Evaluate fastsolv on Polaris solubility datasets

evaluate_fastsolv.ipynb

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   "outputs": [],
   "source": [
    "import polaris as po\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from  rdkit import Chem\n",
    "from rdkit.Chem.Descriptors import MolWt\n",
    "import seaborn as sns\n",
    "from fastsolv import fastsolv\n",
    "from sklearn.metrics import r2_score, mean_absolute_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "992dd5e9-9831-47e7-8791-ae76b07ea88c",
   "metadata": {},
   "source": [
    "### 1. Download Datasets from Polaris"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96176671-a3fb-4907-b259-fae8243fb899",
   "metadata": {},
   "source": [
    "Get the Antiviral ADMET dataset from Polaris "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b2f94e11-1a05-4145-b58e-7afc8bbcb929",
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[2025-08-19 19:57:29] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> The version of Polaris that was used to create the artifact          <a href=\"file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/_artifact.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_artifact.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/_artifact.py#92\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">92</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                      </span>         <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.11</span>.<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8.</span>dev4+g40e3b2b.d20250207<span style=\"font-weight: bold\">)</span> is different from the currently     <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                      </span>         installed version of Polaris <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.11</span>.<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span><span style=\"font-weight: bold\">)</span>.                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
       "</pre>\n"
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       "\u001b[2;36m[2025-08-19 19:57:29]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m The version of Polaris that was used to create the artifact          \u001b]8;id=558010;file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/_artifact.py\u001b\\\u001b[2m_artifact.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=173126;file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/_artifact.py#92\u001b\\\u001b[2m92\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m                      \u001b[0m         \u001b[1m(\u001b[0m\u001b[1;36m0.11\u001b[0m.\u001b[1;36m8.\u001b[0mdev4+g40e3b2b.d20250207\u001b[1m)\u001b[0m is different from the currently     \u001b[2m               \u001b[0m\n",
       "\u001b[2;36m                      \u001b[0m         installed version of Polaris \u001b[1m(\u001b[0m\u001b[1;36m0.11\u001b[0m.\u001b[1;36m10\u001b[0m\u001b[1m)\u001b[0m.                              \u001b[2m               \u001b[0m\n"
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                      </span><span style=\"color: #808000; text-decoration-color: #808000\">WARNING </span> You're loading data from a remote location. If the dataset is small     <a href=\"file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/dataset/_base.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_base.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/dataset/_base.py#181\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">181</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                      </span>         enough, consider caching the dataset first using <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">DatasetV2.cache</span><span style=\"font-weight: bold\">()</span> for  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                      </span>         more performant data access.                                            <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "</pre>\n"
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      "text/plain": [
       "\u001b[2;36m                     \u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m You're loading data from a remote location. If the dataset is small     \u001b]8;id=898257;file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/dataset/_base.py\u001b\\\u001b[2m_base.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=836900;file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/dataset/_base.py#181\u001b\\\u001b[2m181\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m                      \u001b[0m         enough, consider caching the dataset first using \u001b[1;35mDatasetV2.cache\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for  \u001b[2m            \u001b[0m\n",
