122 lines (121 with data), 3.2 kB

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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 14.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediting SIF Stability...\n",
      "Prediting SGF Stability...\n",
      "Predicted SIF/SGF stability saved to the original file:  Sample_Sequence.csv\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>Stability_in_SIF</th>\n",
       "      <th>Stability_in_SGF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Oxytocin</td>\n",
       "      <td>CC[C@H](C)[C@H]1C(=O)N[C@H](C(=O)N[C@H](C(=O)N...</td>\n",
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       "   Compound                                             SMILES  \\\n",
       "0  Oxytocin  CC[C@H](C)[C@H]1C(=O)N[C@H](C(=O)N[C@H](C(=O)N...   \n",
       "\n",
       "  Stability_in_SIF Stability_in_SGF  \n",
       "0       Not Stable           Stable  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lib.pred_util import model_predict, pep_feat, save_results\n",
    "\n",
    "#Define the path to the file that includes peptide sequence\n",
    "Peptide_Path = 'Sample_Sequence.csv'\n",
    "\n",
    "#Featurising Peptides\n",
    "peptide_features= pep_feat(Peptide_Path)\n",
    "\n",
    "#Make Predictions \n",
    "SIF_Stability = model_predict(feat=peptide_features,Env='SIF')\n",
    "SGF_Stability = model_predict(feat=peptide_features,Env='SGF')\n",
    "\n",
    "#Save Results\n",
    "save_results(Peptide_Path,SIF_Stability,SGF_Stability)\n"
   ]
  }
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