--- a/README.md
+++ b/README.md
@@ -1,60 +1,60 @@
-# About DeepDTA: deep drug-target binding affinity prediction
-
-The approach used in this work is the modeling of protein sequences and compound 1D representations (SMILES) with convolutional neural networks (CNNs) to predict the binding affinity value of drug-target pairs.
-
-![Figure](https://github.com/hkmztrk/DeepDTA/blob/master/docs/figures/deepdta.PNG)
-# Installation
-
-## Data
-
-Please see the [README](https://github.com/hkmztrk/DeepDTA/blob/master/data/README.md) for detailed explanation.
-
-## Requirements
-
-You'll need to install following in order to run the codes. Refer to [deepdta.yml](https://github.com/hkmztrk/DeepDTA/blob/master/deepdta.yml) for a conda environment tested in Linux. 
-
-*  [Python 3.4 <=](https://www.python.org/downloads/)
-*  [Keras 2.x](https://pypi.org/project/Keras/)
-*  [Tensorflow 1.x](https://www.tensorflow.org/install/)
-*  numpy
-*  matplotlib
-*  scikit-learn
-
-
-
-You have to place "data" folder under "source" directory. 
-
-# Usage
-```
-python run_experiments.py --num_windows 32 \
-                          --seq_window_lengths 8 12 \
-                          --smi_window_lengths 4 8 \
-                          --batch_size 256 \
-                          --num_epoch 100 \
-                          --max_seq_len 1000 \
-                          --max_smi_len 100 \
-                          --dataset_path 'data/kiba/' \
-                          --problem_type 1 \
-                          --log_dir 'logs/'
-
-
-```
-
-
-
-
-
-**For citation:**
-
-```
-@article{ozturk2018deepdta,
-  title={DeepDTA: deep drug--target binding affinity prediction},
-  author={{\"O}zt{\"u}rk, Hakime and {\"O}zg{\"u}r, Arzucan and Ozkirimli, Elif},
-  journal={Bioinformatics},
-  volume={34},
-  number={17},
-  pages={i821--i829},
-  year={2018},
-  publisher={Oxford University Press}
-}
-```
+# About DeepDTA: deep drug-target binding affinity prediction
+
+The approach used in this work is the modeling of protein sequences and compound 1D representations (SMILES) with convolutional neural networks (CNNs) to predict the binding affinity value of drug-target pairs.
+
+![Figure](https://github.com/hkmztrk/DeepDTA/blob/master/docs/figures/deepdta.png?raw=true)
+# Installation
+
+## Data
+
+Please see the [README](https://github.com/hkmztrk/DeepDTA/blob/master/data/README.md) for detailed explanation.
+
+## Requirements
+
+You'll need to install following in order to run the codes. Refer to [deepdta.yml](https://github.com/hkmztrk/DeepDTA/blob/master/deepdta.yml) for a conda environment tested in Linux. 
+
+*  [Python 3.4 <=](https://www.python.org/downloads/)
+*  [Keras 2.x](https://pypi.org/project/Keras/)
+*  [Tensorflow 1.x](https://www.tensorflow.org/install/)
+*  numpy
+*  matplotlib
+*  scikit-learn
+
+
+
+You have to place "data" folder under "source" directory. 
+
+# Usage
+```
+python run_experiments.py --num_windows 32 \
+                          --seq_window_lengths 8 12 \
+                          --smi_window_lengths 4 8 \
+                          --batch_size 256 \
+                          --num_epoch 100 \
+                          --max_seq_len 1000 \
+                          --max_smi_len 100 \
+                          --dataset_path 'data/kiba/' \
+                          --problem_type 1 \
+                          --log_dir 'logs/'
+
+
+```
+
+
+
+
+
+**For citation:**
+
+```
+@article{ozturk2018deepdta,
+  title={DeepDTA: deep drug--target binding affinity prediction},
+  author={{\"O}zt{\"u}rk, Hakime and {\"O}zg{\"u}r, Arzucan and Ozkirimli, Elif},
+  journal={Bioinformatics},
+  volume={34},
+  number={17},
+  pages={i821--i829},
+  year={2018},
+  publisher={Oxford University Press}
+}
+```