diff --git "a/UltraFlow/dataset/dataset_test.ipynb" "b/UltraFlow/dataset/dataset_test.ipynb"
deleted file mode 100644--- "a/UltraFlow/dataset/dataset_test.ipynb"
+++ /dev/null
@@ -1,738 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from UltraFlow import commons\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import rdkit\n",
-    "from rdkit import Chem\n",
-    "from openbabel import pybel\n",
-    "from tqdm import tqdm\n",
-    "from collections import defaultdict\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "outputs": [],
-   "source": [
-    "lig_path_sdf = r'D:\\jiaxianyan\\soft\\github\\UltraFlow\\UltraFlow\\data\\chembl\\P49841\\visualize_dir\\total_vs.sdf'\n",
-    "m_ligs = pybel.readfile('sdf', lig_path_sdf)\n",
-    "name = 'P49841'\n",
-    "dataset_path = r'D:\\jiaxianyan\\soft\\github\\UltraFlow\\UltraFlow\\data\\chembl'\n",
-    "ligcut = 5.0\n",
-    "protcut = 30\n",
-    "prot_graph_type = 'residue_complete'\n",
-    "n_jobs = 8\n",
-    "lig_max_neighbors = None\n",
-    "prot_max_neighbors = 10"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "outputs": [],
-   "source": [
-    "valid_lig_coords_list, valid_lig_features_list, valid_lig_edges, valid_lig_node_type, valid_index = commons.read_ligands(name, dataset_path, ligcut, lig_type='openbabel')"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "@> 5505 atoms and 1 coordinate set(s) were parsed in 0.06s.\n"
-     ]
-    }
-   ],
-   "source": [
-    "prot_coords_valid, prot_features_valid, prot_edges, prot_node_type, sec_features, alpha_c_coords, c_coords, n_coords =  commons.read_proteins(name, dataset_path, prot_graph_type, protcut)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Get ligand graphs: 0it [00:00, ?it/s][Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.\n",
-      "Get ligand graphs: 24it [00:12,  1.95it/s][Parallel(n_jobs=8)]: Done  34 tasks      | elapsed:   12.8s\n",
-      "Get ligand graphs: 1613it [00:15, 105.37it/s]\n",
-      "[Parallel(n_jobs=8)]: Done 1500 tasks      | elapsed:   15.4s\n",
-      "[Parallel(n_jobs=8)]: Done 1613 out of 1613 | elapsed:   15.6s finished\n"
-     ]
-    }
-   ],
-   "source": [
-    "lig_graphs = commons.pmap_multi(commons.get_lig_graph_equibind,\n",
-    "                                        zip(valid_lig_coords_list, valid_lig_features_list, valid_lig_node_type),\n",
-    "                                        max_neighbors=lig_max_neighbors, cutoff=ligcut,\n",
-    "                                        n_jobs=n_jobs, desc='Get ligand graphs')"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Get protein alpha carbon graphs: 0it [00:00, ?it/s][Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.\n",
-      "Get protein alpha carbon graphs: 1it [00:00, 1002.70it/s]\n",
