diff --git a/Chapter11/chapter_11_02_erk2_graph_conv.ipynb b/Chapter11/chapter_11_02_erk2_graph_conv.ipynb
index 4d9134b..ce0ef10 100644
--- a/Chapter11/chapter_11_02_erk2_graph_conv.ipynb
+++ b/Chapter11/chapter_11_02_erk2_graph_conv.ipynb
@@ -17,8 +17,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/ubuntu/anaconda3/envs/deepchem/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
-      "  from numpy.core.umath_tests import inner1d\n"
+      "\n",
+      "Bad key \"text.kerning_factor\" on line 4 in\n",
+      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.\n",
+      "You probably need to get an updated matplotlibrc file from\n",
+      "https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n",
+      "or from the matplotlib source distribution\n"
      ]
     }
    ],
@@ -60,34 +64,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading raw samples now.\n",
-      "shard_size: 8192\n",
-      "About to start loading CSV from dude_erk2_mk01.csv\n",
-      "Loading shard 1 of size 8192.\n",
-      "Featurizing sample 0\n",
-      "Featurizing sample 1000\n",
-      "Featurizing sample 2000\n",
-      "Featurizing sample 3000\n",
-      "Featurizing sample 4000\n",
-      "TIMING: featurizing shard 0 took 14.259 s\n",
-      "TIMING: dataset construction took 16.347 s\n",
-      "Loading dataset from disk.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "dataset_file = \"dude_erk2_mk01.csv\"\n",
     "tasks = [\"is_active\"]\n",
     "featurizer = dc.feat.ConvMolFeaturizer()\n",
-    "loader = dc.data.CSVLoader(tasks=tasks, smiles_field=\"SMILES\", featurizer=featurizer)\n",
-    "dataset = loader.featurize(dataset_file, shard_size=8192)"
+    "loader = dc.data.CSVLoader(tasks=tasks, feature_field=\"SMILES\", featurizer=featurizer)\n",
+    "dataset = loader.create_dataset(dataset_file, shard_size=8192)"
    ]
   },
   {
@@ -100,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,11 +102,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "metrics = [dc.metrics.Metric(dc.metrics.matthews_corrcoef, np.mean, mode=\"classification\")]"
+    "#metrics = [dc.metrics.Metric(dc.metrics.matthews_corrcoef, np.mean, mode=\"classification\")]\n",
+    "metrics = [dc.metrics.Metric(dc.metrics.matthews_corrcoef)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.],\n",
+       "       [1.],\n",
+       "       [1.],\n",
+       "       ...,\n",
+       "       [0.],\n",
+       "       [0.],\n",
+       "       [0.]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.y"
    ]
   },
   {
@@ -133,151 +145,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "TIMING: dataset construction took 2.760 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.090 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.177 s\n",
-      "Loading dataset from disk.\n",
-      "WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Colocations handled automatically by placer.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Colocations handled automatically by placer.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:317: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Deprecated in favor of operator or tf.math.divide.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:317: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Deprecated in favor of operator or tf.math.divide.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.cast instead.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.cast instead.\n",
-      "/home/ubuntu/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:110: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
-      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "computed_metrics: [0.9733940774144629]\n",
-      "computed_metrics: [0.6147334478929444]\n",
-      "TIMING: dataset construction took 2.791 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.205 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.089 s\n",
-      "Loading dataset from disk.\n",
-      "computed_metrics: [0.9613024323789267]\n",
-      "computed_metrics: [0.7040189638958985]\n",
-      "TIMING: dataset construction took 2.675 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.063 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.160 s\n",
-      "Loading dataset from disk.\n",
-      "computed_metrics: [0.9560246198785713]\n",
-      "computed_metrics: [0.7729109291165613]\n",
-      "TIMING: dataset construction took 2.789 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.072 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.157 s\n",
-      "Loading dataset from disk.\n",
-      "computed_metrics: [0.8640073855714518]\n",
-      "computed_metrics: [0.6630256418076944]\n",
-      "TIMING: dataset construction took 2.734 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.090 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.186 s\n",
-      "Loading dataset from disk.\n",
-      "computed_metrics: [0.8874180607161601]\n",
-      "computed_metrics: [0.7032364358565029]\n",
-      "TIMING: dataset construction took 2.374 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.497 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.071 s\n",
-      "Loading dataset from disk.\n",
-      "computed_metrics: [0.8966687876929688]\n",
-      "computed_metrics: [0.8641283031407085]\n",
-      "TIMING: dataset construction took 2.876 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.214 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.076 s\n",
-      "Loading dataset from disk.\n",
-      "computed_metrics: [0.885610629113555]\n",
-      "computed_metrics: [0.8934523381859952]\n",
-      "TIMING: dataset construction took 2.759 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.189 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.300 s\n",
-      "Loading dataset from disk.\n",
-      "computed_metrics: [0.8228368852646348]\n",
-      "computed_metrics: [0.7534545402108427]\n",
-      "TIMING: dataset construction took 2.919 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.220 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.088 s\n",
-      "Loading dataset from disk.\n",
-      "computed_metrics: [0.9327612022162147]\n",
-      "computed_metrics: [0.7271046235060015]\n",
-      "TIMING: dataset construction took 2.716 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.198 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.170 s\n",
-      "Loading dataset from disk.\n",
-      "computed_metrics: [0.9594842772087291]\n",
-      "computed_metrics: [0.7040189638958985]\n",
