pubmed_assay_train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad2d508d-2f07-4bcd-90f4-e8370565b28a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from lightning import pytorch as pl\n",
    "from chemprop import data, featurizers, models, nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5e8f2612-39fe-4744-80e8-d97bda6d41cb",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "     Unnamed: 0.1  Unnamed: 0          CID  1159552  1159553  1159555  \\\n",
       "0               0          19        196.0        0        0        0   \n",
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       "..            ...         ...          ...      ...      ...      ...   \n",
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       "\n",
       "     1224838  1224839  1224841  1224842  ...  1920064  1920067  1920068  \\\n",
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       "\n",
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       "\n",
       "                                                SMILES  \n",
       "0                                   C(CCC(=O)O)CC(=O)O  \n",
       "1                             C1(C(=O)NC(=O)N1)NC(=O)N  \n",
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       "..                                                 ...  \n",
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       "\n",
       "[360 rows x 78 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_input = pd.read_csv('cell_based_assay_traindata_binary_smiles.csv')\n",
    "df_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d8452119-f3cb-4b98-9977-ce2e059fdddd",
   "metadata": {},
   "outputs": [],
   "source": [
    "smiles_column = 'SMILES'\n",
    "smis = df_input.loc[:, smiles_column].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "581aaa46-2023-4993-85c3-3a5cb28947e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['C(CCC(=O)O)CC(=O)O', 'C1(C(=O)NC(=O)N1)NC(=O)N',\n",
       "        'C1=CC=C(C(=C1)C(=O)O)N', 'C1=CC=C(C=C1)CO', 'CCCCCCCC(=O)O'],\n",
       "       dtype=object),\n",
       " array([0, 0, 0, 0, 0]))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_columns= '1224842'\n",
    "ys = df_input.loc[:, target_columns].values\n",
    "smis[:5], ys[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f2d9c5e1-9acc-4147-a268-2a03f7c1fc93",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = [data.MoleculeDatapoint.from_smi(smi, y) for smi, y in zip(smis, ys)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c514129e-0e69-411d-991a-3b22cdb790af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CV_NO_VAL',\n",
       " 'CV',\n",
       " 'SCAFFOLD_BALANCED',\n",
       " 'RANDOM_WITH_REPEATED_SMILES',\n",
       " 'RANDOM',\n",
       " 'KENNARD_STONE',\n",
       " 'KMEANS']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# available split types\n",
    "list(data.SplitType.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e223c9b5-a60d-464e-b9f0-20b00ec3b352",
   "metadata": {},
   "outputs": [],
   "source": [
    "mols = [d.mol for d in all_data]  # RDkit Mol objects are use for structure based splits\n",
    "train_indices, val_indices, test_indices = data.make_split_indices(mols, \"random\", (0.8, 0.1, 0.1))\n",
    "train_data, val_data, test_data = data.split_data_by_indices(\n",
    "    all_data, train_indices, val_indices, test_indices\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f114fe35-1a49-489c-88ef-30b56b8da38f",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'MoleculeDatapoint' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[17], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m featurizer \u001b[38;5;241m=\u001b[39m featurizers\u001b[38;5;241m.\u001b[39mSimpleMoleculeMolGraphFeaturizer()\n\u001b[0;32m----> 3\u001b[0m train_dset \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mMoleculeDataset(train_data[\u001b[38;5;241m0\u001b[39m], featurizer)\n\u001b[1;32m      4\u001b[0m val_dset \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mMoleculeDataset(val_data[\u001b[38;5;241m0\u001b[39m], featurizer)\n\u001b[1;32m      5\u001b[0m test_dset \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mMoleculeDataset(test_data[\u001b[38;5;241m0\u001b[39m], featurizer)\n",
      "File \u001b[0;32m<string>:5\u001b[0m, in \u001b[0;36m__init__\u001b[0;34m(self, data, featurizer)\u001b[0m\n",
