Merge branch 'master' of https://github.com/sirimullalab/DLSCORE

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 Sirimulla Research Lab
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -9,7 +9,7 @@ DLSCORE is an ensemble of neural networks, trained on the recent release of the
 
 If you have all the prerequisites, you can move on to the testing stage. For the sake of simplicity, we included 5 folders with a protein and ligand each (in a .pdbqt format). Make sure you have a folder containing the following files/directories:
 
-- dlscore.py
+- dlscore2.py
 - dl_networks_04/
 - autodock_vina_1_1_2_linux_x86/
 - 1gvx/ 
@@ -32,33 +32,34 @@ To install python 3.5, please visit this [link](https://www.python.org/downloads
 
 We recommend to use the proteins and ligands (in a .pdbqt format) from [tests](https://github.com/sirimullalab/DLSCORE/tree/master/tests) to try DLSCORE. When running it, it should look like this:
 
-`python dlscore.py -r receptor1/receptor1.pdbqt -l ligand1/ligand1.pdbqt -v path/to/vina -n 10`
+`python dlscore.py -r receptor1/receptor1.pdbqt -l ligand1/ligand1.pdbqt -v path/to/vina -n 10 -o test`
 
 Where:
 ````
 -r stands for the receptor or protein (MUST BE in a pdbqt format)
 -l stands for the ligand (MUST BE in a pdbqt format)
 -v is the path to Autodock Vina
 -n is the desired number of networks
+-o is the name of the output file (no need to add a .csv extension)
 ````
 
 We recommend setting `-n` to 10, as it has been shown to give the optimum results (highest Pearson, Spearman and Kendall correlation coefficients, and lowest RMSE and MAE values).
 
 The command to run DLSCORE should look this:
 
 `
-python dlscore.py -r 1erb/1erb_protein.pdbqt -l 1erb/1erb_ligand.pdbqt -v autodock_vina_1_1_2_linux_x86/bin/vina -n <number_of_networks>
+python dlscore.py -r 1erb/1erb_protein.pdbqt -l 1erb/1erb_ligand.pdbqt -v autodock_vina_1_1_2_linux_x86/bin/vina -n <number_of_networks> -o output_file
 `
 
 And the output (with some parameter and warning messages displayed) should be:
 
 `
-[{'dlscore': [7.0548329, 6.6804061, 7.331718, 7.5543647, 7.4937844, 7.2915564, 6.8153844, 7.3344746, 6.8370795, 6.8463974], 'nnscore': [7.528647808595601, 6.841528068839551, 8.970434997068462, 8.652536458379778, 7.5314641378295235, 6.567334597110879, 8.493516759548358, 7.6294518800050355, 6.909480876402764, 8.603892203786629, 10.735374727080952, 7.269388390573901, 8.861930933915144, 6.566368370705019, 6.649809561604744, 6.611589875000943, 5.496196964085726, 7.70903543971305, 6.70953530232466, 7.821874753600213], 'pdb_id': '1erb', 'vina_output': [-8.31353, 56.54909, 1495.07589, 0.50669, 68.97156, 0.0]}]
+[{'vina_output': -8.31353, 'nnscore': 7.607969605308547, 'dlscore': 7.1239998340606689}]
 `
 
 DLSCORE will be producing the number of networks specified with in `-n`, NNScore 2.0 will display 20, and vina 1 (plus 5 . The output thrown by DLSCORE and NNScore 2.0 are pKd values, while Vina gives delta G (kcal/mol)
 
-The same applies for the rest of the proteins and ligands. To see the rest of the protein-ligand complexes for our dataset (300 from PDBbind v.2016 refined-set), please read our [research article](www.includelink.com)
+The same applies for the rest of the proteins and ligands. To see the rest of the protein-ligand complexes for our dataset (300 from PDBbind v.2016 refined-set), please read our [research article](https://doi.org/10.26434/chemrxiv.6159143.v1)