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Mendeley readers
Attention Score in Context
| Chapter title |
Linear Interaction Energy: Method and Applications in Drug Design
|
|---|---|
| Chapter number | 20 |
| Book title |
Computational Drug Discovery and Design
|
| Published in |
Methods in molecular biology, January 2012
|
| DOI | 10.1007/978-1-61779-465-0_20 |
| Pubmed ID | |
| Book ISBNs |
978-1-61779-464-3, 978-1-61779-465-0
|
| Authors |
Hugo Gutiérrez-de-Terán, Johan Åqvist, Gutiérrez-de-Terán, Hugo, Åqvist, Johan |
| Abstract |
A broad range of computational methods exist for the estimation of ligand-protein binding affinities. In this chapter we will provide a guide to the linear interaction energy (LIE) method for binding free energy calculations, focusing on the drug design problem. The method is implemented in combination with molecular dynamics (MD) sampling of relevant conformations of the ligands and complexes under consideration. The detailed procedure for MD sampling is followed by key notes in order to properly analyze such sampling and obtain sufficiently accurate estimations of ligand-binding affinities. |
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 % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 4 | 4% |
| United States | 1 | 1% |
| Sri Lanka | 1 | 1% |
| Italy | 1 | 1% |
| Unknown | 88 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 21% |
| Researcher | 15 | 16% |
| Student > Master | 13 | 14% |
| Student > Bachelor | 9 | 9% |
| Student > Doctoral Student | 7 | 7% |
| Other | 12 | 13% |
| Unknown | 19 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 26 | 27% |
| Agricultural and Biological Sciences | 13 | 14% |
| Biochemistry, Genetics and Molecular Biology | 12 | 13% |
| Medicine and Dentistry | 6 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 4% |
| Other | 10 | 11% |
| Unknown | 24 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 02 October 2014.
All research outputs
#15,306,972
of 22,765,347 outputs
Outputs from Methods in molecular biology
#5,319
of 13,090 outputs
Outputs of similar age
#163,408
of 244,307 outputs
Outputs of similar age from Methods in molecular biology
#264
of 473 outputs
Altmetric has tracked 22,765,347 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,090 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 45th percentile – i.e., 45% 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 244,307 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 473 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.