-      "[0.9733940774144629, 0.9613024323789267, 0.9560246198785713, 0.8640073855714518, 0.8874180607161601, 0.8966687876929688, 0.885610629113555, 0.8228368852646348, 0.9327612022162147, 0.9594842772087291]\n",
-      "[0.6147334478929444, 0.7040189638958985, 0.7729109291165613, 0.6630256418076944, 0.7032364358565029, 0.8641283031407085, 0.8934523381859952, 0.7534545402108427, 0.7271046235060015, 0.7040189638958985]\n"
+      "[0.7652402518502088, 0.6744587560282231, 0.9754981647402814, 0.7502144806274917, 0.7876463822050892, 0.7444791855276909, 0.8876996292511582, 0.7575401795072797, 0.7099611167619201, 0.7934302775134846]\n",
+      "[0.6771111667693442, 0.531615869110444, 0.813812201341186, 0.6518011356977635, 0.7063410995530055, 0.6090352313647203, 0.7534545402108427, 0.814715779285453, 0.4678118373451869, 0.8799809496431685]\n"
      ]
     }
    ],
@@ -291,9 +167,9 @@
     "    train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split(dataset)\n",
     "    model.fit(train_dataset)\n",
     "    train_scores = model.evaluate(train_dataset, metrics, transformers)\n",
-    "    training_score_list.append(train_scores[\"mean-matthews_corrcoef\"])\n",
+    "    training_score_list.append(train_scores[\"matthews_corrcoef\"])\n",
     "    validation_scores = model.evaluate(valid_dataset, metrics, transformers)\n",
-    "    validation_score_list.append(validation_scores[\"mean-matthews_corrcoef\"])\n",
+    "    validation_score_list.append(validation_scores[\"matthews_corrcoef\"])\n",
     "print(training_score_list)\n",
     "print(validation_score_list)"
    ]
@@ -307,22 +183,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce2892f390>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f87e2f27b38>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -346,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -355,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 16,
    "metadata": {
     "inputHidden": false,
     "outputHidden": false
@@ -364,472 +240,472 @@
     {
      "data": {
       "text/plain": [
-       "[array([0.78670406, 0.213296  ], dtype=float32),\n",
-       " array([0.03079515, 0.9692049 ], dtype=float32),\n",
-       " array([0.58493924, 0.41506073], dtype=float32),\n",
-       " array([0.24829295, 0.7517071 ], dtype=float32),\n",
-       " array([0.9275609, 0.0724391], dtype=float32),\n",
-       " array([0.42476124, 0.57523876], dtype=float32),\n",
-       " array([0.49025437, 0.5097456 ], dtype=float32),\n",
-       " array([0.97195596, 0.02804398], dtype=float32),\n",
-       " array([0.9975581 , 0.00244191], dtype=float32),\n",
-       " array([9.999355e-01, 6.447515e-05], dtype=float32),\n",
-       " array([0.99808735, 0.00191258], dtype=float32),\n",
-       " array([9.9960166e-01, 3.9835321e-04], dtype=float32),\n",
-       " array([9.9972945e-01, 2.7051347e-04], dtype=float32),\n",
-       " array([0.99626094, 0.00373907], dtype=float32),\n",
-       " array([9.9987352e-01, 1.2640837e-04], dtype=float32),\n",
-       " array([0.9984927 , 0.00150731], dtype=float32),\n",
-       " array([9.9959880e-01, 4.0125093e-04], dtype=float32),\n",
-       " array([9.9983919e-01, 1.6080444e-04], dtype=float32),\n",
-       " array([9.999192e-01, 8.087628e-05], dtype=float32),\n",
-       " array([9.9987078e-01, 1.2920667e-04], dtype=float32),\n",
-       " array([9.999151e-01, 8.492516e-05], dtype=float32),\n",
-       " array([0.998798  , 0.00120199], dtype=float32),\n",
-       " array([0.9935579 , 0.00644213], dtype=float32),\n",
-       " array([9.9958426e-01, 4.1573922e-04], dtype=float32),\n",
-       " array([0.9985259 , 0.00147416], dtype=float32),\n",
-       " array([0.995468  , 0.00453197], dtype=float32),\n",
-       " array([9.9987352e-01, 1.2640837e-04], dtype=float32),\n",
-       " array([9.9988329e-01, 1.1665429e-04], dtype=float32),\n",
-       " array([9.9986541e-01, 1.3456838e-04], dtype=float32),\n",
-       " array([0.99498296, 0.00501696], dtype=float32),\n",
-       " array([9.9958628e-01, 4.1377108e-04], dtype=float32),\n",
-       " array([9.999609e-01, 3.914170e-05], dtype=float32),\n",
-       " array([9.9996090e-01, 3.9141665e-05], dtype=float32),\n",
-       " array([9.9970716e-01, 2.9283381e-04], dtype=float32),\n",
-       " array([9.9984884e-01, 1.5118689e-04], dtype=float32),\n",
-       " array([0.99344987, 0.00655011], dtype=float32),\n",
-       " array([9.9995542e-01, 4.4534183e-05], dtype=float32),\n",
-       " array([0.99694556, 0.00305447], dtype=float32),\n",
-       " array([9.9958795e-01, 4.1201437e-04], dtype=float32),\n",
-       " array([0.9894259 , 0.01057409], dtype=float32),\n",
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+       " array([9.9962866e-01, 3.7135888e-04], dtype=float32),\n",
+       " array([9.9956506e-01, 4.3490651e-04], dtype=float32),\n",
+       " array([0.99877256, 0.00122739], dtype=float32),\n",
+       " array([9.999819e-01, 1.808392e-05], dtype=float32),\n",
+       " array([9.9918967e-01, 8.1036426e-04], dtype=float32),\n",
+       " array([0.702075  , 0.29792497], dtype=float32),\n",
+       " array([9.99884367e-01, 1.15636496e-04], dtype=float32),\n",
+       " array([0.99837554, 0.00162444], dtype=float32),\n",
+       " array([9.9926549e-01, 7.3451374e-04], dtype=float32),\n",
+       " array([9.991302e-01, 8.698438e-04], dtype=float32),\n",
+       " array([9.991804e-01, 8.196247e-04], dtype=float32),\n",
+       " array([9.9984074e-01, 1.5920642e-04], dtype=float32),\n",
+       " array([9.9982738e-01, 1.7256876e-04], dtype=float32),\n",
+       " array([9.999051e-01, 9.484378e-05], dtype=float32),\n",
+       " array([9.9978465e-01, 2.1533998e-04], dtype=float32),\n",
+       " array([9.9986064e-01, 1.3938280e-04], dtype=float32),\n",
+       " array([9.9983490e-01, 1.6503599e-04], dtype=float32),\n",
+       " array([0.99834394, 0.00165604], dtype=float32),\n",
+       " array([9.9933308e-01, 6.6686654e-04], dtype=float32),\n",
+       " array([0.9989557 , 0.00104422], dtype=float32),\n",
+       " array([0.99879116, 0.00120886], dtype=float32),\n",
+       " array([9.9907243e-01, 9.2751131e-04], dtype=float32),\n",
+       " array([0.9986486 , 0.00135141], dtype=float32),\n",
+       " array([9.9996257e-01, 3.7415968e-05], dtype=float32),\n",
+       " array([0.9987741 , 0.00122583], dtype=float32),\n",
+       " array([9.9987161e-01, 1.2842291e-04], dtype=float32),\n",
+       " array([9.9972528e-01, 2.7472276e-04], dtype=float32),\n",
+       " array([9.998288e-01, 1.711374e-04], dtype=float32),\n",
+       " array([0.996745  , 0.00325498], dtype=float32),\n",
