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X Demographics
Mendeley readers
Attention Score in Context
| Title |
ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database
|
|---|---|
| Published in |
Journal of Cheminformatics, June 2018
|
| DOI | 10.1186/s13321-018-0283-x |
| Pubmed ID | |
| Authors |
Jie Dong, Ning-Ning Wang, Zhi-Jiang Yao, Lin Zhang, Yan Cheng, Defang Ouyang, Ai-Ping Lu, Dong-Sheng Cao |
| Abstract |
Current pharmaceutical research and development (R&D) is a high-risk investment which is usually faced with some unexpected even disastrous failures in different stages of drug discovery. One main reason for R&D failures is the efficacy and safety deficiencies which are related largely to absorption, distribution, metabolism and excretion (ADME) properties and various toxicities (T). Therefore, rapid ADMET evaluation is urgently needed to minimize failures in the drug discovery process. Here, we developed a web-based platform called ADMETlab for systematic ADMET evaluation of chemicals based on a comprehensively collected ADMET database consisting of 288,967 entries. Four function modules in the platform enable users to conveniently perform six types of drug-likeness analysis (five rules and one prediction model), 31 ADMET endpoints prediction (basic property: 3, absorption: 6, distribution: 3, metabolism: 10, elimination: 2, toxicity: 7), systematic evaluation and database/similarity searching. We believe that this web platform will hopefully facilitate the drug discovery process by enabling early drug-likeness evaluation, rapid ADMET virtual screening or filtering and prioritization of chemical structures. The ADMETlab web platform is designed based on the Django framework in Python, and is freely accessible at http://admet.scbdd.com/ . |
X Demographics
The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 2 | 14% |
| United Kingdom | 2 | 14% |
| Russia | 1 | 7% |
| United States | 1 | 7% |
| Unknown | 8 | 57% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 50% |
| Scientists | 6 | 43% |
| Science communicators (journalists, bloggers, editors) | 1 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 590 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 590 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 66 | 11% |
| Student > Bachelor | 65 | 11% |
| Student > Ph. D. Student | 62 | 11% |
| Researcher | 54 | 9% |
| Student > Doctoral Student | 21 | 4% |
| Other | 82 | 14% |
| Unknown | 240 | 41% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 86 | 15% |
| Biochemistry, Genetics and Molecular Biology | 80 | 14% |
| Pharmacology, Toxicology and Pharmaceutical Science | 54 | 9% |
| Agricultural and Biological Sciences | 26 | 4% |
| Unspecified | 19 | 3% |
| Other | 71 | 12% |
| Unknown | 254 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 06 March 2023.
All research outputs
#2,413,377
of 24,261,860 outputs
Outputs from Journal of Cheminformatics
#217
of 893 outputs
Outputs of similar age
#49,637
of 333,061 outputs
Outputs of similar age from Journal of Cheminformatics
#4
of 14 outputs
Altmetric has tracked 24,261,860 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 893 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one has done well, scoring higher than 75% 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 333,061 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 85% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.