      "File \u001b[0;32m~/chemprop_download/chemprop/chemprop/data/datasets.py:174\u001b[0m, in \u001b[0;36mMoleculeDataset.__post_init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    172\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mData cannot be None!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 174\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreset()\n\u001b[1;32m    175\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m~/chemprop_download/chemprop/chemprop/data/datasets.py:314\u001b[0m, in \u001b[0;36mMoleculeDataset.reset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    311\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreset\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    312\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Reset the atom and bond features; atom and extra descriptors; and targets of each\u001b[39;00m\n\u001b[1;32m    313\u001b[0m \u001b[38;5;124;03m    datapoint to their initial, unnormalized values.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 314\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mreset()\n\u001b[1;32m    315\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__V_fs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_V_fs\n\u001b[1;32m    316\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__E_fs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_E_fs\n",
      "File \u001b[0;32m~/chemprop_download/chemprop/chemprop/data/datasets.py:137\u001b[0m, in \u001b[0;36m_MolGraphDatasetMixin.reset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreset\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    135\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Reset the atom and bond features; atom and extra descriptors; and targets of each\u001b[39;00m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;124;03m    datapoint to their initial, unnormalized values.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 137\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__Y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_Y\n\u001b[1;32m    138\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__X_d \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_X_d\n",
      "File \u001b[0;32m~/anaconda3/envs/chempropv2.1/lib/python3.11/functools.py:1001\u001b[0m, in \u001b[0;36mcached_property.__get__\u001b[0;34m(self, instance, owner)\u001b[0m\n\u001b[1;32m    999\u001b[0m val \u001b[38;5;241m=\u001b[39m cache\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattrname, _NOT_FOUND)\n\u001b[1;32m   1000\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m val \u001b[38;5;129;01mis\u001b[39;00m _NOT_FOUND:\n\u001b[0;32m-> 1001\u001b[0m     val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc(instance)\n\u001b[1;32m   1002\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1003\u001b[0m         cache[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattrname] \u001b[38;5;241m=\u001b[39m val\n",
      "File \u001b[0;32m~/chemprop_download/chemprop/chemprop/data/datasets.py:42\u001b[0m, in \u001b[0;36m_MolGraphDatasetMixin._Y\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;129m@cached_property\u001b[39m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_Y\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m     41\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"the raw targets of the dataset\"\"\"\u001b[39;00m\n\u001b[0;32m---> 42\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray([d\u001b[38;5;241m.\u001b[39my \u001b[38;5;28;01mfor\u001b[39;00m d \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata], \u001b[38;5;28mfloat\u001b[39m)\n",
      "\u001b[0;31mTypeError\u001b[0m: 'MoleculeDatapoint' object is not iterable"
     ]
    }
   ],
   "source": [
    "featurizer = featurizers.SimpleMoleculeMolGraphFeaturizer()\n",
    "\n",
    "train_dset = data.MoleculeDataset(train_data[0], featurizer)\n",
    "val_dset = data.MoleculeDataset(val_data[0], featurizer)\n",
    "test_dset = data.MoleculeDataset(test_data[0], featurizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "93ec3650-eec9-47d8-92c1-30f87518257a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      Unnamed: 0      AID          CID  Activity Outcome Protein Accession  \\\n",
       "0        2194047  1671197  155819421.0                 1          AAV41877   \n",
       "1        2194049  1671197   57372628.0                 0          AAV41877   \n",
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       "4        2194059  1671197    9916275.0                 1          AAV41877   \n",