+       " array([9.9960262e-01, 3.9736612e-04], dtype=float32),\n",
+       " array([9.9963737e-01, 3.6259799e-04], dtype=float32),\n",
+       " array([9.990219e-01, 9.780853e-04], dtype=float32),\n",
+       " array([0.9983968 , 0.00160312], dtype=float32),\n",
+       " array([9.9982953e-01, 1.7040824e-04], dtype=float32),\n",
+       " array([9.9989748e-01, 1.0250921e-04], dtype=float32),\n",
+       " array([9.9967134e-01, 3.2866068e-04], dtype=float32),\n",
+       " array([9.9976069e-01, 2.3930256e-04], dtype=float32),\n",
+       " array([0.05130126, 0.94869876], dtype=float32),\n",
+       " array([9.9983120e-01, 1.6875232e-04], dtype=float32),\n",
+       " array([9.994079e-01, 5.920773e-04], dtype=float32),\n",
+       " array([9.999329e-01, 6.715881e-05], dtype=float32),\n",
+       " array([9.9948883e-01, 5.1118433e-04], dtype=float32),\n",
+       " array([0.99893326, 0.00106669], dtype=float32),\n",
+       " array([9.9981064e-01, 1.8938360e-04], dtype=float32),\n",
+       " array([9.9917078e-01, 8.2930236e-04], dtype=float32),\n",
+       " array([9.9970657e-01, 2.9346207e-04], dtype=float32),\n",
+       " array([9.9981183e-01, 1.8820883e-04], dtype=float32),\n",
+       " array([9.9997461e-01, 2.5387137e-05], dtype=float32),\n",
+       " array([9.9993014e-01, 6.9812224e-05], dtype=float32),\n",
+       " array([9.9984467e-01, 1.5526205e-04], dtype=float32),\n",
+       " array([9.9996972e-01, 3.0239766e-05], dtype=float32),\n",
+       " array([0.9984761 , 0.00152386], dtype=float32),\n",
+       " array([9.997950e-01, 2.049987e-04], dtype=float32),\n",
+       " array([0.9961423 , 0.00385767], dtype=float32),\n",
+       " array([0.9979527 , 0.00204726], dtype=float32),\n",
+       " array([9.999602e-01, 3.978177e-05], dtype=float32),\n",
+       " array([0.25030857, 0.7496914 ], dtype=float32),\n",
+       " array([9.999249e-01, 7.506466e-05], dtype=float32),\n",
+       " array([0.9979842 , 0.00201577], dtype=float32),\n",
+       " array([9.9992371e-01, 7.6332595e-05], dtype=float32),\n",
+       " array([9.9981910e-01, 1.8090887e-04], dtype=float32),\n",
+       " array([0.9786512 , 0.02134877], dtype=float32),\n",
+       " array([9.9973184e-01, 2.6816383e-04], dtype=float32),\n",
+       " array([0.9987293 , 0.00127072], dtype=float32),\n",
+       " array([0.9980148 , 0.00198515], dtype=float32),\n",
+       " array([9.999198e-01, 8.027794e-05], dtype=float32),\n",
+       " array([9.9913019e-01, 8.6977705e-04], dtype=float32),\n",
+       " array([9.9988341e-01, 1.1654544e-04], dtype=float32),\n",
+       " array([0.9985769 , 0.00142311], dtype=float32),\n",
+       " array([0.99806756, 0.0019325 ], dtype=float32),\n",
+       " array([0.9975091, 0.0024908], dtype=float32)]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -847,7 +723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -863,7 +739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -873,7 +749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -906,38 +782,38 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.786704</td>\n",
-       "      <td>0.213296</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cn1ccnc1Sc2ccc(cc2Cl)Nc3c4cc(c(cc4ncc3C#N)OCCC...</td>\n",
+       "      <td>0.998708</td>\n",
+       "      <td>0.001292</td>\n",
+       "      <td>0</td>\n",
+       "      <td>C#CCOc1c(cc(cc1Cl)/C=N/NC(=O)Cc2csc(n2)N)Cl</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.030795</td>\n",
-       "      <td>0.969205</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cc1cnc(nc1c2cc([nH]c2)C(=O)N[C@H](CO)c3cccc(c3...</td>\n",
+       "      <td>0.998298</td>\n",
+       "      <td>0.001702</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CC[C@H](C)NC(=O)c1ccc(cc1)NCC(=O)Nc2ccc(cc2)C(...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.584939</td>\n",
-       "      <td>0.415061</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cc1c2cc(c(cc2oc(=O)c1Cc3ccccc3)OC(=O)N(C)C)Cl</td>\n",
+       "      <td>0.999834</td>\n",
+       "      <td>0.000166</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cc1ccc(c(c1)C)NC(=S)NNC(=O)CCCNC(=O)c2ccsc2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.248293</td>\n",
-       "      <td>0.751707</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cn1c-2c(c(n1)C(=O)N)CCc3c2nc(nc3)NC4CCN(CC4)C(...</td>\n",
+       "      <td>0.998469</td>\n",
+       "      <td>0.001531</td>\n",
+       "      <td>0</td>\n",
+       "      <td>c1ccc(c(c1)CS(=O)(=O)Nc2cccc3c2oc(=O)[nH]3)Cl</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.927561</td>\n",
-       "      <td>0.072439</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cc1c2cc(ccc2[nH]n1)c3cc(cnc3)OC[C@H](Cc4ccccc4)N</td>\n",
+       "      <td>0.999735</td>\n",
+       "      <td>0.000265</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CC(=O)C1=C(C)OC(=N)[C@@H](CN)[C@H]1c1ccco1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -945,21 +821,21 @@
       ],
       "text/plain": [
        "        neg       pos  active  \\\n",
-       "0  0.786704  0.213296       1   \n",
-       "1  0.030795  0.969205       1   \n",
-       "2  0.584939  0.415061       1   \n",
-       "3  0.248293  0.751707       1   \n",
-       "4  0.927561  0.072439       1   \n",
+       "0  0.998708  0.001292       0   \n",
+       "1  0.998298  0.001702       0   \n",
+       "2  0.999834  0.000166       0   \n",
+       "3  0.998469  0.001531       0   \n",
+       "4  0.999735  0.000265       0   \n",
        "\n",
        "                                              SMILES  \n",
-       "0  Cn1ccnc1Sc2ccc(cc2Cl)Nc3c4cc(c(cc4ncc3C#N)OCCC...  \n",
-       "1  Cc1cnc(nc1c2cc([nH]c2)C(=O)N[C@H](CO)c3cccc(c3...  \n",
-       "2      Cc1c2cc(c(cc2oc(=O)c1Cc3ccccc3)OC(=O)N(C)C)Cl  \n",
-       "3  Cn1c-2c(c(n1)C(=O)N)CCc3c2nc(nc3)NC4CCN(CC4)C(...  \n",
-       "4   Cc1c2cc(ccc2[nH]n1)c3cc(cnc3)OC[C@H](Cc4ccccc4)N  "
+       "0        C#CCOc1c(cc(cc1Cl)/C=N/NC(=O)Cc2csc(n2)N)Cl  \n",
+       "1  CC[C@H](C)NC(=O)c1ccc(cc1)NCC(=O)Nc2ccc(cc2)C(...  \n",
+       "2        Cc1ccc(c(c1)C)NC(=S)NNC(=O)CCCNC(=O)c2ccsc2  \n",
+       "3      c1ccc(c(c1)CS(=O)(=O)Nc2cccc3c2oc(=O)[nH]3)Cl  \n",
+       "4         CC(=O)C1=C(C)OC(=N)[C@@H](CN)[C@H]1c1ccco1  "
       ]
      },
-     "execution_count": 12,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -970,7 +846,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 20,
    "metadata": {
     "inputHidden": false,
     "outputHidden": false
@@ -1005,179 +881,179 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.030795</td>\n",