       "\u001b[2;36m                      \u001b[0m         more performant data access.                                            \u001b[2m            \u001b[0m\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[19:57:30] </span><span style=\"color: #008000; text-decoration-color: #008000\"> Success: Fetching dataset</span>                                                                 <a href=\"file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/utils/context.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">context.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/utils/context.py#53\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">53</span></a>\n",
       "</pre>\n"
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       "\u001b[2;36m[19:57:30]\u001b[0m\u001b[2;36m \u001b[0m\u001b[32m Success: Fetching dataset\u001b[0m                                                                 \u001b]8;id=75727;file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/utils/context.py\u001b\\\u001b[2mcontext.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=729285;file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/utils/context.py#53\u001b\\\u001b[2m53\u001b[0m\u001b]8;;\u001b\\\n"
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   "source": [
    "antiviral_admet_ds = po.load_dataset(\"asap-discovery/antiviral-admet-2025-unblinded\")\n",
    "antiviral_admet_ds.load_to_memory()\n",
    "antiviral_admet_df = pd.DataFrame(antiviral_admet_ds.zarr_data).dropna(subset=\"KSOL\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b329b7e3-311a-4275-857d-d813d8c2fdfe",
   "metadata": {},
   "source": [
    "Get the Biogen dataset from Polaris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "154c97a4-9368-44ca-b9cb-b53f8b6d0d1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[2025-08-19 20:00:22] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> The version of Polaris that was used to create the artifact <span style=\"font-weight: bold\">(</span>dev<span style=\"font-weight: bold\">)</span> is <a href=\"file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/_artifact.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_artifact.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/_artifact.py#92\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">92</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                      </span>         different from the currently installed version of Polaris <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.11</span>.<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span><span style=\"font-weight: bold\">)</span>. <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[2025-08-19 20:00:22]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m The version of Polaris that was used to create the artifact \u001b[1m(\u001b[0mdev\u001b[1m)\u001b[0m is \u001b]8;id=210923;file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/_artifact.py\u001b\\\u001b[2m_artifact.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=840339;file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/_artifact.py#92\u001b\\\u001b[2m92\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m                      \u001b[0m         different from the currently installed version of Polaris \u001b[1m(\u001b[0m\u001b[1;36m0.11\u001b[0m.\u001b[1;36m10\u001b[0m\u001b[1m)\u001b[0m. \u001b[2m               \u001b[0m\n"
      ]
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[2025-08-19 20:00:23] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> To verify the checksum, we need to recompute it. This can be slow    <a href=\"file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/mixins/_checksum.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_checksum.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/mixins/_checksum.py#67\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">67</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                      </span>         for large datasets.                                                  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
       "</pre>\n"
      ],
