| Title |
Ligand-guided optimization of CXCR4 homology models for virtual screening using a multiple chemotype approach
|
|---|---|
| Published in |
Perspectives in Drug Discovery and Design, October 2010
|
| DOI | 10.1007/s10822-010-9393-x |
| Pubmed ID | |
| Authors |
Marco A. C. Neves, Sérgio Simões, M. Luisa Sá e Melo |
| Abstract |
CXCR4 is a G-protein coupled receptor for CXCL12 that plays an important role in human immunodeficiency virus infection, cancer growth and metastasization, immune cell trafficking and WHIM syndrome. In the absence of an X-ray crystal structure, theoretical modeling of the CXCR4 receptor remains an important tool for structure-function analysis and to guide the discovery of new antagonists with potential clinical use. In this study, the combination of experimental data and molecular modeling approaches allowed the development of optimized ligand-receptor models useful for elucidation of the molecular determinants of small molecule binding and functional antagonism. The ligand-guided homology modeling approach used in this study explicitly re-shaped the CXCR4 binding pocket in order to improve discrimination between known CXCR4 antagonists and random decoys. Refinement based on multiple test-sets with small compounds from single chemotypes provided the best early enrichment performance. These results provide an important tool for structure-based drug design and virtual ligand screening of new CXCR4 antagonists. |
Mendeley readers
The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 3% |
| United Kingdom | 1 | 2% |
| Portugal | 1 | 2% |
| Unknown | 60 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 14% |
| Student > Bachelor | 6 | 9% |
| Student > Master | 5 | 8% |
| Student > Ph. D. Student | 4 | 6% |
| Student > Doctoral Student | 2 | 3% |
| Other | 3 | 5% |
| Unknown | 35 | 55% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 9 | 14% |
| Agricultural and Biological Sciences | 6 | 9% |
| Biochemistry, Genetics and Molecular Biology | 3 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 5% |
| Medicine and Dentistry | 3 | 5% |
| Other | 3 | 5% |
| Unknown | 37 | 58% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 10 October 2023.
All research outputs
#3,815,396
of 25,457,858 outputs
Outputs from Perspectives in Drug Discovery and Design
#137
of 949 outputs
Outputs of similar age
#15,309
of 108,696 outputs
Outputs of similar age from Perspectives in Drug Discovery and Design
#2
of 9 outputs
Altmetric has tracked 25,457,858 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
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 done well, scoring higher than 83% 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 108,696 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 83% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 7 of them.