-       "      <td>0.969205</td>\n",
+       "      <th>429</th>\n",
+       "      <td>0.051301</td>\n",
+       "      <td>0.948699</td>\n",
        "      <td>1</td>\n",
        "      <td>Cc1cnc(nc1c2cc([nH]c2)C(=O)N[C@H](CO)c3cccc(c3...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.248293</td>\n",
-       "      <td>0.751707</td>\n",
+       "      <th>157</th>\n",
+       "      <td>0.058489</td>\n",
+       "      <td>0.941511</td>\n",
        "      <td>1</td>\n",
-       "      <td>Cn1c-2c(c(n1)C(=O)N)CCc3c2nc(nc3)NC4CCN(CC4)C(...</td>\n",
+       "      <td>c1ccc(cc1)[C@@H](CO)NC(=O)c2cc(c[nH]2)c3c(cn[n...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.424761</td>\n",
-       "      <td>0.575239</td>\n",
+       "      <th>51</th>\n",
+       "      <td>0.107281</td>\n",
+       "      <td>0.892719</td>\n",
        "      <td>1</td>\n",
-       "      <td>CNC(=O)Nc1ccc(cn1)CNc2c(scn2)C(=O)Nc3ccc4c(c3)...</td>\n",
+       "      <td>c1cc(cc(c1)Cl)c2cn[nH]c2c3cc([nH]c3)C(=O)NCc4c...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0.490254</td>\n",
-       "      <td>0.509746</td>\n",
+       "      <th>29</th>\n",
+       "      <td>0.214444</td>\n",
+       "      <td>0.785556</td>\n",
        "      <td>1</td>\n",
-       "      <td>CCN(C)C(=O)c1cc(c[nH]1)c2c(cn[nH]2)c3cccc(c3)Cl</td>\n",
+       "      <td>Cc1ccccc1Nc2ncc(c(n2)c3cc([nH]c3)C(=O)N[C@H](C...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.584939</td>\n",
-       "      <td>0.415061</td>\n",
+       "      <th>448</th>\n",
+       "      <td>0.250309</td>\n",
+       "      <td>0.749691</td>\n",
        "      <td>1</td>\n",
-       "      <td>Cc1c2cc(c(cc2oc(=O)c1Cc3ccccc3)OC(=O)N(C)C)Cl</td>\n",
+       "      <td>Cc1cnc(nc1c2cc([nH]c2)C(=O)N[C@H](CO)c3cccc(c3...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>265</th>\n",
-       "      <td>0.643711</td>\n",
-       "      <td>0.356289</td>\n",
-       "      <td>0</td>\n",
-       "      <td>CCN[C@H](c1cccs1)c1cc2[nH]c(=O)[nH]c2cc1Br</td>\n",
+       "      <th>349</th>\n",
+       "      <td>0.297913</td>\n",
+       "      <td>0.702087</td>\n",
+       "      <td>1</td>\n",
+       "      <td>C[C@@H](c1ccccc1)NC(=O)c2cc(c[nH]2)c3c(cn[nH]3...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>87</th>\n",
-       "      <td>0.735412</td>\n",
-       "      <td>0.264588</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Cc1c2c([nH]c1C(=O)NCCN3c4ccccc4N[C@@H]3C)CCCC2=O</td>\n",
+       "      <th>362</th>\n",
+       "      <td>0.379176</td>\n",
+       "      <td>0.620824</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cc1cc(ccc1Nc2ncc(c(n2)c3cc([nH]c3)C(=O)N[C@H](...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.786704</td>\n",
-       "      <td>0.213296</td>\n",
+       "      <th>45</th>\n",
+       "      <td>0.625167</td>\n",
+       "      <td>0.374833</td>\n",
        "      <td>1</td>\n",
-       "      <td>Cn1ccnc1Sc2ccc(cc2Cl)Nc3c4cc(c(cc4ncc3C#N)OCCC...</td>\n",
+       "      <td>Cc1cnc(nc1c2cc([nH]c2)C(=O)N[C@H](CO)c3cccc(c3...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>181</th>\n",
-       "      <td>0.892009</td>\n",
-       "      <td>0.107991</td>\n",
-       "      <td>0</td>\n",
-       "      <td>c1ccc(cc1)c2nc([nH]n2)CN3c4ccccc4N[C@@H]3C5CC5</td>\n",
+       "      <th>397</th>\n",
+       "      <td>0.702075</td>\n",
+       "      <td>0.297925</td>\n",
+       "      <td>1</td>\n",
+       "      <td>c1ccc(cc1)[C@@H](C(=O)N)NC(=O)c2cc(c[nH]2)c3c(...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>254</th>\n",
-       "      <td>0.896371</td>\n",
-       "      <td>0.103629</td>\n",
+       "      <th>218</th>\n",
+       "      <td>0.958905</td>\n",
+       "      <td>0.041095</td>\n",
        "      <td>0</td>\n",
-       "      <td>c1c(c(cc(c1N)F)F)C(=O)N(CCO)CCO</td>\n",
+       "      <td>c1cc2c(cc1C#N)nc([nH]2)[C@@H]([C@H](c3[nH]c4cc...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>271</th>\n",
-       "      <td>0.913182</td>\n",
-       "      <td>0.086818</td>\n",
+       "      <th>215</th>\n",
+       "      <td>0.964832</td>\n",
+       "      <td>0.035168</td>\n",
        "      <td>0</td>\n",
-       "      <td>c1cc(oc1)C(=O)N(c2nc3ccc(cc3s2)F)/N=C/c4cccs4</td>\n",
+       "      <td>Cc1c(sc(n1)N)C(=O)NC2CCC(CC2)O</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>262</th>\n",
-       "      <td>0.918457</td>\n",
-       "      <td>0.081543</td>\n",
+       "      <th>219</th>\n",
+       "      <td>0.970495</td>\n",
+       "      <td>0.029505</td>\n",
        "      <td>0</td>\n",
-       "      <td>N=C(N)c1cccc(OCCCOc2cccc(C(F)(F)F)c2)c1</td>\n",
+       "      <td>Cc1cc2c(cc1C(C)C)c(co2)CC(=O)Nc3cccc(c3)NS(=O)...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>198</th>\n",
-       "      <td>0.926560</td>\n",
-       "      <td>0.073440</td>\n",
+       "      <th>453</th>\n",
+       "      <td>0.978651</td>\n",
+       "      <td>0.021349</td>\n",
        "      <td>0</td>\n",
-       "      <td>N=c1[nH]c(N2CCN3C[C@@H](CNCc4cccnc4)CC[C@H]3C2...</td>\n",
+       "      <td>Cc1ccc(cc1)c2csc3c2c(ncn3)N[C@@H]4CCCNC4=O</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.927561</td>\n",
-       "      <td>0.072439</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cc1c2cc(ccc2[nH]n1)c3cc(cnc3)OC[C@H](Cc4ccccc4)N</td>\n",
+       "      <th>182</th>\n",
+       "      <td>0.985785</td>\n",
+       "      <td>0.014215</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CCn1ccc(n1)[C@@H]2Nc3c4c(cc(nc4sc3C(=O)N2)C(F)F)C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>0.947998</td>\n",
-       "      <td>0.052002</td>\n",
+       "      <th>39</th>\n",
+       "      <td>0.986688</td>\n",
+       "      <td>0.013312</td>\n",
        "      <td>0</td>\n",
-       "      <td>c1c(cc(cc1Cl)Cl)n2c3c(cn2)c(ncn3)NN</td>\n",
+       "      <td>Cc1c(c([nH]c1C(=O)Nc2ccc3c(c2)CCCN3C)C)C(=O)C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>53</th>\n",
-       "      <td>0.948888</td>\n",
-       "      <td>0.051112</td>\n",
+       "      <td>0.987214</td>\n",
+       "      <td>0.012786</td>\n",
        "      <td>0</td>\n",
-       "      <td>c1ccc2c(c1)nc(s2)Nc3c(c(ncn3)Nc4cccc5c4nccc5)N</td>\n",
+       "      <td>Cc1noc(C)c1C(=O)N1C[C@H](C(=O)NCCNC(=O)Cc2cccc...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>269</th>\n",
-       "      <td>0.949782</td>\n",
-       "      <td>0.050218</td>\n",
+       "      <th>354</th>\n",
+       "      <td>0.988896</td>\n",
+       "      <td>0.011104</td>\n",