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       "\u001b[2;36m[2025-08-19 20:00:23]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m To verify the checksum, we need to recompute it. This can be slow    \u001b]8;id=601500;file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/mixins/_checksum.py\u001b\\\u001b[2m_checksum.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=448182;file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/mixins/_checksum.py#67\u001b\\\u001b[2m67\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m                      \u001b[0m         for large datasets.                                                  \u001b[2m               \u001b[0m\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:00:23] </span><span style=\"color: #008000; text-decoration-color: #008000\"> Success: Fetching dataset</span>                                                                 <a href=\"file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/utils/context.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">context.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/utils/context.py#53\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">53</span></a>\n",
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       "\u001b[2;36m[20:00:23]\u001b[0m\u001b[2;36m \u001b[0m\u001b[32m Success: Fetching dataset\u001b[0m                                                                 \u001b]8;id=835257;file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/utils/context.py\u001b\\\u001b[2mcontext.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=939853;file:///opt/homebrew/Caskroom/miniforge/base/envs/rdkit_2025_03/lib/python3.11/site-packages/polaris/utils/context.py#53\u001b\\\u001b[2m53\u001b[0m\u001b]8;;\u001b\\\n"
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   ],
   "source": [
    "biogen_ds = po.load_dataset(\"biogen/adme-fang-v1\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b17d4920-7b36-499b-9907-9e01c09c5e3e",
   "metadata": {},
   "source": [
    "### 2. Convert Units to LogS "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a378d4f-49b6-44a2-ad32-ec416ddd8934",
   "metadata": {},
   "source": [
    "The Antiviral ADMET dataset has kinetic solubility in $\\mu$M, we need to convert this to the log of molar solubility (LogS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28821f86-c531-45c1-b02f-dc31685533e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "antiviral_admet_df['LogS'] = np.log10(antiviral_admet_df.KSOL * 1e-6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9956122-8bb6-473a-b7c8-fd41e4b56257",
   "metadata": {},
   "source": [
    "The Biogen dataset has solubility in log10($\\mu$g/ml), we need to convert this to the log of molar solubility (LogS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a1a9e46c-1b6f-4267-816d-b3f3d423432e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ug_ml_to_logS(ug_ml, molar_mass):\n",
    "    g_per_liter = ug_ml / 1000.0\n",
    "    molar_solubility_S = g_per_liter / molar_mass\n",
    "    log_S = np.log10(molar_solubility_S)\n",
    "    return log_S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "cb912ff2-7b73-4b7a-8290-b97dc68881fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "biogen_df = biogen_ds.table\n",
    "biogen_df.dropna(subset=\"LOG_SOLUBILITY\",inplace=True)\n",
    "biogen_df['mol'] = biogen_df.SMILES.apply(Chem.MolFromSmiles)\n",
    "biogen_df['ug_ml'] = 10**biogen_df.LOG_SOLUBILITY\n",
    "biogen_df['MW'] = biogen_df.mol.apply(MolWt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "aa53a624-4f92-468e-a9f2-9d1c6fe4f190",
   "metadata": {},
   "outputs": [],
   "source": [
    "biogen_df['LogS'] = [ug_ml_to_logS(x,y) for x,y in biogen_df[[\"ug_ml\",\"MW\"]].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "0c676e45-38ff-4a37-8ca3-ab17a8971c02",
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "antiviral_admet_df['Dataset'] = \"Antiviral\"\n",
    "biogen_df['Dataset'] = \"Biogen\"\n",
    "cols = ['Dataset','LogS']\n",
    "sns.violinplot(x='Dataset',y='LogS',data=pd.concat([antiviral_admet_df[cols],biogen_df[cols]]));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7557c134-32c8-4b74-9362-192dc2e8ee71",
   "metadata": {},
   "source": [
    "### 3. Evaluate fastsolv on the Antiviral ADMET dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0472a923-7da2-4142-8a3c-b4fe9ecae4e8",
   "metadata": {},
   "source": [
    "A simple function at add the necessary columns for **fastsolv**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "7ba8d8ee-35fe-464f-9921-25035b55f96f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_dataframe(df,smiles_col):\n",
    "    df['solute_smiles'] = df[smiles_col]\n",
    "    df['solvent_smiles'] = \"O\"\n",
    "    df['temperature'] = 298"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "221b41b5-fb99-4dcf-9e8d-6a5cbe33d056",
   "metadata": {},
   "source": [
