| Title |
Yada: a novel tool for molecular docking calculations
|
|---|---|
| Published in |
Perspectives in Drug Discovery and Design, August 2016
|
| DOI | 10.1007/s10822-016-9953-9 |
| Pubmed ID | |
| Authors |
S. Piotto, L. Di Biasi, R. Fino, R. Parisi, L. Sessa, S. Concilio |
| Abstract |
Molecular docking is a computational method employed to estimate the binding between a small ligand (the drug candidate) and a protein receptor that has become a standard part of workflow in drug discovery. Generally, when the binding site is known and a molecule is similar to known ligands, the most popular docking methods are rather accurate in the prediction of the geometry. Unfortunately, when the binding site is unknown, the blind docking analysis requires large computational resources and the results are often not accurate. Here we present Yada, a new tool for molecular docking that is capable to distribute efficiently calculations onto general purposes computer grid and that combines biological and structural information of the receptor. Yada is available for Windows and Linux and it is free to download at www.yada.unisa.it . |
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 % |
|---|---|---|
| Japan | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 27 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 30% |
| Professor > Associate Professor | 5 | 19% |
| Student > Master | 3 | 11% |
| Student > Doctoral Student | 1 | 4% |
| Student > Ph. D. Student | 1 | 4% |
| Other | 3 | 11% |
| Unknown | 6 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 8 | 30% |
| Computer Science | 5 | 19% |
| Agricultural and Biological Sciences | 4 | 15% |
| Biochemistry, Genetics and Molecular Biology | 3 | 11% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 7% |
| Other | 0 | 0% |
| Unknown | 5 | 19% |
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 24 September 2020.
All research outputs
#7,993,771
of 25,457,858 outputs
Outputs from Perspectives in Drug Discovery and Design
#408
of 949 outputs
Outputs of similar age
#115,628
of 349,887 outputs
Outputs of similar age from Perspectives in Drug Discovery and Design
#19
of 31 outputs
Altmetric has tracked 25,457,858 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 949 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has gotten more attention than average, scoring higher than 56% 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 349,887 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 65% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.