| Title |
Ligand-based virtual screening interface between PyMOL and LiSiCA
|
|---|---|
| Published in |
Journal of Cheminformatics, September 2016
|
| DOI | 10.1186/s13321-016-0157-z |
| Pubmed ID | |
| Authors |
Athira Dilip, Samo Lešnik, Tanja Štular, Dušanka Janežič, Janez Konc |
| Abstract |
Ligand-based virtual screening of large small-molecule databases is an important step in the early stages of drug development. It is based on the similarity principle and is used to reduce the chemical space of large databases to a manageable size where chosen ligands can be experimentally tested. Ligand-based virtual screening can also be used to identify bioactive molecules with different basic scaffolds compared to already known bioactive molecules, thus having the potential to increase the structural variability of compounds. Here, we present an interface between the popular molecular graphics system PyMOL and the ligand-based virtual screening software LiSiCA available at http://insilab.org/lisica-plugin and demonstrate how this interface can be used in the early stages of drug discovery process.Graphical AbstractLigand-based virtual screening interface between PyMOL and LiSiCA. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 1 | 33% |
| France | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 33% |
| Scientists | 1 | 33% |
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 38 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 9 | 24% |
| Student > Ph. D. Student | 6 | 16% |
| Student > Doctoral Student | 4 | 11% |
| Other | 3 | 8% |
| Student > Master | 3 | 8% |
| Other | 6 | 16% |
| Unknown | 7 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 10 | 26% |
| Agricultural and Biological Sciences | 5 | 13% |
| Chemistry | 5 | 13% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 5% |
| Medicine and Dentistry | 2 | 5% |
| Other | 5 | 13% |
| Unknown | 9 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 22 September 2016.
All research outputs
#6,623,716
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#542
of 891 outputs
Outputs of similar age
#99,195
of 340,445 outputs
Outputs of similar age from Journal of Cheminformatics
#12
of 19 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 891 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one is in the 38th percentile – i.e., 38% 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 340,445 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 70% 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 is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.