| Title |
Molecular representations in AI-driven drug discovery: a review and practical guide
|
|---|---|
| Published in |
Journal of Cheminformatics, September 2020
|
| DOI | 10.1186/s13321-020-00460-5 |
| Pubmed ID | |
| Authors |
Laurianne David, Amol Thakkar, Rocío Mercado, Ola Engkvist |
X Demographics
The data shown below were collected from the profiles of 72 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 12 | 17% |
| Japan | 7 | 10% |
| United Kingdom | 5 | 7% |
| Sweden | 4 | 6% |
| Spain | 3 | 4% |
| Switzerland | 2 | 3% |
| India | 2 | 3% |
| Israel | 1 | 1% |
| Netherlands | 1 | 1% |
| Other | 7 | 10% |
| Unknown | 28 | 39% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 35 | 49% |
| Scientists | 34 | 47% |
| Practitioners (doctors, other healthcare professionals) | 2 | 3% |
| Science communicators (journalists, bloggers, editors) | 1 | 1% |
Mendeley readers
The data shown below were compiled from readership statistics for 578 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 578 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 87 | 15% |
| Researcher | 78 | 13% |
| Student > Master | 54 | 9% |
| Student > Bachelor | 49 | 8% |
| Other | 24 | 4% |
| Other | 57 | 10% |
| Unknown | 229 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 105 | 18% |
| Computer Science | 49 | 8% |
| Biochemistry, Genetics and Molecular Biology | 40 | 7% |
| Chemical Engineering | 28 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 22 | 4% |
| Other | 90 | 16% |
| Unknown | 244 | 42% |
Attention Score in Context
This research output has an Altmetric Attention Score of 66. 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 25 February 2024.
All research outputs
#654,377
of 25,837,817 outputs
Outputs from Journal of Cheminformatics
#6
of 981 outputs
Outputs of similar age
#18,945
of 431,495 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 19 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 981 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.1. This one has done particularly well, scoring higher than 99% 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 431,495 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 19 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 94% of its contemporaries.