Commit 8cce02c4 authored by Jacob Durrant's avatar Jacob Durrant

Updated README.md.

parent 3dcb6126
......@@ -18,8 +18,8 @@ functions to aid in the identification of small-molecule ligands. The scoring
functions presented here, either on their own or used in conjunction with
other more traditional functions, may prove useful in drug-discovery projects.
Additional information about NNScore 1.0 can be found [in the original
paper](https://web.archive.org/web/20180730200710/http://pubs.acs.org/doi/full/10.1021/ci100244v).
NNScore 2.0 is described in [a separate
paper](http://pubs.acs.org/doi/full/10.1021/ci100244v). NNScore 2.0 is
described in [a separate
publication](https://pubs.acs.org/doi/abs/10.1021/ci2003889).
Note that NNScore 2.0 is not necessarily superior to NNScore 1.0. The best
......@@ -27,24 +27,27 @@ scoring function to use is highly system dependent. Including positive
controls (known inhibitors) in virtual screens is a useful way to identify
which scoring function is best suited to your needs.
If you use NNScore in your research, please cite the appropriate reference:
If you use NNScore in your research, **please cite the appropriate
reference**:
NNScore: A Neural-Network-Based Scoring Function for the Characterization of
Protein-Ligand Complexes. Jacob D. Durrant, J. Andrew McCammon. Journal of
Chemical Information and Modeling, 2010, 50 (10), pp 1865-1871.
{REFERENCE FOR NNSCORE 2.0 HERE}
NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function. Jacob D.
Durrant, Andrew McCammon. Journal of Chemical Information and Modeling, 2011,
51 (11), pp 2897-2903.
## Usage for Version 1.0 ##
NNScore 1.0 has been implemented as a python script. The program accepts the
following parameters:
NNScore 1.0 has been implemented as a [python](http://www.python.org/) script.
The program accepts the following parameters:
```text
-receptor <pdbqt filename>
-ligand <pdbqt filename>
-network <network filename>
-networks_dir <directory>
-receptor <pdbqt filename>
-ligand <pdbqt filename>
-network <network filename>
-networks_dir <directory>
```
Note: It is best to use multiple neural networks to judge ligand binding by
......@@ -56,7 +59,8 @@ files and specify the path to that directory using the -networks_dir
parameter.
Note: Only pdbqt files of the receptor and ligand are accepted. Scripts to
convert from pdb to pdbqt are included in the AutoDockTools package.
convert from pdb to pdbqt are included in the [AutoDockTools
package](http://autodock.scripps.edu/resources/adt).
Examples:
......@@ -81,29 +85,29 @@ python NeuroScore.py -receptor protease.pdbqt
## Usage for Version 2.0 ##
NNScore 2.0 has also been implemented as a python script. As demonstrated in
our paper {TITLE HERE}, NNScore 2.0 is not necessarily superior to NNScore
1.0. The best scoring function to use is highly system dependent. Including
positive controls (known inhibitors) in virtual screens is a useful way to
identify which scoring function is best suited to your needs.
NNScore 2.0 has also been implemented as a [python](http://www.python.org/)
script. As demonstrated in [our
paper](https://pubs.acs.org/doi/abs/10.1021/ci2003889), NNScore 2.0 is not
necessarily superior to NNScore 1.0. The best scoring function to use is
highly system dependent. Including positive controls (known inhibitors) in
virtual screens is a useful way to identify which scoring function is best
suited to your needs.
### REQUIREMENTS ###
Python: NNScore 2.0 has been tested using Python versions 2.6.5, 2.6.1, and
2.5.2 on Ubuntu 10.04.1 LTS, Mac OS X 10.6.8, and Windows XP Professional,
respectively. A copy of the Python interpreter can be downloaded from
http://www.python.org/getit/.
Python3: A copy of the Python interpreter can be downloaded from
[http://www.python.org/getit/](http://www.python.org/getit/).
AutoDock Vina 1.1.2: NNScore 2.0 uses AutoDock Vina 1.1.2 to obtain some
information about the receptor-ligand complex. Note that previous versions of
AutoDock Vina are not suitble. AutoDock Vina 1.1.2 can be downloaded from
http://vina.scripps.edu/download.html.
[http://vina.scripps.edu/download.html](http://vina.scripps.edu/download.html).
MGLTools: As receptor and ligand inputs, NNScore 2.0 accepts models in the
PDBQT format. Files in the more common PDB format can be converted to the
PDBQT format using scripts included in MGLTools (prepare_receptor4.py and
prepare_ligand4.py). MGLTools can be obtained from
http://mgltools.scripps.edu/downloads.
PDBQT format using scripts included in MGLTools (`prepare_receptor4.py` and
`prepare_ligand4.py`). MGLTools can be obtained from
[http://mgltools.scripps.edu/downloads](http://mgltools.scripps.edu/downloads).
### COMMAND-LINE PARAMETERS ###
......@@ -128,7 +132,7 @@ affinity using several metrics:
20 different ways by the scores assigned by each network.
2) The poses are ranked by the best score given by any of the 20 networks.
3) The poses are ranked by the average of the scores given by the 20 networks.
This is the recommended metric.
**This is the recommended metric.**
### EXAMPLE OF USAGE ###
......@@ -138,14 +142,20 @@ python NNScore2.py -receptor myreceptor.pdbqt -ligand myligand.pdbqt -vina_execu
## Download ##
All versions of NNScore are released under the GNU General Public License.
All versions of NNScore are released under the [GNU General Public
License](https://git.durrantlab.pitt.edu/jdurrant/nnscore2/blob/master/gpl-3.0.txt).
Your use of NNScore implies acceptance of the terms stipulated in that
license.
Download any version of NNScore from sourceforge.net
* Visit
[https://git.durrantlab.pitt.edu/jdurrant/nnscore1](https://git.durrantlab.pitt.edu/jdurrant/nnscore1)
to download the latest version of NNScore 1.
* Visit
[https://git.durrantlab.pitt.edu/jdurrant/nnscore2](https://git.durrantlab.pitt.edu/jdurrant/nnscore2)
to download the latest version of NNScore 2.
## Contact ##
If you have any questions, comments, or suggestions, please don't hesitate to
contact me, Jacob Durrant, at jdurrant [at] ucsd [dot] edu. I'd be happy to
help. :)
contact me, [Jacob Durrant](http://durrantlab.com), at durrantj [at] pitt
[dot] edu. I'd be happy to help. :)
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