    "Run fastsolv on the Antiviral ADMET dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "7345cf4e-61de-4162-96e7-a37461245b3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████| 447/447 [00:02<00:00, 151.19it/s]\n",
      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
      "GPU available: True (mps), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb7bf1f4de9742799da654d4057648b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6f91d93f3632470dad3e8763eb491018",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52a94856f23a40dcac90e3c90445b202",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a42f51a709784622899139b81b29f9dd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prepare_dataframe(antiviral_admet_df,\"CXSMILES\")\n",
    "antiviral_admet_pred = fastsolv(antiviral_admet_df)\n",
    "antiviral_admet_df['predicted_LogS'] = antiviral_admet_pred.predicted_logS.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "da7e79b1-59a2-44a4-9cc6-e331967611c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(-5.5, -0.75, 'MAE = 1.47')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.regplot(x=\"LogS\",y=\"predicted_LogS\",data=antiviral_admet_df,scatter_kws={\"s\": 5, \"alpha\" : 0.35})\n",
    "r2 = r2_score(antiviral_admet_df.LogS,antiviral_admet_df.predicted_LogS)\n",
    "mae = mean_absolute_error(antiviral_admet_df.LogS,antiviral_admet_df.predicted_LogS)\n",
    "ax.text(-5.5, -0.5, f\"$R^2$ = {r2:.2f}\")\n",
    "ax.text(-5.5, -0.75, f\"MAE = {mae:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c361a0ba-f0e0-457d-9c30-44381ed990fb",
   "metadata": {},
   "source": [
    "### 4. Evaluate fastsolv on the Biogen dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "560a280d-c431-4b20-a88f-8d953befeda9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████| 2174/2174 [00:09<00:00, 223.02it/s]\n",
      "💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
      "GPU available: True (mps), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5712accf088443f8bdc43cfeee02c61c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "457f9c1d035241979955eb2c0b442a27",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9039d562884c41359e8405fea76a44f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f951b18dc8a41fd953b3dbf3df52742",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |                                                                                                 …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prepare_dataframe(biogen_df,\"SMILES\")\n",
    "biogen_pred = fastsolv(biogen_df)\n",
    "biogen_df['predicted_LogS'] = biogen_pred.predicted_logS.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "a9ae2248-6510-46e3-a62e-e798ecb5bc90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.regplot(x=\"LogS\",y=\"predicted_LogS\",data=biogen_df,scatter_kws={\"s\": 5, \"alpha\" : 0.35})\n",
    "r2 = r2_score(biogen_df.LogS,biogen_df.predicted_LogS)\n",
    "mae = mean_absolute_error(biogen_df.LogS,biogen_df.predicted_LogS)\n",
    "ax.text(-6.5, 0.0, f\"$R^2$ = {r2:.2f}\")\n",
    "ax.text(-6.5, -0.25, f\"MAE = {mae:.2f}\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "a560643f-8a8f-4db8-ad9f-22a078c3030e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UNIQUE_ID</th>\n",
       "      <th>MOL_smiles</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>LOG_HLM_CLint</th>\n",
       "      <th>LOG_RLM_CLint</th>\n",
       "      <th>LOG_MDR1-MDCK_ER</th>\n",
       "      <th>LOG_HPPB</th>\n",
       "      <th>LOG_RPPB</th>\n",
       "      <th>LOG_SOLUBILITY</th>\n",
       "      <th>mol</th>\n",
       "      <th>MW</th>\n",
       "      <th>sol_ug_ml</th>\n",
       "      <th>ug_ml</th>\n",
       "      <th>LogS</th>\n",
       "      <th>solute_smiles</th>\n",
       "      <th>solvent_smiles</th>\n",
       "      <th>temperature</th>\n",
       "      <th>predicted_LogS</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b5e1231aef0080c76b4207ca1355485432877339</td>\n",
       "      <td>Brc1cc2c(cc1Cn1cncn1)OCCCO2</td>\n",
       "      <td>Brc1cc2c(cc1Cn1cncn1)OCCCO2</td>\n",
       "      <td>0.886265</td>\n",
       "      <td>2.357933</td>\n",
       "      <td>-0.247518</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.536432</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x150117f40&gt;</td>\n",
       "      <td>310.151</td>\n",
       "      <td>34.390</td>\n",
       "      <td>34.390</td>\n",
       "      <td>-3.955141</td>\n",
       "      <td>Brc1cc2c(cc1Cn1cncn1)OCCCO2</td>\n",
       "      <td>O</td>\n",
       "      <td>298</td>\n",
       "      <td>-1.341322</td>\n",