-      "[Parallel(n_jobs=8)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
-     ]
-    }
-   ],
-   "source": [
-    "prot_graphs = commons.pmap_multi(commons.get_prot_alpha_c_graph_equibind,\n",
-    "                                 zip([prot_coords_valid], [prot_features_valid], [prot_node_type], [sec_features], [alpha_c_coords], [c_coords], [n_coords]),\n",
-    "                                 n_jobs=n_jobs, cutoff=protcut, max_neighbor=prot_max_neighbors,\n",
-    "                                 desc='Get protein alpha carbon graphs')"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "outputs": [],
-   "source": [
-    "prot_coords_list, graph_prot_index = [], []\n",
-    "index = 0\n",
-    "for prot_coord, valid_i in zip([prot_coords_valid], [valid_index]):\n",
-    "    prot_coords_list.extend([prot_coord] * len(valid_i))\n",
-    "    graph_prot_index.extend([index] * len(valid_i))\n",
-    "    index += 1"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "outputs": [],
-   "source": [
-    "from rdkit.Chem.Scaffolds import MurckoScaffold"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<rdkit.Chem.rdchem.Mol at 0x20615fb5440>",
-      "image/png": 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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CN1CCCCC1CCN2C3=CC=CC=C3SC4=C2C=C(C=C4)SC\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<rdkit.Chem.rdchem.Mol at 0x20614f3fdf0>",
-      "image/png": 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SVG9vr62tra2tbU9PD7mdnDlzxsnJicFg2NjY+Pv7p6enG/2R15UrVxwcHBBCW7ZsIWC9uClTpiCEHj16ZOpCbzVr1iyEUHV1NdmNAINAjFJUcXExQsjf35/sRrCffvoJIeTt7d3V1WW6KsXFxfjsJh6PZ9KFjvA9VFgsFsEP7n6ro6MDIeTs7Dw4OEhuJ8BA8N4oRVFn1AzvZM2aNSwWy3RVwsPDi4qKWCyWWCyOiYlRKpXGPf/QhlTLly+fPn26jY1NW1ubcUu8q9u3byOEgoKC8DENYL4gRimKau+HE9DJsmXLbt265eXldfPmzYiIiKGnWIZ4/PjxkSNHVq1axWazt2/fLhQKNRqNSqXq7OwMDw9vbm42vMS4UedXDAxF9uUwGIFer8cv/ZqamsjuBZs+fTpCqK6ujphyTU1N7733HkJo5syZjx8/Ht9J3rAhVUtLS0dHx5IlSxBCU6dOrampMW7/Y7dy5UqEEGyKZQEgRqnol19+QQh5enqS3Qj2/PlzhJCrqyuRe661t7ePI+bwt1lTU1O9vLyG0nPEDakwDOvp6cGnDzGZTHwZbIJpNBoHBwcajfby5UviqwPjghilotOnTyOE+Hw+2Y1gP/74I0IoJiaG4LpjjzmVSvXbDam8vb3xDan6+/tH+6BGo0lOTkYIOTk5Xbt2zQRf4k3wO/rf/e53BNcFpgBjo1REnVEzsjpxdnaWSCTJyckKhSIqKkomk732A11dXRcuXODxeG5ubvHx8RcvXlQqlUMbUuET/Hk83htmBNjb2+fk5OzYsUOlUsXHx+fk5Jj4O/0P6jxCBEZAdo6DEcyfPx8hVFFRQXYjWGBgIEKosLCQlOparXbHjh0IITs7u5ycHAzDnjx5kpmZ+dsp/AKB4MGDB+MoodfrDxw4gJ/nxIkTxv4Go8JXLTh79ixhFYHpQIxSjlwut7GxcXBweMMNKTH6+vrs7OzodPqrV6/I6kGv1+/duxchZGNjM3wxaScnp/Xr11+4cMEoY4v4PFQajZaenm742cYCX7urvr6emHLApCBGKUcikSCEQkNDyW4Eu3nzJkJoyZIlZDeCCQSCkJAQGo3GYrG2bt2ak5Nj9Mld//jHP/A9WlJTU039Zj6+dpe7uzvpUwCAUcB7v5RDnXV8qTN+l5aWduDAgZqamkWLFploQ6o9e/aw2ewPPvjg6NGjCoXi7Nmzpnsr/l//+hdCKCgoaGhoApg1eMREOXh4WfPzpRHR6fQlS5aYKENxSUlJV69edXFxuXjx4vr16zUajRFPrtfrq6qqDh06FBAQcODAAR8fn8ePH7969cqIJQBZYEs7atHpdK6urr29ve3t7Ww2m8ROMAxjs9ldXV1PnjyZOXMmiZ0Q7M6dO7GxsS9evAgLC8vLy8OXVR43jUZTXFycl5cnEona29vxg+7u7lqtVqlUBgQEXL161cPDwxiNA/KQPaoA/kdVVRVCaM6cOWQ3gj148AAhxGazyW6EBHV1dfgjIH9//87OznGcQS6X5+TkbN26dXgKz5gxA3+bdWBgoLGxEV/0et68ec3NzUb/CoBIMDZKLQqFwsfHh06n63Q6cjetxO/o8QmL1obD4eD7pFZVVa1atUomk+EzYt/q6dOn165dE4vFMplsYGBg6Gx8Pp/H4y1dunRoMHTGjBklJSVcLvfu3bvBwcHk7pMKDEV2joP/0d/f7+PjgxDCx+ZI7GT37t0IocOHD5PYA7na29sXL16MEPL29n7zm0lv2JDq6dOnb/igQqEIDQ1FCLm5uZWXlxv7GwCCQIxSTmVlpbu7O0IoPDz8tZngRFqwYAFC6J02B7U8CoUiJCRkxJgb2pAKX0gFx2Aw8A2pFArFGEtoNBp8NX5nZ2eZTGaCLwFMDmKUimpra/GxuYCAgPGNzRlIqVTS6XR7e3u1Wk18dUpRqVSxsbFDMdfX1ycSiT7++OPhDwA9PDy2bt0qEonGdwOh1Wp///vfI4Ts7e2FQqHRvwIwNYhRimpoaJg9ezZCaP78+W++MTSFa9euIYRWrFhBcF1qGhgY2LRpE0JowoQJwzek4nA4Bw8erKioMPwter1ev3//fnw04NSpU0ZpGxAGYpS62traFi1ahBDy8fEheNZgeno6QuiPf/wjkUWpTKfTRUZGTp06lUaj4RtS/fLLL0avMjQnNSMjw+gnB6YDMUppCoUCf1bu5uZG5EolUVFRCKFLly4RVpH6tmzZghASCAQmrXL8+HHC5qQCY4EYpTqVShUTE4OPzRUUFBBQUafT4Wt3Pn/+nIBy5oKwXTyzs7MnTJiAENq2bRvsdmcWIEbNQH9//8aNG/FHEARcId67dw8h5Ovra+pCZoTgXTwLCwudnZ0RQgkJCfCUj/pgTr0ZsLOzy87O/uKLL/r7+1NSUs6cOWPSctevX0fUWJGEOgjexTMyMrKoqMjd3T0vLy82Nham3lMcxKh5oNFof/vb3wQCgU6n27Vr11//+lejl3jx4gW+BXFaWtqcOXPw9yUBjvhltwIDA2/evDlt2rTr169HRkZ2dXURVhq8M7Ivh8G7OXbsGP4IIi0tzSgnvH//vkAgCA4OHlo8iU6nZ2ZmGuXkFgN/0Jefn09wXXLfewNjBDFqfr7//nv8EcSePXvGt2GnTqf7+eef09PTORzO0D+oQ1sQt7a2Gr1nsza0i6dcLie+OonvvYExghg1S2Kx2NHRESGUlJQ09pkzarW6oKAgNTV1+G4cbm5u+BbEJO4UQnH4CrAk7uJJ1ntvYIwgRs3VzZs38deSIiIi3pyAoy3alpqaWlBQMDAwQFjPZgofid61axeJPahUKi6Xi1+Tkr5JF3gNLJRnrlatWlVaWrp27dri4uLIyMirV6+yWKzhPzD2RdvAm1FhFwAGg5GXl7dgwQI6nV5eXr569WoSmwGvgRg1Y35+fqWlpVFRUXfu3Fm9erVUKvXy8qqrq5NIJGKxuKysDMMw9OuibXw+Pzk5eYzrZoLhKioqEAXeALOzs0MI3b9/H3+lFFAHbCJi9lpaWtauXVtXV8disRgMxtOnT/HjEydO5HK5iYmJMTExrq6u5DZpvpqamnx9fd3d3bu6usi9fn/x4sXkyZMZDEZ3dzcxr6+CMYJfhtmbNm1aSUlJXFyci4uLVCr18PDgcrl8Pj86Onr4ckRgfIa2RyV9DAS/vVi2bBlkKNXA78MSMJnMwsLC3t7exsbGZcuWkf4Hb0moMDA6vBPSxxbAb0GMWghHR0dHR0fYY9Lohq5GyW6EQjtvg9fA2CgAo1KpVPiwcnd3t5OTE4mdDA4Ourq6qtXqzs7O117JAKSDOfUAjKqyslKr1S5evJjcDEUIVVdX9/X1zZ07FzKUgiBGARgVde7oYWCUyiBGARgV1Z4vUaET8FsQowCMDMMwirx4j6h0XQx+C2IUgJHV19fL5XJPT09vb29yO2lpaXn27Jmrq+u8efPI7QSMCGIUgJHhF4BUWL66tLQUIbRixYqhNWEBpcBvBYCRUWc4kjqdgBFBjAIwMuoMR1KnEzAiiFEARoBhWHx8vJOTU01NDbmdqNXqe/fu0en0wMBAcjsBo4EYBWAENBpt6tSpKpVq9+7dx48fJ7GTn376aWBgYOHChbA+HmVBjAIwsr1793777bcYhn322Wd//vOfyWoD7uipD2IUgFGlpqZmZWXZ2tpmZGR89tlner2e+B7g+RL1wdIkALyFSCRKSUnRaDSbNm06f/48vi0rMTAMY7PZXV1dDQ0Nvr6+hNUF7wRiFIC3u3HjRkJCwqtXr9atWycUCvFtWQnw8OHDuXPnstns9vZ2YiqCcYCbegDeLiwsrLi42MPD49///nd4eLhcLiemLnWmAIA3gBgFYEz8/f1v3brl7e1dWVm5evXq1tZWAorCwKhZgBgFYKzmzZtXXl7u5+dXW1sbEhLy+PFjU1eEx/RmAcZGAXg3crl83bp1FRUVU6ZMkUqlCxcuNFGhysrKFStWTJgwQalUwu6EVAZXowC8Gzc3t4KCgqioqPb29rCwMPyC0Vj0en1VVdWhQ4c4HE5QUNDMmTO//PJLyFCKg6tRAMajv79/y5Ytly5dcnJyunTpEpfLNeRsarW6sLAwLy9PLBZ3dnbiBz08PJKSkr777jvY6pXiIEYBGCedTrdnz57Tp0/b2dlduHAhJSXlXc8gl8uLiorEYnFubm5PTw9+0NfXl8fj8Xi8sLAw2JLeLMAvCYBxotPpJ0+eZDKZhw8f3rx5c3d39+7du8fywebmZqlUKhaLpVLp4OAgfpDD4fD5fB6P5+/vb8qugfHB1SgAhsrIyDh48CCGYWlpaQKBYLQfq6urk0gkYrG4rKwM/7uj0+lBQUF8Pn/9+vVeXl4EtgyMCWIUACPIysr66KOPtFrt559/npmZObRMvU6nKy8vl0gkly9ffvToEX6QwWBERETw+fyEhIRJkyaR1zUwDohRAIwjNzf3gw8+0Gg0W7ZsOXbs2K1btyQSSW5u7vBHRlwul8/nR0dHw8N3SwIxCoDRFBQUJCcn9/b22traarVa/KCfn19CQkJiYqK/vz88c7dIEKMAGFNlZeWJEydkMtmUKVPi4uI2btwI23laPIhRAIxPo9E4ODiQ3QUgCMQoAAAYBCaDAgCAQSBGAQDAIBCjAABgEIhRAAAwyP8D4dqbJRvSPq0AAAGNelRYdHJka2l0UEtMIHJka2l0IDIwMjIuMDMuNAAAeJx7v2/tPQYg4GWAAEYgFgNiSSBuYGRjSADSTEzE0cw41MPEcckTMod4/dxA9zMyMTAxMzCzMLCwMrCyMbCxM7BzMHBwMnByMXBxM3DzMPDwMvDyMfDxM/ALMAgIMggKMQgJMwiLMIiIMrAyMAhyMPBxMogKMIiwsDGwsjAzMbJxcfPw8nGy8QsIcnDysQkJi4gKCIoLMUICCwzEsmclH/A+eWQ/iGO4ZP6Bc3v+gtm2Tw4dOHatDMz2891xQDPtvx2IHVzWdUByVqE9iN2b43fgkeuMvSC2du3T/RP37AWr+frTYH/vEVWw3ud7be2Vyvz3gdgq91/b6zvMAYunNx+07zukfQDEPuOo7uD8OgfMflje6NBUGABmX+TrcZCY9xSsvnNuoEPkTDcwO+N0mkPjB32wG/y9FRxWe94Cszdaddprhi0EsxkSPu45dVnQAeyeiFl2DmX5YLb2p7v2jD9awWy9SlMHn+NeYLYYAL80aXn3clgHAAACIHpUWHRNT0wgcmRraXQgMjAyMi4wMy40AAB4nH1UUY5bIQz8zym4QJDH2Bg+N5tVVVWbSG3aO/R/76/avO7CqqjvxRYPBhs845xSPN+v336/pY+Hr6dTSvSfX+89/SpEdHpNMUiXly9fb+n58XR5n3m+/7w9fiTmxOp7/P2MfXrcX99nkJ7TuWTV6jHTGVmLlV4SZRrP3MuBlNxbM193ZG9cWDbIEsiaqVOpSGfKvQpDN0gJpGbTLsV8XTw61w1Qj+SFa5dIGXhtG2A97sPs99DITSZFeYO0QCJbZ8/p68VIrW+ALYCUa0eBjJGY+vy/yO5IyiaAB3IgE2vfhYSTlJCbsInFKZjJaIvEQCrg1U5nzrX5xXbJEQT5OkrpiBoYoek2ZhAkmURbk1EtrV6FHVIGslht1I5qFed/hwyGPJIpC8XdzdRsi6wDWamihiZqM647GcHGhdTP2c1r4Py4NnfANmpExiDxAVcS2VGOflBJ3MVzsx+36VYcTIPKQpDqgg5Odd8VBz/mRdfipUKrum0KPuhpxRPCI6I2wQ74crt+6tCjZy/323X2bLw8G9M/Upnd5+GTzBaDm85Gglud7QI3mz0BtzaVD7c+5Y2wVcUYDotaMRwvqsRwZVEfhpNFZRhOFzVhuLqoBsPZIg8M1xYdYLi+EI5wvBKLcIyFQYRjXqiKqkxEOeJ+hGX5e7wZNg4c+2ylcSUtvt//pH18+gOyexv1T0HGVgAAAQ56VFh0U01JTEVTIHJka2l0IDIwMjIuMDMuNAAAeJw1kEuOAzEIRK+SZSK5EX+weulr5fBT9CiWLOEy8CiOnHPeuHrszLHzC/Rzjry+7yuJN1uui2mnq8S6L6fdXXtdgkBNIRlFpI0SVrYfRTWsp5DLLSZLqLZ2LyYrjpo0ptxi4pOXXmHrZioXmf5MyhobeULtWjEAVS4ZJURa16WUbfi6EYiBDXSxdK7biT3Am2Eit41ilS3rGcUanfFVoQ+/KqpqpOSUxJjZpenTOthzoYjhb95tAUtGku3PNAV0LCeAxysssqDExkz8O2XdDo9LgeyIqeJSQVtNdn82a5rbQd4OTw0pqGL7SI6ta67P9w9gSljVKbMCwQAAAABJRU5ErkJggg==\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C1CCC(CCC2C3CCCCC3CC3CCCCC32)CC1\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<rdkit.Chem.rdchem.Mol at 0x2061af426c0>",
-      "image/png": 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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "smiles = \"CN1CCCCC1CCN2C3=CC=CC=C3SC4=C2C=C(C=C4)SC\"\n",
-    "# convert SMILES string to RDKit mol object\n",
-    "mol = Chem.MolFromSmiles(smiles)\n",
-    "# create RDKit mol object corresponding to Bemis-Murcko scaffold of original compound\n",
