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Enumeration method for tree-like chemical compounds with benzene rings and naphthalene rings by breadth-first search order

Overview of attention for article published in BMC Bioinformatics, March 2016
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Title
Enumeration method for tree-like chemical compounds with benzene rings and naphthalene rings by breadth-first search order
Published in
BMC Bioinformatics, March 2016
DOI 10.1186/s12859-016-0962-4
Pubmed ID
Authors

Jira Jindalertudomdee, Morihiro Hayashida, Yang Zhao, Tatsuya Akutsu

Abstract

Drug discovery and design are important research fields in bioinformatics. Enumeration of chemical compounds is essential not only for the purpose, but also for analysis of chemical space and structure elucidation. In our previous study, we developed enumeration methods BfsSimEnum and BfsMulEnum for tree-like chemical compounds using a tree-structure to represent a chemical compound, which is limited to acyclic chemical compounds only. In this paper, we extend the methods, and develop BfsBenNaphEnum that can enumerate tree-like chemical compounds containing benzene rings and naphthalene rings, which include benzene isomers and naphthalene isomers such as ortho, meta, and para, by treating a benzene ring as an atom with valence six, instead of a ring of six carbon atoms, and treating a naphthalene ring as two benzene rings having a special bond. We compare our method with MOLGEN 5.0, which is a well-known general purpose structure generator, to enumerate chemical structures from a set of chemical formulas in terms of the number of enumerated structures and the computational time. The result suggests that our proposed method can reduce the computational time efficiently. We propose the enumeration method BfsBenNaphEnum for tree-like chemical compounds containing benzene rings and naphthalene rings as cyclic structures. BfsBenNaphEnum was from 50 times to 5,000,000 times faster than MOLGEN 5.0 for instances with 8 to 14 carbon atoms in our experiments.

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Mendeley readers

Mendeley readers

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Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 30%
Student > Master 2 20%
Student > Bachelor 1 10%
Researcher 1 10%
Professor > Associate Professor 1 10%
Other 1 10%
Unknown 1 10%
Readers by discipline Count As %
Computer Science 7 70%
Agricultural and Biological Sciences 1 10%
Chemistry 1 10%
Unknown 1 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 March 2016.
All research outputs
#17,790,561
of 22,852,911 outputs
Outputs from BMC Bioinformatics
#5,941
of 7,292 outputs
Outputs of similar age
#202,999
of 298,399 outputs
Outputs of similar age from BMC Bioinformatics
#108
of 130 outputs
Altmetric has tracked 22,852,911 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,292 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
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We're also able to compare this research output to 130 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.