       "      <td>Biogen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0604cf74e44450d5963d169e18854d96c4e5dba8</td>\n",
       "      <td>Brc1ccc(-c2nnc(Cn3cnc4ccccc43)o2)o1</td>\n",
       "      <td>Brc1ccc(-c2nnc(Cn3cnc4ccccc43)o2)o1</td>\n",
       "      <td>0.675687</td>\n",
       "      <td>1.613704</td>\n",
       "      <td>-0.010669</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.797475</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x150187f40&gt;</td>\n",
       "      <td>345.156</td>\n",
       "      <td>62.730</td>\n",
       "      <td>62.730</td>\n",
       "      <td>-3.740540</td>\n",
       "      <td>Brc1ccc(-c2nnc(Cn3cnc4ccccc43)o2)o1</td>\n",
       "      <td>O</td>\n",
       "      <td>298</td>\n",
       "      <td>-2.414264</td>\n",
       "      <td>Biogen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9be43035e7b8a5078babf4345584f4dc6dcd1448</td>\n",
       "      <td>Brc1cnc2ccccc2c1</td>\n",
       "      <td>Brc1cnc2ccccc2c1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.033858</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x150187ed0&gt;</td>\n",
       "      <td>208.058</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.925</td>\n",
       "      <td>-5.352043</td>\n",
       "      <td>Brc1cnc2ccccc2c1</td>\n",
       "      <td>O</td>\n",
       "      <td>298</td>\n",
       "      <td>-1.294908</td>\n",
       "      <td>Biogen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70ac5fbe7181b1fce36dbaedaedcd3cdeb4fdf82</td>\n",
       "      <td>C#CCN1CCC(c2nc(Cc3noc(C)n3)no2)CC1</td>\n",
       "      <td>C#CCN1CCC(c2nc(Cc3noc(C)n3)no2)CC1</td>\n",
       "      <td>0.675687</td>\n",
       "      <td>1.107108</td>\n",
       "      <td>-0.135635</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.695044</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x150187e60&gt;</td>\n",
       "      <td>287.323</td>\n",
       "      <td>49.550</td>\n",
       "      <td>49.550</td>\n",
       "      <td>-3.763327</td>\n",
       "      <td>C#CCN1CCC(c2nc(Cc3noc(C)n3)no2)CC1</td>\n",
       "      <td>O</td>\n",
       "      <td>298</td>\n",
       "      <td>-1.561687</td>\n",
       "      <td>Biogen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>e9cafc1522a01017509e0fda082d84676cdc1f5e</td>\n",
       "      <td>C#CCN1CCC[C@H]1C(=O)Nc1cnn(-c2ncccn2)c1</td>\n",
       "      <td>C#CCN1CCC[C@H]1C(=O)Nc1cnn(-c2ncccn2)c1</td>\n",
       "      <td>0.675687</td>\n",
       "      <td>1.921166</td>\n",
       "      <td>0.596581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.653984</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x150187df0&gt;</td>\n",
       "      <td>296.334</td>\n",
       "      <td>45.080</td>\n",
       "      <td>45.080</td>\n",
       "      <td>-3.817798</td>\n",
       "      <td>C#CCN1CCC[C@H]1C(=O)Nc1cnn(-c2ncccn2)c1</td>\n",
       "      <td>O</td>\n",
       "      <td>298</td>\n",
       "      <td>-1.749381</td>\n",
       "      <td>Biogen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3511</th>\n",
       "      <td>6b39954041ed15e815bc15457e06440183006a00</td>\n",
       "      <td>c1cncc(-c2ccnn2CN2CCOCC2)c1</td>\n",
       "      <td>c1cncc(-c2ccnn2CN2CCOCC2)c1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.239049</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x150131e70&gt;</td>\n",
       "      <td>244.298</td>\n",
       "      <td>17.340</td>\n",
       "      <td>17.340</td>\n",
       "      <td>-4.148871</td>\n",
       "      <td>c1cncc(-c2ccnn2CN2CCOCC2)c1</td>\n",
       "      <td>O</td>\n",
       "      <td>298</td>\n",
       "      <td>-1.242087</td>\n",
       "      <td>Biogen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3513</th>\n",
       "      <td>d08dfa20c576ef45061fd2af3a1c7acc286bc6d2</td>\n",
       "      <td>c1cnn(-c2ccc(-c3ccc(NC4CC4)nn3)cc2)c1</td>\n",
       "      <td>c1cnn(-c2ccc(-c3ccc(NC4CC4)nn3)cc2)c1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.107905</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x150131ee0&gt;</td>\n",
       "      <td>277.331</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.780</td>\n",
       "      <td>-5.550904</td>\n",
       "      <td>c1cnn(-c2ccc(-c3ccc(NC4CC4)nn3)cc2)c1</td>\n",
       "      <td>O</td>\n",
       "      <td>298</td>\n",
       "      <td>-1.602266</td>\n",
       "      <td>Biogen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3514</th>\n",
       "      <td>c9bf3aca6d10ff3538ce617d83b9de8c80723024</td>\n",
       "      <td>c1csc(-c2csc3nc(CN4CCOCC4)nc(N4CCc5[nH]ncc5C4)...</td>\n",