        "      <td>0</td>\n",
-       "      <td>Cc1ccccc1c2nc(on2)CN3CCN(CC3)c4ccccc4C</td>\n",
+       "      <td>CCN(CC)c1ccc2c(c1)Oc3cc(ccc3C24c5cc(c(cc5C(=O)...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>453</th>\n",
-       "      <td>0.949959</td>\n",
-       "      <td>0.050041</td>\n",
+       "      <th>228</th>\n",
+       "      <td>0.989718</td>\n",
+       "      <td>0.010282</td>\n",
        "      <td>0</td>\n",
-       "      <td>Cc1c(cc(c2c1oc-3c(c(=O)c(c(c3n2)C(=O)[C@@H]4CC...</td>\n",
+       "      <td>C=C1C=C(O)N2C(=NC(NCCc3c[nH]c4ccccc34)=N[C@H]2...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>419</th>\n",
-       "      <td>0.951547</td>\n",
-       "      <td>0.048453</td>\n",
+       "      <th>100</th>\n",
+       "      <td>0.990552</td>\n",
+       "      <td>0.009448</td>\n",
        "      <td>0</td>\n",
-       "      <td>CSc1ncc(c(n1)C(=O)OCc2nc3ccccc3c(n2)N)Cl</td>\n",
+       "      <td>[C@@H]12[C@@H](N[C@@H]3[C@@H](N1)NC(=O)N3)NC(=...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>161</th>\n",
-       "      <td>0.954606</td>\n",
-       "      <td>0.045394</td>\n",
+       "      <th>319</th>\n",
+       "      <td>0.990722</td>\n",
+       "      <td>0.009278</td>\n",
        "      <td>0</td>\n",
-       "      <td>c1ccc2c(c1)cc(o2)C3=NN([C@@H](C3)c4ccco4)C(=O)...</td>\n",
+       "      <td>c1c2c(cc3c1OCO3)C(=O)N[C@H]4C2=C[C@@H]([C@H]([...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>272</th>\n",
-       "      <td>0.955823</td>\n",
-       "      <td>0.044177</td>\n",
+       "      <th>390</th>\n",
+       "      <td>0.991421</td>\n",
+       "      <td>0.008579</td>\n",
        "      <td>0</td>\n",
-       "      <td>CCc1nc2c3ccccc3oc2c(n1)N4CCN(CC4)c5ccccc5F</td>\n",
+       "      <td>Cc1ccc(cc1)n2c3c(c(cc(=O)n3Cc4ccccc4C#N)C)c(n2)C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>108</th>\n",
-       "      <td>0.960468</td>\n",
-       "      <td>0.039532</td>\n",
+       "      <th>116</th>\n",
+       "      <td>0.991954</td>\n",
+       "      <td>0.008046</td>\n",
        "      <td>0</td>\n",
-       "      <td>c1ccc2c(c1)cccc2C(=O)NNc3c(c(ncn3)Oc4cccc5c4cc...</td>\n",
+       "      <td>CCn1nc(C)c(Br)c1C(=O)N1C[C@H](C(=O)N[C@@H](C)c...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>379</th>\n",
-       "      <td>0.960618</td>\n",
-       "      <td>0.039381</td>\n",
+       "      <th>391</th>\n",
+       "      <td>0.992746</td>\n",
+       "      <td>0.007254</td>\n",
        "      <td>0</td>\n",
-       "      <td>C/C(=N\\N=C(\\c1c(non1)N)/N)/c2ccc(cc2)OCc3cccc4...</td>\n",
+       "      <td>Cc1cccc(C)c1OCc1nc2c3c4c(sc3ncn2n1)C(C)(C)NC(C...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>79</th>\n",
-       "      <td>0.967507</td>\n",
-       "      <td>0.032493</td>\n",
+       "      <th>162</th>\n",
+       "      <td>0.992758</td>\n",
+       "      <td>0.007242</td>\n",
        "      <td>0</td>\n",
-       "      <td>Cn1cc(c2c1cccc2)C(=O)NNC(=O)NCCc3ccccc3Cl</td>\n",
+       "      <td>C[C@H]1CC[C@@H]([C@@H](C1)OC(=O)C2CCN(CC2)C(=O...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>461</th>\n",
-       "      <td>0.970460</td>\n",
-       "      <td>0.029540</td>\n",
+       "      <th>173</th>\n",
+       "      <td>0.993147</td>\n",
+       "      <td>0.006853</td>\n",
        "      <td>0</td>\n",
-       "      <td>COc1ccc(c(c1OC)OC)/C=N/c2c(c3c(s2)CCCC3)C(=O)N...</td>\n",
+       "      <td>Cc1ccc(cc1)/C=N/n2c(c(c3c2nc4ccccc4n3)C(=O)NCC...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1185,61 +1061,61 @@
       ],
       "text/plain": [
        "          neg       pos  active  \\\n",
-       "1    0.030795  0.969205       1   \n",
-       "3    0.248293  0.751707       1   \n",
-       "5    0.424761  0.575239       1   \n",
-       "6    0.490254  0.509746       1   \n",
-       "2    0.584939  0.415061       1   \n",
-       "265  0.643711  0.356289       0   \n",
-       "87   0.735412  0.264588       0   \n",
-       "0    0.786704  0.213296       1   \n",
-       "181  0.892009  0.107991       0   \n",
-       "254  0.896371  0.103629       0   \n",
-       "271  0.913182  0.086818       0   \n",
-       "262  0.918457  0.081543       0   \n",
-       "198  0.926560  0.073440       0   \n",
-       "4    0.927561  0.072439       1   \n",
-       "42   0.947998  0.052002       0   \n",
-       "53   0.948888  0.051112       0   \n",
-       "269  0.949782  0.050218       0   \n",
-       "453  0.949959  0.050041       0   \n",
-       "419  0.951547  0.048453       0   \n",
-       "161  0.954606  0.045394       0   \n",
-       "272  0.955823  0.044177       0   \n",
-       "108  0.960468  0.039532       0   \n",
-       "379  0.960618  0.039381       0   \n",
-       "79   0.967507  0.032493       0   \n",
-       "461  0.970460  0.029540       0   \n",
+       "429  0.051301  0.948699       1   \n",
+       "157  0.058489  0.941511       1   \n",
+       "51   0.107281  0.892719       1   \n",
+       "29   0.214444  0.785556       1   \n",
+       "448  0.250309  0.749691       1   \n",
+       "349  0.297913  0.702087       1   \n",
+       "362  0.379176  0.620824       1   \n",
+       "45   0.625167  0.374833       1   \n",
+       "397  0.702075  0.297925       1   \n",
+       "218  0.958905  0.041095       0   \n",
+       "215  0.964832  0.035168       0   \n",
+       "219  0.970495  0.029505       0   \n",
+       "453  0.978651  0.021349       0   \n",
+       "182  0.985785  0.014215       0   \n",
+       "39   0.986688  0.013312       0   \n",
+       "53   0.987214  0.012786       0   \n",
+       "354  0.988896  0.011104       0   \n",
+       "228  0.989718  0.010282       0   \n",
+       "100  0.990552  0.009448       0   \n",
+       "319  0.990722  0.009278       0   \n",
+       "390  0.991421  0.008579       0   \n",
+       "116  0.991954  0.008046       0   \n",
+       "391  0.992746  0.007254       0   \n",
+       "162  0.992758  0.007242       0   \n",
+       "173  0.993147  0.006853       0   \n",
        "\n",
        "                                                SMILES  \n",
-       "1    Cc1cnc(nc1c2cc([nH]c2)C(=O)N[C@H](CO)c3cccc(c3...  \n",
-       "3    Cn1c-2c(c(n1)C(=O)N)CCc3c2nc(nc3)NC4CCN(CC4)C(...  \n",
-       "5    CNC(=O)Nc1ccc(cn1)CNc2c(scn2)C(=O)Nc3ccc4c(c3)...  \n",
-       "6      CCN(C)C(=O)c1cc(c[nH]1)c2c(cn[nH]2)c3cccc(c3)Cl  \n",
-       "2        Cc1c2cc(c(cc2oc(=O)c1Cc3ccccc3)OC(=O)N(C)C)Cl  \n",
-       "265         CCN[C@H](c1cccs1)c1cc2[nH]c(=O)[nH]c2cc1Br  \n",