       "...          ...      ...          ...               ...               ...   \n",
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       "8123     2213091  1671197    6917787.0                 0          AAV41877   \n",
       "8124     2213093  1671197    3354132.0                 0          AAV41877   \n",
       "\n",
       "                                                 SMILES  \n",
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       "1               C[N+](C)(CCOC1=CC=CC=C1)CC2=CC=CS2.[I-]  \n",
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       "3     C[C@@H](C1=NC=NC=C1F)C(CN2C=NC=N2)(C3=C(C=C(C=...  \n",
       "4     CCN(CC)C1=CC2=C(C=C1)C(=C3C=CC(=[N+](CC)CC)C=C...  \n",
       "...                                                 ...  \n",
       "8120  CCOC(=O)C1(CCN(CC1)CCC(C#N)(C2=CC=CC=C2)C3=CC=...  \n",
       "8121                       C(CC(=O)O)[C@@H](C(=O)O)N.Cl  \n",
       "8122  C1=CC=C(C(=C1)C2C(O2)(CN3C=NC=N3)C4=CC=C(C=C4)...  \n",
       "8123         CC1=CC=CC=C1N2CCN(CC2)CCC3=NN=C4N3CCCC4.Cl  \n",
       "8124                       CC1=C(C(=O)C=CO1)OC(=O)C(C)C  \n",
       "\n",
       "[8125 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_input = pd.read_csv('single_assay_train_binary.csv')\n",
    "df_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c8938242-f282-42f8-924b-af382a3dbc9f",
   "metadata": {},
   "outputs": [
    {
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       "      Activity Outcome                                             SMILES\n",
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       "2                    0  CC(C)C1=C(C=C2C(=C1)CC[C@@H]3[C@@]2(CCC[C@@]3(...\n",
       "3                    1  C[C@@H](C1=NC=NC=C1F)C(CN2C=NC=N2)(C3=C(C=C(C=...\n",
       "4                    1  CCN(CC)C1=CC2=C(C=C1)C(=C3C=CC(=[N+](CC)CC)C=C...\n",
       "...                ...                                                ...\n",
       "8120                 1  CCOC(=O)C1(CCN(CC1)CCC(C#N)(C2=CC=CC=C2)C3=CC=...\n",
       "8121                 1                       C(CC(=O)O)[C@@H](C(=O)O)N.Cl\n",
       "8122                 1  C1=CC=C(C(=C1)C2C(O2)(CN3C=NC=N3)C4=CC=C(C=C4)...\n",
       "8123                 0         CC1=CC=CC=C1N2CCN(CC2)CCC3=NN=C4N3CCCC4.Cl\n",
       "8124                 0                       CC1=C(C(=O)C=CO1)OC(=O)C(C)C\n",
       "\n",
       "[8125 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['Activity Outcome', 'SMILES']\n",
    "df_input = df_input[columns]\n",
    "df_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "971b17f7-6e4d-4920-ab1d-1aa00296325e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_input.to_csv('single_assay_train_test.csv', header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a75b5c47-8c3b-4926-b781-47ba7acfa01c",
   "metadata": {},
   "outputs": [],
   "source": [
    "smiles_column = 'SMILES'\n",
    "smis = df_input.loc[:, smiles_column].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9cb1cd7e-e695-4f75-8574-1e9a5b1cf9ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['C[C@H]1/C=C\\\\C=C(/C(=O)NC2=CC(=C3C(=C2O)C(=C(C4=C3C(=O)[C@](O4)(OC=C[C@@H]([C@H]([C@H]([C@@H]([C@@H]([C@@H]([C@H]1O)C)O)C)OC(=O)C)C)OC)C)C)O)[O-])\\\\C.[Na+]',\n",
       "        'C[N+](C)(CCOC1=CC=CC=C1)CC2=CC=CS2.[I-]',\n",
       "        'CC(C)C1=C(C=C2C(=C1)CC[C@@H]3[C@@]2(CCC[C@@]3(C)C(=O)[O-])C)S(=O)(=O)O.[Na+]',\n",
       "        'C[C@@H](C1=NC=NC=C1F)C(CN2C=NC=N2)(C3=C(C=C(C=C3)F)F)O',\n",
       "        'CCN(CC)C1=CC2=C(C=C1)C(=C3C=CC(=[N+](CC)CC)C=C3O2)C4=C(C=C(C=C4)S(=O)(=O)[O-])S(=O)(=O)[O-].[Na+]'],\n",
       "       dtype=object),\n",
       " array([1, 0, 0, 1, 1]))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_columns= 'Activity Outcome'\n",
    "ys = df_input.loc[:, target_columns].values\n",
    "smis[:5], ys[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bd7f4710-1493-475c-9fbd-2e113781942d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[22:58:05] WARNING: not removing hydrogen atom without neighbors\n"
     ]
    }
   ],
   "source": [
    "all_data = [data.MoleculeDatapoint.from_smi(smi, y) for smi, y in zip(smis, ys)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c8cbc86b-8997-48a7-b45e-13b2829280f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CV_NO_VAL',\n",
       " 'CV',\n",
       " 'SCAFFOLD_BALANCED',\n",
       " 'RANDOM_WITH_REPEATED_SMILES',\n",
       " 'RANDOM',\n",
       " 'KENNARD_STONE',\n",
