scholarly journals Chirality Induction in Bioorganometallic Conjugates

Inorganics ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 111
Author(s):  
Toshiyuki Moriuchi ◽  
Satoshi Ohmura ◽  
Takayo Moriuchi-Kawakami

Considerable attention has been given to the research field of bioorganometallic chemistry, which is a hybrid chemistry field between biology and organometallic chemistry. The introduction of biomolecules, which have hydrogen bonding sites and chiral centers, into organometallic compounds is a promising strategy to construct chirality-organized bioorganometallic conjugates. This feature paper sketches an outline of induction of helical chirality into bioorganometallic conjugates by the control of a torsional twist of the organometallic moiety. Topics covered included control of the helical chirality of 1,n′-disubstituted ferrocene moieties in ferrocene-dipeptide conjugates, and the chirality induction of the Au(I)–Au(I) axis in the dinuclear organogold(I)-uracil conjugates.

Author(s):  
Toshiyuki Moriuchi ◽  
Satoshi D. Ohmura ◽  
Takayo Moriuchi-Kawakami

Considerable attention has been given to the research field of bioorganometallic chemistry, which is a hybrid chemistry field between biology and organometallic chemistry. The introduction of biomolecules, which have hydrogen bonding sites and chiral centers, into organometallic compounds is considered to be a promising strategy to construct chirality-organized bioorganometallic conjugates. This feature paper sketches an outline of induction of helical chirality into bioorganometallic conjugates. Topics covered include control of the helical chirality of 1,n’-disubstituted ferrocene moieties in ferrocene-dipeptide conjugates and the chirality induction of the Au(I)–Au(I) axis in the dinuclear organogold(I)-uracil conjugates.


Catalysts ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 646
Author(s):  
Victorio Cadierno

The use of organometallic compounds in organic chemistry is one of the cornerstones of the modern synthetic methodology for the activation and generation of new bonds in a molecule [...]


1999 ◽  
Vol 121 (4) ◽  
pp. 754-759 ◽  
Author(s):  
Tadashi Mizutani ◽  
Shigeyuki Yagi ◽  
Tomoko Morinaga ◽  
Tetsutaro Nomura ◽  
Toru Takagishi ◽  
...  

ChemInform ◽  
2010 ◽  
Vol 32 (24) ◽  
pp. no-no
Author(s):  
Herbert W. Roesky ◽  
Mrinalini G. Walawakar ◽  
Ramaswamy Murugavel

1998 ◽  
Vol 63 (24) ◽  
pp. 8769-8784 ◽  
Author(s):  
Tadashi Mizutani ◽  
Shigeyuki Yagi ◽  
Atsushi Honmaru ◽  
Shinji Murakami ◽  
Masaru Furusyo ◽  
...  

ChemPhysChem ◽  
2017 ◽  
Vol 18 (15) ◽  
pp. 1987-1991 ◽  
Author(s):  
Corina H. Pollok ◽  
Qi Zhang ◽  
Konrad Tiefenbacher ◽  
Christian Merten

2021 ◽  
Author(s):  
Eliseu Guimarães ◽  
Daniela Vianna ◽  
Aline Paes ◽  
Alexandre Plastino

Sentiment analysis in tweets is a research field of great importance, mainly due to the popularity of Twitter. However, collecting and annotating tweets is an expensive and time-consuming task, making that some domains have only a limited set of labeled data. A promising strategy to handle this issue is to leverage labeled domains rich in data to select instances that enrich target datasets. This paper proposes different strategies for selecting instances from a set of labeled source datasets in order to improve the performance of classifiers trained only with the target dataset. Different approaches are proposed, including similarity metrics and variations in the number of selected instances. The results show that the size of the training set plays an essential role in the predictive capacity of the classifier. Furthermore, the results point out the importance of taking into account diversity criteria when selecting the instances.


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