-       "87    Cc1c2c([nH]c1C(=O)NCCN3c4ccccc4N[C@@H]3C)CCCC2=O  \n",
-       "0    Cn1ccnc1Sc2ccc(cc2Cl)Nc3c4cc(c(cc4ncc3C#N)OCCC...  \n",
-       "181     c1ccc(cc1)c2nc([nH]n2)CN3c4ccccc4N[C@@H]3C5CC5  \n",
-       "254                    c1c(c(cc(c1N)F)F)C(=O)N(CCO)CCO  \n",
-       "271      c1cc(oc1)C(=O)N(c2nc3ccc(cc3s2)F)/N=C/c4cccs4  \n",
-       "262            N=C(N)c1cccc(OCCCOc2cccc(C(F)(F)F)c2)c1  \n",
-       "198  N=c1[nH]c(N2CCN3C[C@@H](CNCc4cccnc4)CC[C@H]3C2...  \n",
-       "4     Cc1c2cc(ccc2[nH]n1)c3cc(cnc3)OC[C@H](Cc4ccccc4)N  \n",
-       "42                 c1c(cc(cc1Cl)Cl)n2c3c(cn2)c(ncn3)NN  \n",
-       "53      c1ccc2c(c1)nc(s2)Nc3c(c(ncn3)Nc4cccc5c4nccc5)N  \n",
-       "269             Cc1ccccc1c2nc(on2)CN3CCN(CC3)c4ccccc4C  \n",
-       "453  Cc1c(cc(c2c1oc-3c(c(=O)c(c(c3n2)C(=O)[C@@H]4CC...  \n",
-       "419           CSc1ncc(c(n1)C(=O)OCc2nc3ccccc3c(n2)N)Cl  \n",
-       "161  c1ccc2c(c1)cc(o2)C3=NN([C@@H](C3)c4ccco4)C(=O)...  \n",
-       "272         CCc1nc2c3ccccc3oc2c(n1)N4CCN(CC4)c5ccccc5F  \n",
-       "108  c1ccc2c(c1)cccc2C(=O)NNc3c(c(ncn3)Oc4cccc5c4cc...  \n",
-       "379  C/C(=N\\N=C(\\c1c(non1)N)/N)/c2ccc(cc2)OCc3cccc4...  \n",
-       "79           Cn1cc(c2c1cccc2)C(=O)NNC(=O)NCCc3ccccc3Cl  \n",
-       "461  COc1ccc(c(c1OC)OC)/C=N/c2c(c3c(s2)CCCC3)C(=O)N...  "
+       "429  Cc1cnc(nc1c2cc([nH]c2)C(=O)N[C@H](CO)c3cccc(c3...  \n",
+       "157  c1ccc(cc1)[C@@H](CO)NC(=O)c2cc(c[nH]2)c3c(cn[n...  \n",
+       "51   c1cc(cc(c1)Cl)c2cn[nH]c2c3cc([nH]c3)C(=O)NCc4c...  \n",
+       "29   Cc1ccccc1Nc2ncc(c(n2)c3cc([nH]c3)C(=O)N[C@H](C...  \n",
+       "448  Cc1cnc(nc1c2cc([nH]c2)C(=O)N[C@H](CO)c3cccc(c3...  \n",
+       "349  C[C@@H](c1ccccc1)NC(=O)c2cc(c[nH]2)c3c(cn[nH]3...  \n",
+       "362  Cc1cc(ccc1Nc2ncc(c(n2)c3cc([nH]c3)C(=O)N[C@H](...  \n",
+       "45   Cc1cnc(nc1c2cc([nH]c2)C(=O)N[C@H](CO)c3cccc(c3...  \n",
+       "397  c1ccc(cc1)[C@@H](C(=O)N)NC(=O)c2cc(c[nH]2)c3c(...  \n",
+       "218  c1cc2c(cc1C#N)nc([nH]2)[C@@H]([C@H](c3[nH]c4cc...  \n",
+       "215                     Cc1c(sc(n1)N)C(=O)NC2CCC(CC2)O  \n",
+       "219  Cc1cc2c(cc1C(C)C)c(co2)CC(=O)Nc3cccc(c3)NS(=O)...  \n",
+       "453         Cc1ccc(cc1)c2csc3c2c(ncn3)N[C@@H]4CCCNC4=O  \n",
+       "182  CCn1ccc(n1)[C@@H]2Nc3c4c(cc(nc4sc3C(=O)N2)C(F)F)C  \n",
+       "39       Cc1c(c([nH]c1C(=O)Nc2ccc3c(c2)CCCN3C)C)C(=O)C  \n",
+       "53   Cc1noc(C)c1C(=O)N1C[C@H](C(=O)NCCNC(=O)Cc2cccc...  \n",
+       "354  CCN(CC)c1ccc2c(c1)Oc3cc(ccc3C24c5cc(c(cc5C(=O)...  \n",
+       "228  C=C1C=C(O)N2C(=NC(NCCc3c[nH]c4ccccc34)=N[C@H]2...  \n",
+       "100  [C@@H]12[C@@H](N[C@@H]3[C@@H](N1)NC(=O)N3)NC(=...  \n",
+       "319  c1c2c(cc3c1OCO3)C(=O)N[C@H]4C2=C[C@@H]([C@H]([...  \n",
+       "390   Cc1ccc(cc1)n2c3c(c(cc(=O)n3Cc4ccccc4C#N)C)c(n2)C  \n",
+       "116  CCn1nc(C)c(Br)c1C(=O)N1C[C@H](C(=O)N[C@@H](C)c...  \n",
+       "391  Cc1cccc(C)c1OCc1nc2c3c4c(sc3ncn2n1)C(C)(C)NC(C...  \n",
+       "162  C[C@H]1CC[C@@H]([C@@H](C1)OC(=O)C2CCN(CC2)C(=O...  \n",
+       "173  Cc1ccc(cc1)/C=N/n2c(c(c3c2nc4ccccc4n3)C(=O)NCC...  "
       ]
      },
-     "execution_count": 13,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1250,22 +1126,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce22f670b8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f87d43681d0>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAPfklEQVR4nO3df6zddX3H8eeL25axKSzC3bK0xeJanZ1z0d2haKYs4lLMQmOiAgu6TWZjFkoznQlzGyGYEX8sWUiHU+aM4B9iNUbrUqioqIkT1wtYWGHEuzrgtmZeKxERtBbe++Oe4uHe03rRfu/3ls/zkTScz/d8e8675abP+/1+7zknVYUkqV0n9D2AJKlfhkCSGmcIJKlxhkCSGmcIJKlxy/oe4Kk67bTTas2aNX2PIUnHldtuu+27VTU+6r7jLgRr1qxhcnKy7zEk6biS5L4j3eepIUlqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklq3HH3OgJJ3dq6dStTU1O9zrBv3z4AVq5c2escAGvXrmXz5s19j9EpQyBpyXn00Uf7HqEphkDSkyyF7363bNkCwNVXX93zJG3wGoEkNc4QSFLjDIEkNc4QSFLjDIEkNc4QSFLjDIEkNc4QSFLjDIEkNc5XFktLxFJ4j5+l4vDfw+FXGLeu6/c7MgTSEjE1NcU399zB6c94rO9RerfiJ7MnK35832TPk/Tv/ofHOn8OQyAtIac/4zHe+eKH+h5DS8hVt5/c+XN4jUCSGmcIJKlxhkCSGmcIJKlxhkCSGmcIJKlxhkCSGmcIJKlxhkCSGmcIJKlxhkCSGmcIJKlxhkCSGmcIJKlxnYYgyYYk9yaZSnLZiPtPT3JLkjuS3JnkNV3OI0mar7MQJBkDrgHOBdYDFyZZP2e3vwO2VdWLgAuA93c1jyRptC6PCM4Epqpqb1UdBG4ANs7Zp4DDn7pwCrC/w3kkSSN0GYKVwAND6+nBtmFXABclmQZ2ACM/lDPJpiSTSSZnZma6mFWSmtVlCDJiW81ZXwh8pKpWAa8BPppk3kxVdW1VTVTVxPj4eAejSlK7ugzBNLB6aL2K+ad+Lga2AVTV14BfAk7rcCZJ0hxdhmAXsC7JGUlWMHsxePucfe4HXgWQ5PnMhsBzP5K0iDoLQVUdAi4BdgL3MPvTQXuSXJnkvMFubwfekmQ38DHgz6pq7ukjSVKHlnX54FW1g9mLwMPbLh+6fTfw8i5nkCQdna8slqTGGQJJapwhkKTGGQJJalynF4slLdy+ffv44Q/GuOr2k3/2zmrGfT8Y41f27ev0OTwikKTGeUQgLRErV67kx4e+zTtf/FDfo2gJuer2kzlx5dy3aTu2PCKQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMYZAklq3LK+B5D0U/c/PMZVt5/c9xi9+79HZr9H/fVffrznSfp3/8NjrOv4OQyBtESsXbu27xGWjINTUwCc+Gz/TtbR/ddGpyFIsgG4GhgDPlRV7x6xzxuAK4ACdlfVn3Q5k7RUbd68ue8RlowtW7YAcPXVV/c8SRs6C0GSMeAa4NXANLAryfaqunton3XA3wAvr6oHk/xaV/NIkkbr8mLxmcBUVe2tqoPADcDGOfu8Bbimqh4EqKrvdDiPJGmELkOwEnhgaD092DbsucBzk3w1ya2DU0nzJNmUZDLJ5MzMTEfjSlKbugxBRmyrOetlzF4LORu4EPhQkl+d95uqrq2qiaqaGB8fP+aDSlLLugzBNLB6aL0K2D9in89U1U+q6lvAvdD5T0pJkoZ0GYJdwLokZyRZAVwAbJ+zz6eBPwRIchqzp4r2djiTJGmOzkJQVYeAS4CdwD3Atqrak+TKJOcNdtsJHEhyN3AL8I6qOtDVTJKk+Tp9HUFV7QB2zNl2+dDtAt42+CVJ6oHvNSRJjTMEktQ4QyBJjTMEktQ4QyBJjTMEktQ4QyBJjTMEktQ4QyBJjTMEktQ4QyBJjTMEktQ4QyBJjTMEktQ4QyBJjVtQCJK8N8nJSZYn+UKS7ya5qOvhJEndW+gRwR9V1UPAHzP7OcPPBd7R2VSSpEWz0BAsH/z3NcDHqup7Hc0jSVpkC/2oys8m+W/gUeAvk4wDP+puLEnSYlnQEUFVXQacBUxU1U+AHwIbuxxMkrQ4FnREkGQ58EbgFUkAvgx8oMO5JEmLZKGnhv6F2esE7x+s3zjY9hddDCVJWjwLDcHvV9XvDq2/mGR3FwNJkhbXQn9q6LEkv3l4keQ5wGPdjCRJWkwLPSJ4B3BLkr2D9RrgzzuZSJK0qBZ6RPBV4IPA44NfHwS+1tVQkqTFs9AjguuBh4B3DdYXAh8FXt/FUJKkxbPQEDxvzsXiW7xYLElPDws9NXRHkpceXiR5CbOniyRJx7mFHhG8BHhTkvsH69OBe5LcBVRVvbCT6SRJnVtoCDZ0OoUkqTcLCkFV3df1IJKkfnT6CWVJNiS5N8lUksuOst/rklSSiS7nkSTN11kIkowB1wDnAuuBC5OsH7HfM4FLga93NYsk6ci6PCI4E5iqqr1VdRC4gdFvXf0u4L34+QaS1IsuQ7ASeGBoPT3Y9oQkLwJWV9W/dziHJOkougxBRmyrJ+5MTgD+CXj7z3ygZFOSySSTMzMzx3BESVKXIZgGVg+tVwH7h9bPBF4AfCnJ/wIvBbaPumBcVddW1URVTYyPj3c4siS1p8sQ7ALWJTkjyQrgAmD74Tur6vtVdVpVramqNcCtwHlVNdnhTJKkOToLQVUdAi4BdgL3ANuqak+SK5Oc19XzSpKemoW+svjnUlU7gB1ztl1+hH3P7nIWSdJonb6gTJK09BkCSWqcIZCkxhkCSWqcIZCkxhkCSWqcIZCkxhkCSWqcIZCkxhkCSWqcIZCkxhkCSWqcIZCkxhkCSWqcIZCkxnX6eQSSjj9bt25lamqq1xkOP/+WLVt6nQNg7dq1bN68ue8xOmUIJC05J510Ut8jNMUQSHqSp/t3v5rPawSS1DhDIEmNMwSS1DhDIEmNMwSS1DhDIEmNMwSS1DhDIEmNMwSS1DhDIEmNMwSS1DhDIEmNMwSS1DhDIEmNMwSS1LhOQ5BkQ5J7k0wluWzE/W9LcneSO5N8Icmzu5xHkjRfZyFIMgZcA5wLrAcuTLJ+zm53ABNV9ULgk8B7u5pHkjRal0cEZwJTVbW3qg4CNwAbh3eoqluq6pHB8lZgVYfzSJJG6DIEK4EHhtbTg21HcjFw46g7kmxKMplkcmZm5hiOKEnqMgQZsa1G7phcBEwA7xt1f1VdW1UTVTUxPj5+DEeUJHX54fXTwOqh9Spg/9ydkpwD/C3wyqr6cYfzSJJG6PKIYBewLskZSVYAFwDbh3dI8