achieved by use of direct retrieval alone, while the corresponding mean deviation based on a combination of direct retrieval and model building is 1.69 ppm.
Details
- Title: Subtitle
- Automated Spectrum Simulation Methods for Carbon-13 Nuclear Magnetic Resonance Spectroscopy Based on Database Retrieval and Model-Building Strategies
- Creators
- Robert C Schweitzer - Center for Intelligent Chemical Instrumentation, Department of Chemistry, Ohio University, Athens, Ohio 45701-2979Gary W Small - Center for Intelligent Chemical Instrumentation, Department of Chemistry, Ohio University, Athens, Ohio 45701-2979
- Resource Type
- Journal article
- Publication Details
- Journal of chemical information and computer sciences, Vol.37(2), pp.249-257
- DOI
- 10.1021/ci9601731
- ISSN
- 0095-2338
- eISSN
- 1520-5142
- Language
- English
- Date published
- 03/01/1997
- Academic Unit
- Chemistry
- Record Identifier
- 9984216573602771
Metrics
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