-    "mol_scaffold = MurckoScaffold.GetScaffoldForMol(mol)\n",
-    "# make the scaffold generic by replacing all atoms with carbons and all bonds with single bonds\n",
-    "mol_scaffold_generic = MurckoScaffold.MakeScaffoldGeneric(mol_scaffold)\n",
-    "# convert the generic scaffold mol object back to a SMILES string format\n",
-    "smiles_scaffold_generic = Chem.CanonSmiles(Chem.MolToSmiles(mol_scaffold_generic))\n",
-    "# display compound and its generic Bemis-Murcko scaffold\n",
-    "display(mol)\n",
-    "print(smiles)\n",
-    "display(mol_scaffold_generic)\n",
-    "print(smiles_scaffold_generic)\n",
-    "display(mol_scaffold)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "outputs": [],
-   "source": [
-    "lig_path_smiles = r'D:\\jiaxianyan\\soft\\github\\UltraFlow\\UltraFlow\\data\\chembl\\P49841\\P49841_valid_smiles.smi'\n",
-    "with open(lig_path_smiles, 'r') as f:\n",
-    "    s = f.read().strip().split('\\n')\n",
-    "scaffolds = defaultdict(list)\n",
-    "error_smiles = 0"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1613/1613 [00:01<00:00, 851.98it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i, smiles in tqdm(enumerate(s), total=len(s)):\n",
-    "    try:\n",
-    "        # scaffold = MurckoScaffold.MurckoScaffoldSmiles(mol = Chem.MolFromSmiles(smiles), includeChirality = False)\n",
-    "        mol = Chem.MolFromSmiles(smiles)\n",
-    "        mol_scaffold = MurckoScaffold.GetScaffoldForMol(mol)\n",
-    "        mol_scaffold_generic = MurckoScaffold.MakeScaffoldGeneric(mol_scaffold)\n",
-    "        smiles_scaffold_generic = Chem.CanonSmiles(Chem.MolToSmiles(mol_scaffold_generic))\n",
-    "        scaffolds[smiles_scaffold_generic].append(i)\n",
-    "    except:\n",
-    "        print(smiles + ' returns RDKit error and is thus omitted...')\n",
-    "        error_smiles += 1"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "463\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(len(scaffolds))"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "Text(0, 0.5, 'Frequency')"
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "target_ligands_num = [len(l) for l in scaffolds.values()]\n",
-    "f = plt.hist(target_ligands_num, bins=62)\n",
-    "plt.xlabel('Ligand num')\n",
-    "plt.ylabel('Frequency')"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "outputs": [],
-   "source": [
-    "import pickle\n",
-    "test_dict_path = r'D:\\jiaxianyan\\soft\\github\\UltraFlow\\UltraFlow\\data\\horizontal_test.pkl'\n",
-    "with open(test_dict_path, 'rb') as f:\n",
-    "    test_dict = pickle.load(f)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "[13, 14, 15, 16, 17, 18, 19, 40]"
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_dict[list(test_dict.keys())[0]]"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "outputs": [],
