For the normative version of our publication list see Christoph Steinbeck‘s ORCID profile.
161.
Steinbeck, C; Schneider, C; Rotscheidt, K; Breitmaier, E
A 4-methyl-7-hydroxyphthalide glycoside and other constituents from Quillaja saponaria molina. Journal Article
In: Phytochemistry, vol. 40, no. 4, pp. 1313–1315, 1995.
@article{Steinbeck:1995ve,
title = {A 4-methyl-7-hydroxyphthalide glycoside and other constituents from Quillaja saponaria molina.},
author = {Steinbeck, C and Schneider, C and Rotscheidt, K and Breitmaier, E},
url = {http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=7492375&retmode=ref&cmd=prlinks},
year = {1995},
date = {1995-11-01},
journal = {Phytochemistry},
volume = {40},
number = {4},
pages = {1313--1315},
abstract = {A so far unknown 4-methyl-7-hydroxyphthalideglycoside has been isolated from the methanol extract of the bark of Quillaja saponaria molina. Its structure has been established from NMR experiments as 7-O(-)[beta-glucopyranosyl-(1-->6)-beta-arabinopyranosyl]-7-hydrox y-4-methy l -1[3H]-isobenzofuranone. Two known compounds, 3,4,5-trimethoxyphenyl-beta-D-glucopyranoside and lyoniresinol-3 alpha-O-beta-D-glycopyranoside were also identified.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A so far unknown 4-methyl-7-hydroxyphthalideglycoside has been isolated from the methanol extract of the bark of Quillaja saponaria molina. Its structure has been established from NMR experiments as 7-O(-)[beta-glucopyranosyl-(1-->6)-beta-arabinopyranosyl]-7-hydrox y-4-methy l -1[3H]-isobenzofuranone. Two known compounds, 3,4,5-trimethoxyphenyl-beta-D-glucopyranoside and lyoniresinol-3 alpha-O-beta-D-glycopyranoside were also identified.
162.
Zhang, G L; Rucker, G; Breitmaier, E; Nieger, M; Mayer, R; Steinbeck, C
Alkaloids from Dactylicapnos torulosa Journal Article
In: Phytochemistry, vol. 40, no. 1, pp. 299–305, 1995.
@article{zhang1995alkaloids,
title = {Alkaloids from Dactylicapnos torulosa},
author = {Zhang, G L and Rucker, G and Breitmaier, E and Nieger, M and Mayer, R and Steinbeck, C},
url = {http://dx.doi.org/10.1016/0031-9422(95)00192-A},
doi = {10.1016/0031-9422(95)00192-A},
year = {1995},
date = {1995-01-01},
journal = {Phytochemistry},
volume = {40},
number = {1},
pages = {299--305},
publisher = {Elsevier},
abstract = {Phytochemical investigation of Dactylicapnos torulosa yielded two known compounds,(-)-cis-N-methyl-stylopiumchloride and hydrastinine chloride; and five new alkaloids, dactyline, 8-hydroxydihydrosanguinarine and dactylidine, as well as dactylicapnosine and dactylicapnosinine with novel C-N-O-C moieties. All structures were elucidated by spectroscopical methods. The structure of dactylicapnosine was also determined by single crystal X-ray diffraction analysis. [References: 18]},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Phytochemical investigation of Dactylicapnos torulosa yielded two known compounds,(-)-cis-N-methyl-stylopiumchloride and hydrastinine chloride; and five new alkaloids, dactyline, 8-hydroxydihydrosanguinarine and dactylidine, as well as dactylicapnosine and dactylicapnosinine with novel C-N-O-C moieties. All structures were elucidated by spectroscopical methods. The structure of dactylicapnosine was also determined by single crystal X-ray diffraction analysis. [References: 18]
163.
Rajan, Kohulan; Zielesny, Achim; Steinbeck, Christoph
DECIMER 1.0: deep learning for chemical image recognition using transformers Journal Article
In: J Cheminform, vol. 13, no. 1, pp. 61, 0000, ISSN: 1758-2946.
@article{pmid34404468,
title = {DECIMER 1.0: deep learning for chemical image recognition using transformers},
author = {Kohulan Rajan and Achim Zielesny and Christoph Steinbeck},
doi = {10.1186/s13321-021-00538-8},
issn = {1758-2946},
journal = {J Cheminform},
volume = {13},
number = {1},
pages = {61},
abstract = {The amount of data available on chemical structures and their properties has increased steadily over the past decades. In particular, articles published before the mid-1990 are available only in printed or scanned form. The extraction and storage of data from those articles in a publicly accessible database are desirable, but doing this manually is a slow and error-prone process. In order to extract chemical structure depictions and convert them into a computer-readable format, Optical Chemical Structure Recognition (OCSR) tools were developed where the best performing OCSR tools are mostly rule-based. The DECIMER (Deep lEarning for Chemical ImagE Recognition) project was launched to address the OCSR problem with the latest computational intelligence methods to provide an automated open-source software solution. Various current deep learning approaches were explored to seek a best-fitting solution to the problem. In a preliminary communication, we outlined the prospect of being able to predict SMILES encodings of chemical structure depictions with about 90% accuracy using a dataset of 50-100 million molecules. In this article, the new DECIMER model is presented, a transformer-based network, which can predict SMILES with above 96% accuracy from depictions of chemical structures without stereochemical information and above 89% accuracy for depictions with stereochemical information.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The amount of data available on chemical structures and their properties has increased steadily over the past decades. In particular, articles published before the mid-1990 are available only in printed or scanned form. The extraction and storage of data from those articles in a publicly accessible database are desirable, but doing this manually is a slow and error-prone process. In order to extract chemical structure depictions and convert them into a computer-readable format, Optical Chemical Structure Recognition (OCSR) tools were developed where the best performing OCSR tools are mostly rule-based. The DECIMER (Deep lEarning for Chemical ImagE Recognition) project was launched to address the OCSR problem with the latest computational intelligence methods to provide an automated open-source software solution. Various current deep learning approaches were explored to seek a best-fitting solution to the problem. In a preliminary communication, we outlined the prospect of being able to predict SMILES encodings of chemical structure depictions with about 90% accuracy using a dataset of 50-100 million molecules. In this article, the new DECIMER model is presented, a transformer-based network, which can predict SMILES with above 96% accuracy from depictions of chemical structures without stereochemical information and above 89% accuracy for depictions with stereochemical information.
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