iLgg8B5VfWdDmeRJB1BZyGoqkPAJcBO4B5gW1XtSXJlkvMGu70PeAbwiSTfSLL9CA8nSepIl6eGqKodwI452y4fun1Ol88vSfrZfGWxJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEEhS4wyBJDXOEDTswIEDXHrppRw4cKDvUST1yBA07LrrruOuu+7i+uuv73sUST0yBI06cOAAN910E1XFjTfe6FGB1DBD0KjrrruOgwcPAnDw4EGPCqSGGYJG3XzzzU9af+5zn+tpEkl9MwSNOvXUU4+6ltQOQ9Co/fv3H3UtqR2GoFFVddS1pHYYgkYlOepaUjsMQaMef/zxo64ltaPTECTZkOTeJFNJLhtx/4lJPj64/+tJ1nQ5jyRpvmVdPXCSMeAa4NXANLAryfaquntot4uBB6tqbZILgPcA53c101KxdetWpqam+h5jni1btvTyvGvXrmXz5s29PLekDkMAnAlMVdVegCQ3ABuB4RBsBK4Y3P4k8M9JUh1eudy6dSs33XRTVw+/II888siSvDi7e/fuXp73zjvv7P3/CcCGDRsMkprUZQhWAg8MraeBlxxpn6o6lOT7wKnAd4d3SrIJ2ARw+umndzXvohkbG+v9nPyo5z/hhH4uGfX1vJJmdRmCUT+GMvfb4IXsQ1VdC1wLMDEx8Qt9K71582a/6wPOOeccDh069MR6+fLl815tLKkNXX4rNg2sHlqvAua+aumJfZIsA04BvtfhTBr4/Oc//6S1EZDa1WUIdgHrkpyRZAVwAbB9zj7bgT8d3H4d8MUurw/oyZYtmz0gXL58ec+TSOpTZ6eGBuf8LwF2AmPAh6tqT5Irgcmq2g78G/DRJFPMHglc0NU8mm/uUYGkNnV5jYCq2gHsmLPt8qHbPwJe3+UMkqSj88c1JKlxhkCSGmcIJKlxhkCSGpfj7ac1k8wA9/U9x9PIacx5Jbe0RPi1eWw9u6rGR91x3IVAx1aSyaqa6HsOaS6/NhePp4YkqXGGQJIaZwh0bd8DSEfg1+Yi8RqBJDXOIwJJapwhkKTGGYJGJdmQ5N4kU0ku63se6bAkH07ynST/1fcsrTAEDUoyBlwDnAusBy5Msr7fqaQnfATY0PcQLTEEbToTmKqqvVV1ELgB2NjzTBIAVfUV/KTCRWUI2rQSeGBoPT3YJqlBhqBNGbHNnyOWGmUI2jQNrB5arwL29zSLpJ4ZgjbtAtYlOSPJCmY/K3p7zzNJ6okhaFBVHQIuAXYC9wDbqmpPv1NJs5J8DPga8Lwk00ku7numpzvfYkKSGucRgSQ1zhBIUuMMgSQ1zhBIUuMMgSQ1zhBIv4AkZyd52dD6rUne1OdM0lO1rO8BpOPc2cDDwH8AVNUHep1G+jl4RCCNkOTTSW5LsifJpsG2DUluT7I7yReSrAHeCvxVkm8k+YMkVyT56yTPT/KfQ4+3Jsmdg9u/l+TLg8ffmeQ3+vgzSod5RCCN9uaq+l6Sk4BdST4D/Cvwiqr6VpJnDe7/APBwVf0jQJJXAVTVPUlWJHlOVe0Fzge2JVkObAU2VtVMkvOBfwDe3McfUgJDIB3JpUleO7i9GtgEfKWqvgVQVQt5v/xtwBuAdzMbgvOB5wEvAG5OAjAGfPvYji49NYZAmiPJ2cA5wFlV9UiSLwG7mf1H/Kn4OPCJJJ8Cqqq+meR3gD1VddaxnFn6RXiNQJrvFODBQQR+C3gpcCLwyiRnACR51mDfHwDPHPUgVfU/wGPA3zMbBYB7gfEkZw0eZ3mS3+7sTyItgCGQ5rsJWDa4uPsu4FZghtnTQ59Kspuf/sP+WeC1hy8Wj3isjwMXMXuaiMFHg74OeM/gcb4BvGzE75MWje8+KkmN84hAkhpnCCSpcYZAkhpnCCSpcYZAkhpnCCSpcYZAkhr3/1OLrGFCx8jQAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1289,7 +1165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1298,7 +1174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1307,7 +1183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1340,62 +1216,40 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.786704</td>\n",
-       "      <td>0.213296</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cn1ccnc1Sc2ccc(cc2Cl)Nc3c4cc(c(cc4ncc3C#N)OCCCN5CCOCC5)OC</td>\n",
-       "      <td><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.584939</td>\n",
-       "      <td>0.415061</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Cc1c2cc(c(cc2oc(=O)c1Cc3ccccc3)OC(=O)N(C)C)Cl</td>\n",
-       "      <td><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.927561</td>\n",
-       "      <td>0.072439</td>\n",
+       "      <th>45</th>\n",
+       "      <td>0.625167</td>\n",
+       "      <td>0.374833</td>\n",
        "      <td>1</td>\n",
-       "      <td>Cc1c2cc(ccc2[nH]n1)c3cc(cnc3)OC[C@H](Cc4ccccc4)N</td>\n",
-       "      <td><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
+       "      <td>Cc1cnc(nc1c2cc([nH]c2)C(=O)N[C@H](CO)c3cccc(c3...</td>\n",
+       "      <td><img data-content=\"rdkit/molecule\" src=\"data:image/png;base64,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\" alt=\"Mol\"/></td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0.971956</td>\n",
-       "      <td>0.028044</td>\n",
+       "      <th>397</th>\n",
+       "      <td>0.702075</td>\n",
+       "      <td>0.297925</td>\n",
        "      <td>1</td>\n",
-       "      <td>c1ccc2c(c1)c(c[nH]2)C[C@@H](COc3cc(cnc3)c4ccc5cnccc5c4)N</td>\n",
-       "      <td><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAADICAIAAAAiOjnJAAAABmJLR0QA/wD/AP+gvaeTAAAV4klEQVR4nO3da1RTx9oH8CcJCYS7oEVREEEuhXpFDwfReq1KpdTXNiq2WD3tire3EVttRBel2ENXrBbT4upqerFVWfWIlSqtAuINLR5RrAqKeIuI9VUqggIJIEnm/TA2pdyykzCS43l+H1ya7JlMdv6ZPbP37MgjhABC3Y3f0w1ATycMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg8WKXA7HjsHmzT3djh6Cwep+paVw6xb4+EBREdTWwrvvQlVVT7fpicNgdaeWFli/HkaPhoULgRAYNAh274a0NPD3h+XL4e7dnm7fE4TB6janTsHIkbB6NTx6BEFBYDDAzJnA48HMmdDYCJ99BoGBsHYt1NS09HRLnwiCrKbRaJKS/k8gIAAkMJDs20fq6v6yQUkJkUgIj0c8PPR9+wbK5fKampoeauwTgsGy1vHjx4ODg4cNGy8UGmQy8sMPZMAAsmhRB1sWFZElS/Lo99nd3X3dunUPHz584u19QjBYlnv48OGiRYt4PB4ADB069NChu3PmEAACQKKiSFNTx6UKCwunTJlC4+Xh4XHs2LEn2+onBINlocOHDw8YMAAARCJRSkrKjh07Bg0K8PaudHQkCgXR6UwULywsnDx5sr29fXx8vEajeSJNfqIwWJZoampKTU3l8/kjRozIycmZMWMG7YH+8Y9ktdqMevr06QMAt2/fZtbSHoPBskR1dTUAuLi46HS61NRUOmb65ptvDAYDl+KnT5/etGlTYWFhUFAQAFy6dIl1g588uyc9C30quLi4AEBjY6NAIFi1atX9+/dXrlzZr18/jsUPHDiwdu3axMREV1dXAKivr2fY1h5idrC0Wm1ZWdmpU6eCg4NDQkL69+/Polk2TiQSOTg4NDU1NTY2isXiTz75xKziNJd1dXU0WHV1dUxa2aPMC1ZTU9OECRMMBsOZM2foI/b29gEBAWFhYaGhoWFhYf7+/qGhoWKxmEFTbYuLi0tTU1NdXZ0Fb9aYJ2PCur99Pc28YEml0tOnT/fr12/+/Plqtbq8vLy6urqsrKysrMy4jVAo9Pf3f/bZZyMjI997773ubrCtcHV1vXfvXn19vZeXlwVlAaC+vh4PhQAAaWlp27dvd3Z2zsvLGzJkCH2wtrZWrVZfvHixrKyM/uVyK1euXPn666/ZtLyHWXMUM3ZUdCDxVPZYXGeFBw8etLOz4/F4GRkZr7zyyqlTpzrbsrGx8dy5cxkZGXTXnz17tpvmGbbl+eefB4AjR45YULaoqAgARo0atXr1agBITU3t7tb1PE4XoSsqKubOnavT6ZKSkg4fPrx79+758+fr9foON3ZwcBg2bNhrr702f/58APjuu++66StgW6w5ihnL0q7rqTwUmg5WQ0NDbGxsdXV1bGysp6fnli1bxGJxRkaGQCDorEhaWlpYWNjMmTMBICMjo7m5uTubbBu65VBoO2Os5ubm3Nzcn376qbKyslsqNBEsQsibb75ZWloaHBy8dOnSVatW8Xi8LVu2hIeHd1HqxIkTZWVlxcXFI0aMuH///t69e7ulrTbFmgmdm5ubq6uri4tLd51uqKqqunr1qsXFT548GR4ePmPGjLi4uMDAwEWLFt2+fdvKJpkYY61btw4AXF1dDx48SK8/JCYmmjy+7tu3DwCCgoI+++wzAJg2bVr3HLdtyapVqwBAoVAQQlpaWq5fv25BJVlZWQAQGxtrcTMqKiqmTp3q7+/v6OgYGRmZnZ1tVnGNRpOQkEAPPgEBAdOnT+fz+QDg4OCwfPnyO3fuWNywroK1d+9ePp/P5/OzsrJoFzV16lSdyeurhOj1eh8fHwDYv3+/WCzm8/kVFRUWN9E20a/c2rVrCSHffvutUCiMj4+/evUq9xo0Gk18fLynp6ezs7NCoTD3UrRer09PT3d2dqbf/F69etGeYvz48QUFBVxqOHbsGL2mZGdnJ5PJGhoaCCEFBQXx8fE0avb29lKp1LJ4dRqs8vJyNzc3+qWcO3cu7YFqa2s51rtmzRoAWLBgQVxcHACkpKRY0DibpdPpYmNjASAkJOTKlStJSUl2dnYAIBKJFi9eXFlZabKGffv20e8eLQgAffv2VSqVjY2NXBpw7dq1iRMn0oIxMTG3b9+ur69XKpXGk2pRUVGHDx/urPiDBw9kMhntnIYMGWKc4//8888ikUgqlR4+fDg+Pp5u4OTkJJPJ7t69y3HnUJ0Ga+fOnSKRaNasWRUVFV5eXq6urmVlZdzrVavVPB7PycmJDrD8/Pz0er1ZLWNnz549RUVFWVlZHK8Zt3H+/PlRo0YBAO0t7OzsFixYUFBQIJVKjfGSSqW//fZbh8Vra2ulUin9+IcPH/7rr7/m5+f/7W9/o48888wzCoWii3i1tLQolUpHR0cA8PLy2rVrF318586dkZGRmZmZCoXC2HtFRUW1772MmRYKhXK5vLm52fhUcnIyDZNYLF6xYsXRo0clEgldcObs7GzWwtdOgyWRSABAqVQSQiorKw8dOsSxRqPx48cDwJdffhkQEAAABw8eNLeG9tRqtVqttnhd7927d+Pj4wFg0KBBAPDcc89lZmZyj9ejR48UCoVIJAIAb2/vr7/+WiaT2dvb0w8pPj7+wIED8+bNM342CQkJv//+e+sasrOzvb296bMKhaL1uCI/P5/mFQB8fHyUSmVTu7WCJSUlo0ePpttIJJLq6mrjU3RvA8CECRNyc3MVCoW7uzt9ZMqUKbRPqqmpMWY6MjLy4sWL7d/jxYsXjYdCR0dHmUyWk5Pz4osv0lLu7u6FhYVc9lWnwfr+++8BYNSoUVxq6dC2bdvoG/jggw8AYN