-   "source": [
-    "lig_path_sdf = r'D:\\jiaxianyan\\soft\\github\\UltraFlow\\UltraFlow\\data\\chembl\\P49841\\visualize_dir\\total_vs.sdf'\n",
-    "m_ligs = pybel.readfile('sdf', lig_path_sdf)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "outputs": [],
-   "source": [
-    "test_m = []\n",
-    "for i, m in enumerate(m_ligs):\n",
-    "    if i in test_dict[list(test_dict.keys())[0]]:\n",
-    "        test_m.append(m)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "outputs": [],
-   "source": [
-    "import oddt\n",
-    "def is_molecule(obj):\n",
-    "    \"\"\"Check whether an object is an `oddt.toolkits.{rdk,ob}.Molecule` instance.\n",
-    "\n",
-    "    .. versionadded:: 0.6\n",
-    "    \"\"\"\n",
-    "    return is_openbabel_molecule(obj) or is_rdkit_molecule(obj)\n",
-    "\n",
-    "\n",
-    "def is_openbabel_molecule(obj):\n",
-    "    \"\"\"Check whether an object is an `oddt.toolkits.ob.Molecule` instance.\n",
-    "\n",
-    "    .. versionadded:: 0.6\n",
-    "    \"\"\"\n",
-    "    return (hasattr(oddt.toolkits, 'ob') and\n",
-    "            isinstance(obj, oddt.toolkits.ob.Molecule))\n",
-    "\n",
-    "\n",
-    "def is_rdkit_molecule(obj):\n",
-    "    \"\"\"Check whether an object is an `oddt.toolkits.rdk.Molecule` instance.\n",
-    "\n",
-    "    .. versionadded:: 0.6\n",
-    "    \"\"\"\n",
-    "    return (hasattr(oddt.toolkits, 'rdk') and\n",
-    "            isinstance(obj, oddt.toolkits.rdk.Molecule))"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "False"
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "is_molecule(m_ligs)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'Molecule' object has no attribute 'atom_dict'",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
-      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_30244\\668924972.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[0mtest_m\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0matom_dict\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
-      "\u001B[1;31mAttributeError\u001B[0m: 'Molecule' object has no attribute 'atom_dict'"
-     ]
-    }
-   ],
-   "source": [
-    "test_m[0].atom_dict"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'generator' object has no attribute 'atom_dict'",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
-      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_30244\\755322164.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[0mm_ligs\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0matom_dict\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
-      "\u001B[1;31mAttributeError\u001B[0m: 'generator' object has no attribute 'atom_dict'"
-     ]
-    }
-   ],
-   "source": [
