# Welcome to QuickVina
This is the source code repository and the temporary website for both **QuickVina 2** and **QuickVina-W**, until I release a new version and build new nice website 
The new site is coming sooo soon. Stay tuned. :-)

There are two tools to use

QuickVina 2
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Accurately speed up AutoDock Vina, the famous molecular docking tool.

Quick Vina 2 is a fast and accurate molecular docking tool, attained at accurately accelerating AutoDock Vina. It was tested against 195 protein–ligand complexes that compose the core set of the 2014 release of the PDBbind using default exhaustiveness level of 8, QVina 2 successfully attained up to 20.49-fold acceleration over Vina. The Pearson’s correlation coefficient between Vina’s QVina 2’s binding energy was 0.967 for the first predicted mode and 0.911 for the sum of all predicted modes.

It is also witnessed that QVina 2 is more accurate than GOLD 5.2 and is only slightly less accurate than Dock 6.6. This shows that QVina 2 has paved the way for some high-throughput and sufficiently accurate virtual screening of molecular libraries. This in turn brings great value to the fragment-based computer-aided drug design.

To cite QuickVina 2 please cite:

__"Fast, Accurate, and Reliable Molecular Docking with QuickVina 2"__
Amr Alhossary, Stephanus Daniel Handoko, Yuguang Mu, and Chee-Keong Kwoh. Bioinformatics (2015) 31 (13): 2214-2216. [DOI:10.1093/bioinformatics/btv082](https://doi.org/10.1093/bioinformatics/btv082)



QuickVina-W
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Adding the ability of __Blind Docking__ to QuickVina 2.

_QuickVina-W_ is __faster__ than _QuickVina 2_ and __more accurate__ than _AutoDock Vina_.

### important note:
* If you Don't know the Docking site, then QuickVina-W is your choice with ability to dock _WIDE_ search box.
* However if you _know_ the target search box, we recommend that you use QuickVina 2.


To cite QuickVina-W please cite:

__"Protein-Ligand Blind Docking Using QuickVina-W With Inter-Process Spatio-Temporal Integration"__
Nafisa M. Hassan, Amr A. Alhossary, Yuguang Mu & Chee-Keong Kwoh. Nature Scientific Reports 7(1) (2017). [DOI:10.1038/s41598-017-15571-7](http://dx.doi.org/10.1038/s41598-017-15571-7)
