Name
Abstract | ||
|---|---|---|
Nine different artificial neural networks were trained with the spherically encoded chemical environments of more than 500 000 carbon atoms to predict their C-13 NMR chemical shifts. Based on these results the: PC-program "C_shift" was developed which allows the calculation:of the C-13 NMR spectra of any proposed molecular structure consisting of the covalently bonded elements C, H, N, O, P, S and the halogens. Results were obtained with a mean deviation as low as 1.8 ppm; this accuracy is equivalent to a determination on the basis of a large database but, in a time as short as known from increment calculations, was demonstrated exemplary using the natural agent epothilone A. The artificial neural networks allow simultaneously a precise and fast prediction of a large number of C-13 NMR spectra, needed for high throughout NMR and screening of a substance or spectra libraries. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1021/ci000021c | JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES |
Keywords | Field | DocType |
chemical shift,artificial neural network,high throughput,mean deviation,molecular structure | Analytical chemistry,Molecule,Computational chemistry,Atom,Carbon-13 NMR,Chemistry,Spectral line,Halogen,Chemical shift,Covalent bond,Artificial neural network | Journal |
Volume | Issue | ISSN |
40 | 5 | 0095-2338 |
Citations | PageRank | References |
1 | 0.40 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| J Meiler | 1 | 42 | 11.15 |
| R Meusinger | 2 | 4 | 0.92 |
| M Will | 3 | 3 | 0.86 |