       " 'KMEANS']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data.SplitType.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c32cee57-5da5-4fa8-bd57-c44db952a776",
   "metadata": {},
   "outputs": [],
   "source": [
    "mols = [d.mol for d in all_data]  # RDkit Mol objects are use for structure based splits\n",
    "train_indices, val_indices, test_indices = data.make_split_indices(mols, \"random\", (0.8, 0.1, 0.1))\n",
    "train_data, val_data, test_data = data.split_data_by_indices(\n",
    "    all_data, train_indices, val_indices, test_indices\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d1afa157-54b3-4c4e-ad88-52a67d76ffa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "k_splits = KFold(n_splits=5)\n",
    "k_train_indices, k_val_indices, k_test_indices = [], [], []\n",
    "for fold in k_splits.split(mols):\n",
    "    k_train_indices.append(fold[0])\n",
    "    k_val_indices.append([])\n",
    "    k_test_indices.append(fold[1])\n",
    "k_train_data, _, k_test_data = data.split_data_by_indices(\n",
    "    all_data, k_train_indices, None, k_test_indices\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f707d398-a31b-4a5e-9a4a-7e995c1a4abc",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'MoleculeDatapoint' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[29], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m featurizer \u001b[38;5;241m=\u001b[39m featurizers\u001b[38;5;241m.\u001b[39mSimpleMoleculeMolGraphFeaturizer()\n\u001b[0;32m----> 3\u001b[0m train_dset \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mMoleculeDataset(train_data[\u001b[38;5;241m0\u001b[39m], featurizer)\n\u001b[1;32m      4\u001b[0m scaler \u001b[38;5;241m=\u001b[39m train_dset\u001b[38;5;241m.\u001b[39mnormalize_targets()\n\u001b[1;32m      6\u001b[0m val_dset \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mMoleculeDataset(val_data[\u001b[38;5;241m0\u001b[39m], featurizer)\n",
      "File \u001b[0;32m<string>:5\u001b[0m, in \u001b[0;36m__init__\u001b[0;34m(self, data, featurizer)\u001b[0m\n",
      "File \u001b[0;32m~/chemprop_download/chemprop/chemprop/data/datasets.py:174\u001b[0m, in \u001b[0;36mMoleculeDataset.__post_init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    172\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mData cannot be None!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 174\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreset()\n\u001b[1;32m    175\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m~/chemprop_download/chemprop/chemprop/data/datasets.py:314\u001b[0m, in \u001b[0;36mMoleculeDataset.reset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    311\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreset\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    312\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Reset the atom and bond features; atom and extra descriptors; and targets of each\u001b[39;00m\n\u001b[1;32m    313\u001b[0m \u001b[38;5;124;03m    datapoint to their initial, unnormalized values.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 314\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mreset()\n\u001b[1;32m    315\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__V_fs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_V_fs\n\u001b[1;32m    316\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__E_fs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_E_fs\n",
      "File \u001b[0;32m~/chemprop_download/chemprop/chemprop/data/datasets.py:137\u001b[0m, in \u001b[0;36m_MolGraphDatasetMixin.reset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreset\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    135\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Reset the atom and bond features; atom and extra descriptors; and targets of each\u001b[39;00m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;124;03m    datapoint to their initial, unnormalized values.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 137\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__Y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_Y\n\u001b[1;32m    138\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__X_d \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_X_d\n",
