| Title |
AutoDock VinaXB: implementation of XBSF, new empirical halogen bond scoring function, into AutoDock Vina
|
|---|---|
| Published in |
Journal of Cheminformatics, May 2016
|
| DOI | 10.1186/s13321-016-0139-1 |
| Pubmed ID | |
| Authors |
Mathew R. Koebel, Grant Schmadeke, Richard G. Posner, Suman Sirimulla |
| Abstract |
Halogen bonding has recently come to play as a target for lead optimization in rational drug design. However, most docking program don't account for halogen bonding in their scoring functions and are not able to utilize this new approach. In this study a new and improved halogen bonding scoring function (XBSF) is presented along with its implementation in the AutoDock Vina molecular docking software. This new improved program is termed as AutoDock VinaXB, where XB stands for the halogen bonding parameters that were added. XBSF scoring function is derived based on the X···A distance and C-X···A angle of interacting atoms. The distance term was further corrected to account for the polar flattening effect of halogens. A total of 106 protein-halogenated ligand complexes were tested and compared in terms of binding affinity and docking poses using Vina and VinaXB. VinaXB performed superior to Vina in the majority of instances. VinaXB was closer to native pose both above and below 2 Å deviation categories almost twice as frequently as Vina. Implementation of XBSF into AutoDock Vina has been shown to improve the accuracy of the docking result with regards to halogenated ligands. AutoDock VinaXB addresses the issues of halogen bonds that were previously being scored unfavorably due to repulsion factors, thus effectively lowering the output RMSD values. |
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 Kingdom | 1 | 33% |
| Germany | 1 | 33% |
| United States | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 131 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | <1% |
| India | 1 | <1% |
| Unknown | 129 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 24 | 18% |
| Student > Master | 24 | 18% |
| Student > Bachelor | 18 | 14% |
| Researcher | 16 | 12% |
| Student > Doctoral Student | 7 | 5% |
| Other | 21 | 16% |
| Unknown | 21 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 33 | 25% |
| Biochemistry, Genetics and Molecular Biology | 26 | 20% |
| Agricultural and Biological Sciences | 15 | 11% |
| Pharmacology, Toxicology and Pharmaceutical Science | 9 | 7% |
| Computer Science | 6 | 5% |
| Other | 15 | 11% |
| Unknown | 27 | 21% |
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 15 December 2020.
All research outputs
#3,616,933
of 22,870,727 outputs
Outputs from Journal of Cheminformatics
#355
of 837 outputs
Outputs of similar age
#62,723
of 334,246 outputs
Outputs of similar age from Journal of Cheminformatics
#6
of 14 outputs
Altmetric has tracked 22,870,727 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 837 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one has gotten more attention than average, scoring higher than 57% 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 334,246 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 81% 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 gotten more attention than average, scoring higher than 57% of its contemporaries.