--- a
+++ b/DEMO/oneliner_repurpose_LCK_gene.ipynb
@@ -0,0 +1,199 @@
+{
+ "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.oneliner as oneliner\n",
+    "from DeepPurpose import dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target, target_name = dataset.load_LCK()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'MGCGCSSHPEDDWMENIDVCENCHYPIVPLDGKGTLLIRNGSEVRDPLVTYEGSNPPASPLQDNLVIALHSYEPSHDGDLGFEKGEQLRILEQSGEWWKAQSLTTGQEGFIPFNFVAKANSLEPEPWFFKNLSRKDAERQLLAPGNTHGSFLIRESESTAGSFSLSVRDFDQNQGEVVKHYKIRNLDNGGFYISPRITFPGLHELVRHYTNASDGLCTRLSRPCQTQKPQKPWWEDEWEVPRETLKLVERLGAGQFGEVWMGYYNGHTKVAVKSLKQGSMSPDAFLAEANLMKQLQHQRLVRLYAVVTQEPIYIITEYMENGSLVDFLKTPSGIKLTINKLLDMAAQIAEGMAFIEERNYIHRDLRAANILVSDTLSCKIADFGLARLIEDNEYTAREGAKFPIKWTAPEAINYGTFTIKSDVWSFGILLTEIVTHGRIPYPGMTNPEVIQNLERGYRMVRPDNCPEELYQLMRLCWKERPEDRPTFDYLRSVLEDFFTATEGQYQPQP'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checking if pretrained directory is valid...\n",
+      "Beginning to load the pretrained models...\n",
+      "Using pretrained model and making predictions...\n",
+      "repurposing...\n",
+      "in total: 6111 drug-target pairs\n",
+      "encoding drug...\n",
+      "unique drugs: 6111\n",
+      "drug encoding finished...\n",
+      "encoding protein...\n",
+      "unique target sequence: 1\n",
+      "protein encoding finished...\n",
+      "Done.\n",
+      "predicting...\n",
+      "---------------\n",
+      "Predictions from model 1 with drug encoding MPNN and target encoding CNN are done...\n",
+      "-------------\n",
+      "repurposing...\n",
+      "in total: 6111 drug-target pairs\n",
+      "encoding drug...\n",
+      "unique drugs: 6111\n",
+      "drug encoding finished...\n",
+      "encoding protein...\n",
+      "unique target sequence: 1\n",
+      "protein encoding finished...\n",
+      "Done.\n",
+      "predicting...\n",
+      "---------------\n",
+      "Predictions from model 2 with drug encoding CNN and target encoding CNN are done...\n",
+      "-------------\n",
+      "repurposing...\n",
+      "in total: 6111 drug-target pairs\n",
+      "encoding drug...\n",
+      "unique drugs: 6111\n",
+      "drug encoding finished...\n",
+      "encoding protein...\n",
+      "unique target sequence: 1\n",
+      "protein encoding finished...\n",
+      "Done.\n",
+      "predicting...\n",
+      "---------------\n",
+      "Predictions from model 3 with drug encoding Morgan and target encoding CNN are done...\n",
+      "-------------\n",
+      "repurposing...\n",
+      "in total: 6111 drug-target pairs\n",
+      "encoding drug...\n",
+      "unique drugs: 6111\n",
+      "drug encoding finished...\n",
+      "encoding protein...\n",
+      "unique target sequence: 1\n",
+      "-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n",
+      "protein encoding finished...\n",
+      "Done.\n",
+      "predicting...\n",
+      "---------------\n",
+      "Predictions from model 4 with drug encoding Morgan and target encoding AAC are done...\n",
+      "-------------\n",
+      "repurposing...\n",
+      "in total: 6111 drug-target pairs\n",
+      "encoding drug...\n",
+      "unique drugs: 6111\n",
+      "rdkit not found this smiles: [Y+3] convert to all 1 features\n",
+      "rdkit not found this smiles: [K].I convert to all 1 features\n",
+      "drug encoding finished...\n",
+      "encoding protein...\n",
+      "unique target sequence: 1\n",
+      "-- Encoding AAC takes time. Time Reference: 24s for ~100 sequences in a CPU. Calculate your time by the unique target sequence #, instead of the entire dataset.\n",
+      "protein encoding finished...\n",
+      "Done.\n",
+      "predicting...\n",
+      "---------------\n",
+      "Predictions from model 5 with drug encoding Daylight and target encoding AAC are done...\n",
+      "-------------\n",
+      "repurposing...\n",
+      "in total: 6111 drug-target pairs\n",
+      "encoding drug...\n",
+      "unique drugs: 6111\n",
+      "drug encoding finished...\n",
+      "encoding protein...\n",
+      "unique target sequence: 1\n",
+      "protein encoding finished...\n",
+      "Done.\n",
+      "predicting...\n",
+      "---------------\n",
+      "Predictions from model 6 with drug encoding Transformer and target encoding CNN are done...\n",
+      "-------------\n",
+      "models prediction finished...\n",
+      "aggregating results...\n",
+      "---------------\n",
+      "Drug Repurposing Result for Tyrosine-protein kinase Lck\n",
+      "+------+-------------+-----------------------------+---------------+\n",
+      "| Rank |  Drug Name  |         Target Name         | Binding Score |\n",
+      "+------+-------------+-----------------------------+---------------+\n",
+      "|  1   |   441336.0  | Tyrosine-protein kinase Lck |      3.39     |\n",
+      "|  2   |  6917849.0  | Tyrosine-protein kinase Lck |      6.10     |\n",
+      "|  3   |  23947600.0 | Tyrosine-protein kinase Lck |      8.76     |\n",
+      "|  4   |   27924.0   | Tyrosine-protein kinase Lck |      9.56     |\n",
+      "|  5   |   445643.0  | Tyrosine-protein kinase Lck |     13.61     |\n",
+      "|  6   |   16490.0   | Tyrosine-protein kinase Lck |     13.77     |\n",
+      "|  7   |   13109.0   | Tyrosine-protein kinase Lck |     14.80     |\n",
+      "|  8   |    6230.0   | Tyrosine-protein kinase Lck |     18.10     |\n",
+      "|  9   |  11180808.0 | Tyrosine-protein kinase Lck |     18.32     |\n",
+      "|  10  | 124079495.0 | Tyrosine-protein kinase Lck |     19.91     |\n",
+      "checkout ./save_folder/results_aggregation/repurposing.txt for the whole list\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "oneliner.repurpose(target = target, \n",
+    "                    target_name = target_name, \n",
+    "                    save_dir = './save_folder',\n",
+    "                    pretrained_dir = './save_folder/pretrained_models/DeepPurpose_BindingDB/')"
+   ]
+  }
+ ],
+ "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
+}