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Ranking MOLGEN Structure Proposals by 13 C NMR Chemical Shift Prediction with ANALYZE (2002)
| Content Provider | CiteSeerX |
|---|---|
| Author | Meiler, Jens Meringer, Markus |
| Abstract | 86 Artificial neural networks are capable of predicting the 13 C chemical shifts of organic molecules nearly as fast as incremental methods while maintaining the accuracy of database methods. In this article, we apply a recently developed neural network (Meiler et. al., J. Chem. Inf. Comput. Sci. 2000, 40, 1169-1176), to the screening of large sets of molecules obtained by structure generators in the process of automated structure elucidation. Specifically, we apply the network to sets of structures generated by MOLGEN (Benecke et. al., Anal. Chim. Acta 1995, 314, 141-147) for ten randomly selected molecules of less than 13 non-hydrogen atoms. The computed 13 C NMR spectra are compared to the experimental spectrum; in all cases, the computed spectrum belonging to the example molecule yields a significantly smaller deviation to the experimental data then all other predicted spectra. This result suggests that the approach is suitable for automated structure prediction for organic molecules with up to 12 non-hydrogen atoms. |
| File Format | |
| Publisher Date | 2002-01-01 |
| Access Restriction | Open |
| Subject Keyword | Molgen Structure Proposal Nmr Chemical Shift Prediction Non-hydrogen Atom Organic Molecule Incremental Method Database Method Structure Generator Computed Spectrum Belonging Example Molecule Neural Network Large Set Experimental Spectrum Automated Structure Prediction Experimental Data Nmr Spectrum Automated Structure Elucidation Chemical Shift Artificial Neural Network |
| Content Type | Text |