       "      <td>c1csc(-c2csc3nc(CN4CCOCC4)nc(N4CCc5[nH]ncc5C4)...</td>\n",
       "      <td>2.747507</td>\n",
       "      <td>3.118970</td>\n",
       "      <td>1.921635</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.690107</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x150131f50&gt;</td>\n",
       "      <td>438.582</td>\n",
       "      <td>48.990</td>\n",
       "      <td>48.990</td>\n",
       "      <td>-3.951943</td>\n",
       "      <td>c1csc(-c2csc3nc(CN4CCOCC4)nc(N4CCc5[nH]ncc5C4)...</td>\n",
       "      <td>O</td>\n",
       "      <td>298</td>\n",
       "      <td>-2.662541</td>\n",
       "      <td>Biogen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3517</th>\n",
       "      <td>644313d1941ab4550760b7c1162c54d209a7435f</td>\n",
       "      <td>c1nc(NC2CCN(C3CC3)CC2)c2sccc2n1</td>\n",
       "      <td>c1nc(NC2CCN(C3CC3)CC2)c2sccc2n1</td>\n",
       "      <td>1.240899</td>\n",
       "      <td>1.906157</td>\n",
       "      <td>0.094514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.749891</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x150131fc0&gt;</td>\n",
       "      <td>274.393</td>\n",
       "      <td>56.220</td>\n",
       "      <td>56.220</td>\n",
       "      <td>-3.688482</td>\n",
       "      <td>c1nc(NC2CCN(C3CC3)CC2)c2sccc2n1</td>\n",
       "      <td>O</td>\n",
       "      <td>298</td>\n",
       "      <td>-1.360543</td>\n",
       "      <td>Biogen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3518</th>\n",
       "      <td>32658afab9952faf20249dbb305e1ac85ef10a1b</td>\n",
       "      <td>c1nc(Nc2ccc3c(c2)OCCCO3)c2sccc2n1</td>\n",
       "      <td>c1nc(Nc2ccc3c(c2)OCCCO3)c2sccc2n1</td>\n",
       "      <td>1.964066</td>\n",
       "      <td>3.196559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.707229</td>\n",
       "      <td>&lt;rdkit.Chem.rdchem.Mol object at 0x150132030&gt;</td>\n",
       "      <td>299.355</td>\n",
       "      <td>50.960</td>\n",
       "      <td>50.960</td>\n",
       "      <td>-3.768957</td>\n",
       "      <td>c1nc(Nc2ccc3c(c2)OCCCO3)c2sccc2n1</td>\n",
       "      <td>O</td>\n",
       "      <td>298</td>\n",
       "      <td>-1.360945</td>\n",
       "      <td>Biogen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2173 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     UNIQUE_ID  \\\n",
       "0     b5e1231aef0080c76b4207ca1355485432877339   \n",
       "1     0604cf74e44450d5963d169e18854d96c4e5dba8   \n",
       "3     9be43035e7b8a5078babf4345584f4dc6dcd1448   \n",
       "5     70ac5fbe7181b1fce36dbaedaedcd3cdeb4fdf82   \n",
       "6     e9cafc1522a01017509e0fda082d84676cdc1f5e   \n",
       "...                                        ...   \n",
       "3511  6b39954041ed15e815bc15457e06440183006a00   \n",
       "3513  d08dfa20c576ef45061fd2af3a1c7acc286bc6d2   \n",
       "3514  c9bf3aca6d10ff3538ce617d83b9de8c80723024   \n",
       "3517  644313d1941ab4550760b7c1162c54d209a7435f   \n",
       "3518  32658afab9952faf20249dbb305e1ac85ef10a1b   \n",
       "\n",
       "                                             MOL_smiles  \\\n",
       "0                           Brc1cc2c(cc1Cn1cncn1)OCCCO2   \n",
       "1                   Brc1ccc(-c2nnc(Cn3cnc4ccccc43)o2)o1   \n",
       "3                                      Brc1cnc2ccccc2c1   \n",
       "5                    C#CCN1CCC(c2nc(Cc3noc(C)n3)no2)CC1   \n",
       "6               C#CCN1CCC[C@H]1C(=O)Nc1cnn(-c2ncccn2)c1   \n",
       "...                                                 ...   \n",
       "3511                        c1cncc(-c2ccnn2CN2CCOCC2)c1   \n",
       "3513              c1cnn(-c2ccc(-c3ccc(NC4CC4)nn3)cc2)c1   \n",
       "3514  c1csc(-c2csc3nc(CN4CCOCC4)nc(N4CCc5[nH]ncc5C4)...   \n",
       "3517                    c1nc(NC2CCN(C3CC3)CC2)c2sccc2n1   \n",
       "3518                  c1nc(Nc2ccc3c(c2)OCCCO3)c2sccc2n1   \n",
       "\n",
       "                                                 SMILES  LOG_HLM_CLint  \\\n",
       "0                           Brc1cc2c(cc1Cn1cncn1)OCCCO2       0.886265   \n",
       "1                   Brc1ccc(-c2nnc(Cn3cnc4ccccc43)o2)o1       0.675687   \n",
       "3                                      Brc1cnc2ccccc2c1            NaN   \n",
       "5                    C#CCN1CCC(c2nc(Cc3noc(C)n3)no2)CC1       0.675687   \n",
       "6               C#CCN1CCC[C@H]1C(=O)Nc1cnn(-c2ncccn2)c1       0.675687   \n",
       "...                                                 ...            ...   \n",
       "3511                        c1cncc(-c2ccnn2CN2CCOCC2)c1            NaN   \n",
       "3513              c1cnn(-c2ccc(-c3ccc(NC4CC4)nn3)cc2)c1            NaN   \n",
