add relative path to models in README

zhenglz · Jul 11, 2019 · 34a13e7 · 34a13e7
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diff --git a/README.md b/README.md
@@ -1,5 +1,6 @@
 # OnionNet
-A multiple-layer inter-molecular contact based deep neural network for protein-ligand binding affinity prediction. The testing set is CASF-2013 benchmark. The protein-ligand binding affinity is directly predicted.
+A multiple-layer inter-molecular contact based deep neural network for protein-ligand binding affinity prediction.
+The testing set is CASF-2013 benchmark and PDBbind v2016 coreset. The protein-ligand binding affinity is directly predicted.
 
 The model could be applied for re-scoring the docking results.
 
@@ -65,10 +66,7 @@ Using the "generate_features.py" script to generate the features for OnionNet pr
     python generate_features.py -h
     python generate_features.py -inp input_complexes.dat -out output_features.csv
 
-    # or run the script with MPI, cpu 4 cores
-    mpirun -np 4 python generate_features.py -inp input_complexes.dat -out output_features.py 
-
-The input file contatins the absolute or relative pathes of the protein-ligand complexes pdb files.
+The input file contains the absolute or the path of the protein-ligand complexes pdb files.
 The content of the "input_complexes.dat" file could be:
 
     ./10gs/10gs_complex.pdb

diff --git a/onionnet_environments.yml b/onionnet_environments.yml
diff --git a/tutorials/PDB_samples/README.md b/tutorials/PDB_samples/README.md
@@ -28,7 +28,7 @@ Example commands:
 ## 4. Make the prediction
 
     python predict_pKa.py -h
-    python predict_pKa.py -fn docking_complexes_features.csv -model ./models/OnionNet_HFree.model \
-    -scaler models/StandardScaler.model -out predicted_pka_values.csv
+    python predict_pKa.py -fn docking_complexes_features.csv -model ../../models/OnionNet_HFree.model \
+    -scaler ../../models/StandardScaler.model -out predicted_pka_values.csv
 
 Note: The larger the pka value is, the stronger it binds to a receptor.
diff --git a/tutorials/docking_samples/README.md b/tutorials/docking_samples/README.md
@@ -28,7 +28,7 @@ Example commands:
 ## 4. Make the prediction
 
     python predict_pKa.py -h
-    python predict_pKa.py -fn docking_complexes_features.csv -model ./models/OnionNet_HFree.model \
-    -scaler models/StandardScaler.model -out predicted_pka_values.csv
+    python predict_pKa.py -fn docking_complexes_features.csv -model ../../models/OnionNet_HFree.model \
+    -scaler ../../models/StandardScaler.model -out predicted_pka_values.csv
 
 Note: The larger the pka value is, the stronger it binds to a receptor.