-    "m_ligs.atom_dict"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<rdkit.Chem.rdchem.RWMol at 0x1c60415f7b0>",
-      "image/png": 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\n"
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "smi = 'Br.C[S+](CCC(N)C#N)C[C@H]1O[C@@H](n2cnc3c(N)ncnc32)[C@H](O)[C@@H]1O.[Br-]'\n",
-    "m = Chem.MolFromSmiles(smi, sanitize=True)\n",
-    "mw = Chem.RWMol(m)\n",
-    "mw"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<rdkit.Chem.rdchem.Mol at 0x1c6040ddc30>",
-      "image/png": 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\n"
-     },
-     "execution_count": 130,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from rdkit.Chem import GetMolFrags\n",
-    "from rdkit.Chem.Draw import MolsToGridImage\n",
-    "import numpy as np\n",
-    "frags = GetMolFrags(mw, asMols=True)\n",
-    "frag_size = [len(frag.GetAtoms()) for frag in frags]\n",
-    "max_frag_index = np.argmax(frag_size)\n",
-    "max_frag = frags[max_frag_index]\n",
-    "max_frag"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "'C[S+](CCC(N)C#N)C[C@H]1O[C@@H](n2cnc3c(N)ncnc32)[C@H](O)[C@@H]1O'"
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "smi_new = Chem.MolToSmiles(max_frag)\n",
-    "smi_new"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<rdkit.Chem.rdchem.Mol at 0x1c604167530>",
-      "image/png": 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\n"
-     },
-     "execution_count": 135,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def neutralize_atoms(mol):\n",
-    "    pattern = Chem.MolFromSmarts(\"[+1!h0!$([*]~[-1,-2,-3,-4]),-1!$([*]~[+1,+2,+3,+4])]\")\n",
-    "    at_matches = mol.GetSubstructMatches(pattern)\n",
-    "    at_matches_list = [y[0] for y in at_matches]\n",
-    "    if len(at_matches_list) > 0:\n",
-    "        for at_idx in at_matches_list:\n",
-    "            atom = mol.GetAtomWithIdx(at_idx)\n",
-    "            chg = atom.GetFormalCharge()\n",
-    "            hcount = atom.GetTotalNumHs()\n",
-    "            atom.SetFormalCharge(0)\n",
-    "            atom.SetNumExplicitHs(hcount - chg)\n",
-    "            atom.UpdatePropertyCache()\n",
-    "    return mol\n",
-    "max_frag = Chem.AddHs(max_frag)\n",
-    "neutralize_atoms(max_frag)\n",
-    "max_frag"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[H]O[C@]1([H])[C@@]([H])(C([H])([H])[S+](C([H])([H])[H])C([H])([H])C([H])([H])C([H])(C#N)N([H])[H])O[C@@]([H])(n2c([H])nc3c(N([H])[H])nc([H])nc32)[C@]1([H])O[H]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(Chem.MolToSmiles(max_frag))"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "1"
-     },
-     "execution_count": 137,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Chem.GetFormalCharge(max_frag)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file