      "File \u001b[0;32m~/anaconda3/envs/chempropv2.1/lib/python3.11/functools.py:1001\u001b[0m, in \u001b[0;36mcached_property.__get__\u001b[0;34m(self, instance, owner)\u001b[0m\n\u001b[1;32m    999\u001b[0m val \u001b[38;5;241m=\u001b[39m cache\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattrname, _NOT_FOUND)\n\u001b[1;32m   1000\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m val \u001b[38;5;129;01mis\u001b[39;00m _NOT_FOUND:\n\u001b[0;32m-> 1001\u001b[0m     val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc(instance)\n\u001b[1;32m   1002\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1003\u001b[0m         cache[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattrname] \u001b[38;5;241m=\u001b[39m val\n",
      "File \u001b[0;32m~/chemprop_download/chemprop/chemprop/data/datasets.py:42\u001b[0m, in \u001b[0;36m_MolGraphDatasetMixin._Y\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;129m@cached_property\u001b[39m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_Y\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m     41\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"the raw targets of the dataset\"\"\"\u001b[39;00m\n\u001b[0;32m---> 42\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray([d\u001b[38;5;241m.\u001b[39my \u001b[38;5;28;01mfor\u001b[39;00m d \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata], \u001b[38;5;28mfloat\u001b[39m)\n",
      "\u001b[0;31mTypeError\u001b[0m: 'MoleculeDatapoint' object is not iterable"
     ]
    }
   ],
   "source": [
    "featurizer = featurizers.SimpleMoleculeMolGraphFeaturizer()\n",
    "\n",
    "train_dset = data.MoleculeDataset(train_data[0], featurizer)\n",
    "scaler = train_dset.normalize_targets()\n",
    "\n",
    "val_dset = data.MoleculeDataset(val_data[0], featurizer)\n",
    "val_dset.normalize_targets(scaler)\n",
    "\n",
    "test_dset = data.MoleculeDataset(test_data[0], featurizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d6602593-4073-4d97-99b1-8243ce5f7e2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                   Version\n",
      "------------------------- -----------\n",
      "aimsim_core               2.2.2\n",
      "aiohappyeyeballs          2.4.4\n",
      "aiohttp                   3.11.10\n",
      "aiosignal                 1.3.1\n",
      "anyio                     4.6.2\n",
      "argon2-cffi               21.3.0\n",
      "argon2-cffi-bindings      21.2.0\n",
      "astartes                  1.3.0\n",
      "asttokens                 2.0.5\n",
      "async-lru                 2.0.4\n",
      "attrs                     24.2.0\n",
      "Babel                     2.11.0\n",
      "beautifulsoup4            4.12.3\n",
      "bleach                    6.2.0\n",
      "Brotli                    1.0.9\n",
      "certifi                   2024.8.30\n",
      "cffi                      1.17.1\n",
      "charset-normalizer        3.3.2\n",
      "chemprop                  2.1.0\n",
      "comm                      0.2.1\n",
      "ConfigArgParse            1.7\n",
      "debugpy                   1.6.7\n",
      "decorator                 5.1.1\n",
      "defusedxml                0.7.1\n",
      "descriptastorus           2.8.0\n",
      "dill                      0.3.9\n",
      "executing                 0.8.3\n",
      "fastjsonschema            2.20.0\n",
      "filelock                  3.16.1\n",
      "frozenlist                1.5.0\n",
      "fsspec                    2024.10.0\n",
      "h11                       0.14.0\n",
      "httpcore                  1.0.2\n",
      "httpx                     0.27.0\n",
      "idna                      3.7\n",
      "ipykernel                 6.29.5\n",
      "ipython                   8.27.0\n",
      "ipywidgets                8.1.5\n",
      "jedi                      0.19.1\n",
      "Jinja2                    3.1.4\n",
      "joblib                    1.4.2\n",
      "json5                     0.9.25\n",
      "jsonschema                4.23.0\n",
      "jsonschema-specifications 2023.7.1\n",
      "jupyter                   1.0.0\n",
      "jupyter_client            8.6.0\n",
      "jupyter-console           6.6.3\n",
      "jupyter_core              5.7.2\n",
      "jupyter-events            0.10.0\n",
      "jupyter-lsp               2.2.0\n",
      "jupyter_server            2.14.1\n",
      "jupyter_server_terminals  0.4.4\n",
      "jupyterlab                4.2.5\n",
      "jupyterlab-pygments       0.1.2\n",
      "jupyterlab_server         2.27.3\n",
      "jupyterlab_widgets        3.0.13\n",
      "lightning                 2.4.0\n",
      "lightning-utilities       0.11.9\n",
