| Title |
An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential
|
|---|---|
| Published in |
Journal of Cheminformatics, November 2011
|
| DOI | 10.1186/1758-2946-3-51 |
| Pubmed ID | |
| Authors |
Patrick McCarren, Clayton Springer, Lewis Whitehead |
| Abstract |
In drug discovery, a positive Ames test for bacterial mutation presents a significant hurdle to advancing a drug to clinical trials. In a previous paper, we discussed success in predicting the genotoxicity of reagent-sized aryl-amines (ArNH2), a structure frequently found in marketed drugs and in drug discovery, using quantum mechanics calculations of the energy required to generate the DNA-reactive nitrenium intermediate (ArNH:+). In this paper we approach the question of what molecular descriptors could improve these predictions and whether external data sets are appropriate for further training. |
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 % |
|---|---|---|
| Germany | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 3% |
| Germany | 1 | 1% |
| France | 1 | 1% |
| Portugal | 1 | 1% |
| Austria | 1 | 1% |
| Romania | 1 | 1% |
| Japan | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 69 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 21 | 27% |
| Student > Bachelor | 18 | 23% |
| Student > Ph. D. Student | 12 | 15% |
| Other | 7 | 9% |
| Student > Postgraduate | 4 | 5% |
| Other | 8 | 10% |
| Unknown | 8 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 27 | 35% |
| Pharmacology, Toxicology and Pharmaceutical Science | 8 | 10% |
| Medicine and Dentistry | 7 | 9% |
| Biochemistry, Genetics and Molecular Biology | 6 | 8% |
| Agricultural and Biological Sciences | 6 | 8% |
| Other | 11 | 14% |
| Unknown | 13 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 03 March 2022.
All research outputs
#7,107,409
of 23,243,271 outputs
Outputs from Journal of Cheminformatics
#578
of 860 outputs
Outputs of similar age
#63,846
of 241,770 outputs
Outputs of similar age from Journal of Cheminformatics
#20
of 21 outputs
Altmetric has tracked 23,243,271 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 860 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
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 241,770 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.