Commit 21aab246 authored by Jacob Durrant's avatar Jacob Durrant

Updated version numbers.

parent 8b8dad9f
......@@ -17,15 +17,14 @@ the brain. Computer-aided drug design depends on fast and accurate scoring
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](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).
Additional information about NNScore 1 can be found [in the original
paper](http://pubs.acs.org/doi/full/10.1021/ci100244v). NNScore 2 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
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.
Note that NNScore 2 is not necessarily superior to NNScore 1. 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.
If you use NNScore in your research, **please cite the appropriate
reference**:
......@@ -40,7 +39,7 @@ Durrant, Andrew McCammon. Journal of Chemical Information and Modeling, 2011,
## Usage for Version 1.X ##
NNScore 1.0 has been implemented as a [python](http://www.python.org/) script.
NNScore 1 has been implemented as a [python](http://www.python.org/) script.
The program accepts the following parameters:
```text
......@@ -88,24 +87,24 @@ python NeuroScore.py -receptor protease.pdbqt
## Usage for Version 2.X ##
NNScore 2.0 has also been implemented as a [python](http://www.python.org/)
NNScore 2s 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.
paper](https://pubs.acs.org/doi/abs/10.1021/ci2003889), NNScore 2 is not
necessarily superior to NNScore 1. 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 ###
* 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
* AutoDock Vina 1.1.2: NNScore 2 uses AutoDock Vina 1.1.2 to obtain some
information about the receptor-ligand complex. Note that previous versions
of AutoDock Vina are not suitable. AutoDock Vina 1.1.2 can be downloaded
from
[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
* MGLTools: As receptor and ligand inputs, NNScore 2s 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
......@@ -122,13 +121,13 @@ suited to your needs.
typically containing multiple poses, are also permitted.
`-vina_executable`: The location of the AutoDock Vina 1.1.2 executable. If you
don't wish to specify the location of this file every time you use NNScore
2.0, simply edit the `vina_executable` variable defined near the beginning of
the NNScore2.py script.
don't wish to specify the location of this file every time you use NNScore 2,
simply edit the `vina_executable` variable defined near the beginning of the
NNScore2.py script.
### Program Output ###
NNScore 2.0 evaluates each of the ligand poses contained in the file specified
NNScore 2 evaluates each of the ligand poses contained in the file specified
by the -ligand tag using 20 distinct neural-network scoring functions. The
program then seeks to identify which of the poses has the highest predicted
affinity using several metrics:
......
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