      "markdown-it-py            3.0.0\n",
      "MarkupSafe                2.1.3\n",
      "matplotlib-inline         0.1.6\n",
      "mdurl                     0.1.2\n",
      "mhfp                      1.9.6\n",
      "mistune                   2.0.4\n",
      "mordredcommunity          2.0.6\n",
      "mpmath                    1.3.0\n",
      "multidict                 6.1.0\n",
      "multiprocess              0.70.17\n",
      "nbclient                  0.8.0\n",
      "nbconvert                 7.16.4\n",
      "nbformat                  5.10.4\n",
      "nest-asyncio              1.6.0\n",
      "networkx                  3.4.2\n",
      "notebook                  7.2.2\n",
      "notebook_shim             0.2.3\n",
      "numpy                     1.26.4\n",
      "nvidia-cublas-cu12        12.4.5.8\n",
      "nvidia-cuda-cupti-cu12    12.4.127\n",
      "nvidia-cuda-nvrtc-cu12    12.4.127\n",
      "nvidia-cuda-runtime-cu12  12.4.127\n",
      "nvidia-cudnn-cu12         9.1.0.70\n",
      "nvidia-cufft-cu12         11.2.1.3\n",
      "nvidia-curand-cu12        10.3.5.147\n",
      "nvidia-cusolver-cu12      11.6.1.9\n",
      "nvidia-cusparse-cu12      12.3.1.170\n",
      "nvidia-nccl-cu12          2.21.5\n",
      "nvidia-nvjitlink-cu12     12.4.127\n",
      "nvidia-nvtx-cu12          12.4.127\n",
      "overrides                 7.4.0\n",
      "packaging                 24.2\n",
      "padelpy                   0.1.16\n",
      "pandas                    2.2.3\n",
      "pandas-flavor             0.6.0\n",
      "pandocfilters             1.5.0\n",
      "parso                     0.8.3\n",
      "pexpect                   4.8.0\n",
      "pillow                    11.0.0\n",
      "pip                       24.2\n",
      "platformdirs              3.10.0\n",
      "ply                       3.11\n",
      "prometheus_client         0.21.0\n",
      "prompt-toolkit            3.0.43\n",
      "propcache                 0.2.1\n",
      "psutil                    6.1.0\n",
      "ptyprocess                0.7.0\n",
      "pure-eval                 0.2.2\n",
      "pycparser                 2.21\n",
      "Pygments                  2.15.1\n",
      "PyQt5                     5.15.10\n",
      "PyQt5-sip                 12.13.0\n",
      "PySocks                   1.7.1\n",
      "python-dateutil           2.9.0.post0\n",
      "python-json-logger        2.0.7\n",
      "pytorch-lightning         2.4.0\n",
      "pytz                      2024.2\n",
      "PyYAML                    6.0.2\n",
      "pyzmq                     25.1.2\n",
      "qtconsole                 5.6.0\n",
      "QtPy                      2.4.1\n",
      "rdkit                     2024.3.6\n",
      "referencing               0.30.2\n",
      "requests                  2.32.3\n",
      "rfc3339-validator         0.1.4\n",
      "rfc3986-validator         0.1.1\n",
      "rich                      13.9.4\n",
      "rpds-py                   0.10.6\n",
      "scikit-learn              1.6.0\n",
      "scipy                     1.14.1\n",
      "Send2Trash                1.8.2\n",
      "setuptools                75.1.0\n",
      "sip                       6.7.12\n",
      "six                       1.16.0\n",
      "sniffio                   1.3.0\n",
      "soupsieve                 2.5\n",
      "stack-data                0.2.0\n",
      "sympy                     1.13.1\n",
      "tabulate                  0.9.0\n",
      "terminado                 0.17.1\n",
      "threadpoolctl             3.5.0\n",
      "tinycss2                  1.2.1\n",
      "torch                     2.5.1\n",
      "torchmetrics              1.6.0\n",
      "tornado                   6.4.1\n",
      "tqdm                      4.67.1\n",
      "traitlets                 5.14.3\n",
      "triton                    3.1.0\n",
      "typing_extensions         4.12.2\n",
      "tzdata                    2024.2\n",
      "urllib3                   2.2.3\n",
      "wcwidth                   0.2.5\n",
      "webencodings              0.5.1\n",
      "websocket-client          1.8.0\n",
      "wheel                     0.44.0\n",
      "widgetsnbextension        4.0.13\n",
      "xarray                    2024.11.0\n",
      "yarl                      1.18.3\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd5077c5-6fe1-4868-b20d-f9ccda6fd894",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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