--- a
+++ b/DEMO/CNN_Transformer_Kiba.ipynb
@@ -0,0 +1,139 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.chdir('../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import DeepPurpose.DTI as models\n",
+    "from DeepPurpose.utils import *\n",
+    "from DeepPurpose.dataset import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Beginning Processing...\n",
+      "Beginning to extract zip file...\n",
+      "Done!\n",
+      "in total: 118254 drug-target pairs\n",
+      "encoding drug...\n",
+      "unique drugs: 2068\n",
+      "drug encoding finished...\n",
+      "encoding protein...\n",
+      "unique target sequence: 229\n",
+      "protein encoding finished...\n",
+      "splitting dataset...\n",
+      "Done.\n",
+      "cost about 39 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "from time import time\n",
+    "\n",
+    "t1 = time()\n",
+    "X_drug, X_target, y = load_process_KIBA('./data/', binary=False)\n",
+    "\n",
+    "drug_encoding = 'CNN'\n",
+    "target_encoding = 'Transformer'\n",
+    "train, val, test = data_process(X_drug, X_target, y, \n",
+    "                                drug_encoding, target_encoding, \n",
+    "                                split_method='random',frac=[0.7,0.1,0.2])\n",
+    "\n",
+    "# use the parameters setting provided in the paper: https://arxiv.org/abs/1801.10193\n",
+    "config = generate_config(drug_encoding = drug_encoding, \n",
+    "                         target_encoding = target_encoding, \n",
+    "                         cls_hidden_dims = [1024,1024,512], \n",
+    "                         train_epoch = 100, \n",
+    "                         test_every_X_epoch = 10, \n",
+    "                         LR = 0.001, \n",
+    "                         batch_size = 128,\n",
+    "                         hidden_dim_drug = 128,\n",
+    "                         mpnn_hidden_size = 128,\n",
+    "                         mpnn_depth = 3, \n",
+    "                         cnn_target_filters = [32,64,96],\n",
+    "                         cnn_target_kernels = [4,8,12]\n",
+    "                        )\n",
+    "model = models.model_initialize(**config)\n",
+    "t2 = time()\n",
+    "print(\"cost about \" + str(int(t2-t1)) + \" seconds\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Let's use CPU/s!\n",
+      "--- Data Preparation ---\n",
+      "--- Go for Training ---\n",
+      "Training at Epoch 1 iteration 0 with loss 139.439. Total time 0.00222 hours\n",
+      "Training at Epoch 1 iteration 100 with loss 0.85528. Total time 0.19111 hours\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.train(train, val, test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.save_model('./model_CNN_Transformer_Kiba')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}