68eRZXRQjRarVyudzOzm7kyJEuLi5yuZz7cZn67rvvPDw8AMDFxWXu3Ll0mR4AjB49+kBOjunyJ0/ulkgAgM/nL1u2rO6PZe0VFRXGvorGKy8vj342QqHQOKi/c+fOK6+8Ql9x7Nix5eXlHb5Ifn7+yJEj6Wa+vr4qlaqlpYX8NdN+fn55eXltCjY0NCiVymeeecbYV2VnZ69Zs4bOXnk8XkREBH37Tk5OSqWy6wPI+fPnZ86caeyrEhMT8/LyYmJixGKxRCJp3cl1ptNgNTY20h713LlzJmvpkEajcXNzc3BwOHPmjEAgcHBwsLinOXr0aGBgIAAIBAL6FwDw8PBITU2tr683Wfz27dsvv/wyLTV9+nQ6k2hublapVLT/yB4/nvz976SzKZVGQ1asIAIBAfjf2NgOFxPfuHGjzaEwPz9fpVLRZzMzMz09Peko2+SHqtfrMzMzw8LCaIMHDx6clJQUGhpK8yGVSuva3KrRSn19fetD4ZQpUw4cOJCcnExPbfD5/HHjxl2+fNnkHqNKSkrooZDH49E+j/aCrXvKznQ1K1y2bBkALF++nGM72jty5AgNU3h4uFAojIyM3Lt3L/0KcqTRaORyOT24PPfcc0VFRYSQwsLCmJgYuu88PT2Tk5M7uyvBYDCoVCr6rXV3d1epVG0OfFqtNi0trSUk5PFi9bFjSZsx77FjJCiIABA7OyKTkYaGLlqrVqulUik9jtB4FRUVvfDCC7Sp0dHRN2/e5PjGabxCQkLgj5OxgwcP5ngFqba2NikpiZYSCASVlZXnz5+n+8qCYWVRUZHxopOfnx8AqDksk+0qWMbWtD/Ym6WkpMTR0dHBwYHu3759+65atYrLVGD//v2+vr7GYWabZhw/fnzSpEm0zt69e7efsbeZOnU2miaEkIYGsn496d37cbxkssePNzYSb28CQIYNI8XFHN/vhQsXWo95afMyMjI4Fm9Np9PRFSKBgYEcJ4xG1dXViYmJUqmUEEJP7js5OVnQhtbo4gMuBzETJ0hHjBgBADt37rS4Kffv36eD99mzZ6tUquHDh8MfwsPDlUrl/fv325dqP3XqrP7jx48bx619+vRRKBRarZZOnZycnOCvUycTGhqIUkm8vMiGDSQ1lSQlEYOB7NlDPvyQPHpk7hunwxSFQhEXF1dVVWVucaMTJ04AQERExPXr13NycsyamxsZDAaBQMDj8bichuxCVFQUABw/ftzkliaClZ6eDlacOtfpdNOnTweAESNGGLuT4uJimUxGxxwA4ODgIJFI8vPzjb10F1Onzuzbt884XerXrx+NMo/HW7hwodkDu/p68tFHhBCSm0vOnDGvLAMXLlwAgNDQ0A8//BD+OCVrAXpkNHfG04ZE8trAgYNzc38xuaWJYNXW1tJT59wHB60lJCTQPuPWrVttntJoNNu2bZs4cSI9ZNAxxOrVq6Ojo+k/J0yYYNaJbNJqxu7u7t6/f//2UyeubClY9KrwgAEDlEolALz99tuW1UNnwZZ9jkazZxMAsmOH6S1Nr8eip93XrVtnbiO2b99Oh0ddX2GorKxUKBT0xBLdnsvUqTMGg4HuwfT0dAuKP3bkyJ+Hwp5WW1tLx+9btmwBgDfeeMOyeui88sKFC9Y05q23CAD58kvTW5oOVn5+PrQ6db548eLQ0FCJRCKXy7du3VpcXNzQ0UTp119/pWeHjVPurun1+tzcXDr7u3HjBpcinendu7c132xbo9PpeDwen8/ftWsXAMyaNcuyeiIiIgDgxIkT1jTmnXcIANm40fSWppcmT5482d/fX61WHz16dNKkSSUlJW0WuQsEgoEDB4aEhMyaNevNN98EgKqqqpdfflmr1S5ZssQ4Bu/CoUOHHj58GBERYTAYxGIxndNahhDy4MEDACgtLbW4EpsiEAgcHR01Go1QKAQr1th0y9ovFxcAAC51mA4Wj8ebP3/+Bx98sGzZMplMtnr1apFIVFdX13qdO73SEhwcDAAtLS1z5sy5devWmDFj6LDApPfff//EiRPZ2dnwxzoni9XV1el0OgAoKSmxph6b4urqqtFo6Okxi4PVLXcE0Q+HSx2cbqZYuHDhgwcPlErl0qVL6SNubm6DBw/29/ePiYlZuXKlnZ2dXq+nazDefvvtgoICHx+fH3/8kV6CMIm+WzqKtzJYtbW1AOsA3q2pyb15897AgX2sqc1GuLi43Llzh+4fi7ucbllUGB0N3t7w7LOmt+QULF9f37Vr1/bv37+8vLy8vPzSpUs1NTVnzpwx3l0IAEKhMCAgwNnZubi4WCwW//jjj8brVia13llWBuv3330AkgAAYFZ5OQwcaE1ltoJmghACPXEoPH8ehg17/PetW2HGDPjlF2h1OrJjXG//6t2798qVK43/7PCur/LycvoGJBJJ12uX2+jGYNXX/7kSv7QUpk2zpjJbQTNhMBjAzGCp1epFixalpKSMGTPGgkOhVgvr1sGGDbB9O8ybB3r94x9W5XSLrjVzhNa0Wu3Zs2eXLFkCAO7u7l1dP2mHHjH37NkDAC+++KI1zcjMfHxVBoC8/ro1NdkQegV99+7ddEEflyI6ne7jjz+md2lPmjSpqalp8uTJPB4vLCyM47n73NxGHx8CQEQikpZGMjOJnx9JTia7dpF33zVdvPt/beall14CgFdffZXj9k1NTQAgEol27NgBAHPmzLHm1VWqP4M1cKA1NdkQuoZs6dKlHM/tlZaWGlcOSiSS/fv305NY9MKlQCB4/fXXu1jjQK+nBQfPEQrJ8OEkN5fExj7epVOnkpYWEhVlug3dH6ybN2/SXveHH37gsv29e/cAwNPTU6VSAcBbb71lzauvX/9nsHg8Ys5CCttVVlZGL1iFhoZu3bq1i3jRZVt07aG3t3dmZqZcLjfeK5GVlSWXy+lqAD6fL5FIrly50qaGzMxMur5ZLBarVJf+9S/i4UEAiJsbUSoJ95PWTH4fa/PmzUKhY0zMUS6X6a5fvw4Afn5+GzduBIAVK1ZY99J/BmvAAKLVWlOZrTAYDF999dXAP2Yiw4YN27NnT/sFMGfPnqWLBuiyrdzc3Nb3Shiv1VZWVrZZ+Nr60tm0adMAYOzYsfn5+dHR/+Pn1wxAXnqJmDO0IYRRsPR6vURSC0AWLDC98blz5wBgyJAhycnJAPD+++9b/LrvvUeOHiWxsWTCBJKcTDjcqPaf5NGjR1u3bvX396fxGjJkSJt11fQya1BQUE5OjlQqNf44aoe/h3Dz5s02C1/pYtcbN258/vnnaWlpdG3IuHGJ339vSWtZ/aLf5ctELCYAhMuiX51O9/Dhw3feeQcANmzYYPGLpqeTjz8m6ekkJYVs2kTi4iyuyXbRha/GnyWLiIgw3ktYUVGxZs2arKwserW0/b0S7bVf+PrLL78YlyFJJJI2a/a5Y/hTkQoFASC+vm1/9Ly9u3fvZmRk0H77iy++sPgV09Mfz1k2bSKEPP7zqdR6XTUA0FtVq6qq6DAfOr9XokPl5eXGe0DoAL9///7m3vvaBsNgtbSQ8HACQP75zw6e1WhITg5JSkofOnSoceXMihUruKyn7kx6+uM5y1MfLEqj0WzYsIHeoQ4A9ODl5OT06aefWrA25OLFixKJ5NNPP5VKpQ8ePLCybWx/3PbcOZKSQlp3xmfPko8+IhMnEnt7AkDGjdtG90V0dHRaWtq1a9e6twEFBZwWD/1Ho/fneHl5xcfHP//88+0nej2CRwjhcBrVQnI5zJgBJSWg00FCAiiV8O9/Q2YmAIBAAOHh8OqrlaNHq8eMGcPxqqK5Hj2C3bshLo5F3bZFq9WKRCK6/rin2wJgwY/bmsV4BaCuDpRKOHUKZs+GXr3ghRdg0iTo1QsAfAF82TVg40aIiGBXvQ2hq99sB9sea/Nm6NsXTp6EAQMe91gJCexeDdkQ5v+t3MyZcPIk6xdBNodtj4X+a+F/hImYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WYwGAhJjBYiAkMFmICg4WY+H/YDSNkJLb1KAAAAABJRU5ErkJggg==\" alt=\"Mol\"/></td>\n",
+       "      <td>c1ccc(cc1)[C@@H](C(=O)N)NC(=O)c2cc(c[nH]2)c3c(...</td>\n",
+       "      <td><img data-content=\"rdkit/molecule\" 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\" alt=\"Mol\"/></td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "        neg       pos  active  \\\n",
-       "0  0.786704  0.213296       1   \n",
-       "2  0.584939  0.415061       1   \n",
-       "4  0.927561  0.072439       1   \n",
-       "7  0.971956  0.028044       1   \n",
+       "          neg       pos  active  \\\n",
+       "45   0.625167  0.374833       1   \n",
+       "397  0.702075  0.297925       1   \n",
        "\n",
-       "                                              SMILES  \\\n",
-       "0  Cn1ccnc1Sc2ccc(cc2Cl)Nc3c4cc(c(cc4ncc3C#N)OCCC...   \n",
-       "2      Cc1c2cc(c(cc2oc(=O)c1Cc3ccccc3)OC(=O)N(C)C)Cl   \n",
-       "4   Cc1c2cc(ccc2[nH]n1)c3cc(cnc3)OC[C@H](Cc4ccccc4)N   \n",
-       "7  c1ccc2c(c1)c(c[nH]2)C[C@@H](COc3cc(cnc3)c4ccc5...   \n",
+       "                                                SMILES  \\\n",
+       "45   Cc1cnc(nc1c2cc([nH]c2)C(=O)N[C@H](CO)c3cccc(c3...   \n",
+       "397  c1ccc(cc1)[C@@H](C(=O)N)NC(=O)c2cc(c[nH]2)c3c(...   \n",
        "\n",
-       "                                                 Mol  \n",
-       "0  <img src=\"data:image/png;base64,iVBORw0KGgoAAA...  \n",
-       "2  <img src=\"data:image/png;base64,iVBORw0KGgoAAA...  \n",
-       "4  <img src=\"data:image/png;base64,iVBORw0KGgoAAA...  \n",
-       "7  <img src=\"data:image/png;base64,iVBORw0KGgoAAA...  "
+       "                                                   Mol  \n",
+       "45   <img data-content=\"rdkit/molecule\" src=\"data:i...  \n",
+       "397  <img data-content=\"rdkit/molecule\" src=\"data:i...  "
       ]
      },
-     "execution_count": 17,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1406,7 +1260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1416,7 +1270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1458,7 +1312,7 @@
        "Index: []"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1476,16 +1330,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.6702756252973592"
+       "0.000582675077021122"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1493,6 +1347,13 @@
    "source": [
     "model.fit(dataset)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -1511,7 +1372,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.6.10"
   },
   "nteract": {
    "version": "nteract-on-jupyter@1.9.7"