       "3514  c1csc(-c2csc3nc(CN4CCOCC4)nc(N4CCc5[nH]ncc5C4)...       2.747507   \n",
       "3517                    c1nc(NC2CCN(C3CC3)CC2)c2sccc2n1       1.240899   \n",
       "3518                  c1nc(Nc2ccc3c(c2)OCCCO3)c2sccc2n1       1.964066   \n",
       "\n",
       "      LOG_RLM_CLint  LOG_MDR1-MDCK_ER  LOG_HPPB  LOG_RPPB  LOG_SOLUBILITY  \\\n",
       "0          2.357933         -0.247518       NaN       NaN        1.536432   \n",
       "1          1.613704         -0.010669       NaN       NaN        1.797475   \n",
       "3               NaN               NaN       NaN       NaN       -0.033858   \n",
       "5          1.107108         -0.135635       NaN       NaN        1.695044   \n",
       "6          1.921166          0.596581       NaN       NaN        1.653984   \n",
       "...             ...               ...       ...       ...             ...   \n",
       "3511            NaN               NaN       NaN       NaN        1.239049   \n",
       "3513            NaN               NaN       NaN       NaN       -0.107905   \n",
       "3514       3.118970          1.921635       NaN       NaN        1.690107   \n",
       "3517       1.906157          0.094514       NaN       NaN        1.749891   \n",
       "3518       3.196559               NaN       NaN       NaN        1.707229   \n",
       "\n",
       "                                                mol       MW  sol_ug_ml  \\\n",
       "0     <rdkit.Chem.rdchem.Mol object at 0x150117f40>  310.151     34.390   \n",
       "1     <rdkit.Chem.rdchem.Mol object at 0x150187f40>  345.156     62.730   \n",
       "3     <rdkit.Chem.rdchem.Mol object at 0x150187ed0>  208.058      0.925   \n",
       "5     <rdkit.Chem.rdchem.Mol object at 0x150187e60>  287.323     49.550   \n",
       "6     <rdkit.Chem.rdchem.Mol object at 0x150187df0>  296.334     45.080   \n",
       "...                                             ...      ...        ...   \n",
       "3511  <rdkit.Chem.rdchem.Mol object at 0x150131e70>  244.298     17.340   \n",
       "3513  <rdkit.Chem.rdchem.Mol object at 0x150131ee0>  277.331      0.780   \n",
       "3514  <rdkit.Chem.rdchem.Mol object at 0x150131f50>  438.582     48.990   \n",
       "3517  <rdkit.Chem.rdchem.Mol object at 0x150131fc0>  274.393     56.220   \n",
       "3518  <rdkit.Chem.rdchem.Mol object at 0x150132030>  299.355     50.960   \n",
       "\n",
       "       ug_ml      LogS                                      solute_smiles  \\\n",
       "0     34.390 -3.955141                        Brc1cc2c(cc1Cn1cncn1)OCCCO2   \n",
       "1     62.730 -3.740540                Brc1ccc(-c2nnc(Cn3cnc4ccccc43)o2)o1   \n",
       "3      0.925 -5.352043                                   Brc1cnc2ccccc2c1   \n",
       "5     49.550 -3.763327                 C#CCN1CCC(c2nc(Cc3noc(C)n3)no2)CC1   \n",
       "6     45.080 -3.817798            C#CCN1CCC[C@H]1C(=O)Nc1cnn(-c2ncccn2)c1   \n",
       "...      ...       ...                                                ...   \n",
       "3511  17.340 -4.148871                        c1cncc(-c2ccnn2CN2CCOCC2)c1   \n",
       "3513   0.780 -5.550904              c1cnn(-c2ccc(-c3ccc(NC4CC4)nn3)cc2)c1   \n",
       "3514  48.990 -3.951943  c1csc(-c2csc3nc(CN4CCOCC4)nc(N4CCc5[nH]ncc5C4)...   \n",
       "3517  56.220 -3.688482                    c1nc(NC2CCN(C3CC3)CC2)c2sccc2n1   \n",
       "3518  50.960 -3.768957                  c1nc(Nc2ccc3c(c2)OCCCO3)c2sccc2n1   \n",
       "\n",
       "     solvent_smiles  temperature  predicted_LogS Dataset  \n",
       "0                 O          298       -1.341322  Biogen  \n",
       "1                 O          298       -2.414264  Biogen  \n",
       "3                 O          298       -1.294908  Biogen  \n",
       "5                 O          298       -1.561687  Biogen  \n",
       "6                 O          298       -1.749381  Biogen  \n",
       "...             ...          ...             ...     ...  \n",
       "3511              O          298       -1.242087  Biogen  \n",
       "3513              O          298       -1.602266  Biogen  \n",
       "3514              O          298       -2.662541  Biogen  \n",
       "3517              O          298       -1.360543  Biogen  \n",
       "3518              O          298       -1.360945  Biogen  \n",
       "\n",
       "[2173 rows x 19 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biogen_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b23c2743-6dc6-4d6f-bfa3-050f015189e1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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