| Title |
Maximum common subgraph isomorphism algorithms for the matching of chemical structures
|
|---|---|
| Published in |
Perspectives in Drug Discovery and Design, July 2002
|
| DOI | 10.1023/a:1021271615909 |
| Pubmed ID | |
| Authors |
John W. Raymond, Peter Willett |
| Abstract |
The maximum common subgraph (MCS) problem has become increasingly important in those aspects of chemoinformatics that involve the matching of 2D or 3D chemical structures. This paper provides a classification and a review of the many MCS algorithms, both exact and approximate, that have been described in the literature, and makes recommendations regarding their applicability to typical chemoinformatics tasks. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 272 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 5 | 2% |
| United States | 4 | 1% |
| United Kingdom | 2 | <1% |
| Spain | 2 | <1% |
| Australia | 2 | <1% |
| Austria | 2 | <1% |
| India | 2 | <1% |
| South Africa | 1 | <1% |
| France | 1 | <1% |
| Other | 9 | 3% |
| Unknown | 242 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 91 | 33% |
| Researcher | 54 | 20% |
| Student > Master | 30 | 11% |
| Student > Bachelor | 23 | 8% |
| Other | 15 | 6% |
| Other | 36 | 13% |
| Unknown | 23 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 98 | 36% |
| Chemistry | 47 | 17% |
| Agricultural and Biological Sciences | 31 | 11% |
| Engineering | 18 | 7% |
| Biochemistry, Genetics and Molecular Biology | 15 | 6% |
| Other | 33 | 12% |
| Unknown | 30 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 26 September 2021.
All research outputs
#5,264,158
of 25,457,297 outputs
Outputs from Perspectives in Drug Discovery and Design
#237
of 949 outputs
Outputs of similar age
#8,164
of 47,981 outputs
Outputs of similar age from Perspectives in Drug Discovery and Design
#1
of 3 outputs
Altmetric has tracked 25,457,297 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 949 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has gotten more attention than average, scoring higher than 74% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 47,981 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them