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X Demographics
Mendeley readers
Attention Score in Context
| Title |
In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts
|
|---|---|
| Published in |
Frontiers in Chemistry, February 2018
|
| DOI | 10.3389/fchem.2018.00030 |
| Pubmed ID | |
| Authors |
Hongbin Yang, Lixia Sun, Weihua Li, Guixia Liu, Yun Tang |
| Abstract |
During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 400 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 400 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 55 | 14% |
| Student > Bachelor | 53 | 13% |
| Student > Ph. D. Student | 50 | 13% |
| Student > Master | 42 | 11% |
| Student > Doctoral Student | 14 | 4% |
| Other | 50 | 13% |
| Unknown | 136 | 34% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 64 | 16% |
| Biochemistry, Genetics and Molecular Biology | 54 | 14% |
| Pharmacology, Toxicology and Pharmaceutical Science | 45 | 11% |
| Computer Science | 21 | 5% |
| Agricultural and Biological Sciences | 15 | 4% |
| Other | 47 | 12% |
| Unknown | 154 | 39% |
Attention Score in Context
This research output has an Altmetric Attention Score of 19. 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 11 June 2021.
All research outputs
#1,849,852
of 24,226,848 outputs
Outputs from Frontiers in Chemistry
#78
of 6,382 outputs
Outputs of similar age
#40,772
of 334,974 outputs
Outputs of similar age from Frontiers in Chemistry
#5
of 99 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,382 research outputs from this source. They receive a mean Attention Score of 2.2. This one has done particularly well, scoring higher than 98% 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 334,974 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 87% of its contemporaries.
We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.