molecule_metrics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.ndimage.filters import gaussian_filter1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/divyanori/miniconda3/envs/my-rdkit-env/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'3.2.1'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem, DataStructs\n",
    "from rdkit.Chem import SmilesMolSupplier\n",
    "import re\n",
    "\n",
    "from datetime import date\n",
    "import gzip\n",
    "import numpy as np\n",
    "pd.set_option(\"display.max_columns\",100, \"display.width\",2000, \"display.max_colwidth\",100)\n",
    "import pickle\n",
    "from scipy.sparse import csr_matrix, vstack\n",
    "from sklearn.metrics import classification_report, confusion_matrix, f1_score, roc_auc_score\n",
    "from sklearn.utils import class_weight\n",
    "from termcolor import colored\n",
    "from sklearn.model_selection import train_test_split\n",
    "import sklearn.metrics\n",
    "\n",
    "import optuna\n",
    "optuna.logging.set_verbosity(optuna.logging.WARN) #INFO, WARN\n",
    "import lightgbm as lgb; lgb.__version__\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_loss(convergence_log_file):\n",
    "    data = []\n",
    "    with open(convergence_log_file, 'r') as f:\n",
    "        lines = f.readlines()\n",
    "        for line in lines:\n",
    "            lst = line.split(',')\n",
    "            lst[-1] = lst[-1][:-1]\n",
    "            data.append(lst)\n",
    "    df = pd.DataFrame(data)\n",
    "    df = df.rename(columns=df.iloc[0])\n",
    "    df = df.drop(0)\n",
    "    df.columns = df.columns.str.strip()\n",
    "    df[\"epoch\"] = df[\"epoch\"].str[6:].astype(int)\n",
    "\n",
    "    for x in df.columns[1:]:\n",
    "        try:\n",
    "            df[x] = df[x].str.strip().replace(\"NA\",np.nan).astype(float)\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "    plot_df = df[[\"epoch\",\"avg_train_loss\",\"avg_valid_loss\"]]\n",
    "\n",
    "    sns.set(rc = {'figure.figsize':(12,6)})\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    sns.set_context(\"notebook\", font_scale=1.5, rc={\"lines.linewidth\": 2.5})\n",
    "\n",
    "    _ = sns.lineplot(x=\"epoch\", y=\"value\", hue=\"variable\",  palette=['red', '#00A4CCFF'], alpha=0.7, data=pd.melt(plot_df, [\"epoch\"]))\n",
    "    _.set_xlabel(\"Epoch\")\n",
    "    _.set_ylabel(\"Value\")\n",
    "    _.legend(labels=[\"average train loss\", \"average valid loss\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_loss('protac_training_logs/0811/convergence.log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_fraction(generation_log_file):\n",
    "    data = []\n",
    "    with open(generation_log_file, 'r') as f:\n",
    "        lines = f.readlines()\n",
    "        for line in lines:\n",
    "            lst = line.split(',')\n",
    "            data.append(lst)\n",
    "    df = pd.DataFrame(data)\n",
    "    df = df.rename(columns=df.iloc[0])\n",
    "    df = df.drop(0)\n",
    "    df = df.drop(1)\n",
    "    df.columns = df.columns.str.strip()\n",
    "    df[\"set\"] = df[\"set\"].str[6:].astype(int)\n",
    "\n",
    "    for x in df.columns[1:]:\n",
    "        try:\n",
    "            df[x] = df[x].str.strip().replace(\"NA\",np.nan).astype(float)\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "\n",
    "    plot_df = df[[\"set\",\"fraction_valid\"]]\n",
    "\n",
    "    sns.set(rc = {'figure.figsize':(12,6)})\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    sns.set_context(\"notebook\", font_scale=1.5, rc={\"lines.linewidth\": 2.5})\n",
    "\n",
    "    data_df = pd.melt(plot_df, [\"set\"])\n",
    "    x_vals = np.array(data_df['set'].tolist())\n",
    "    y_vals = np.array(data_df['value'].tolist())\n",
    "\n",
    "\n",
    "    _ = sns.lineplot(x=\"set\", y=\"value\", hue=\"variable\",  data=data_df, palette=['#00A4CCFF'])\n",
    "    _.set_xlabel(\"Epoch\")\n",
    "    _.set_ylabel(\"Value\")\n",
    "    _.legend(labels=[\"fraction valid\"])\n",
    "\n",
    "    upper = gaussian_filter1d(y_vals + 0.004, sigma=3)\n",
    "    lower = gaussian_filter1d(y_vals - 0.004, sigma=3)\n",
    "    _.fill_between(x_vals, upper, lower, color='red', alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_fraction('protac_training_logs/0811/generation.log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Molecule Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regeneration Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def regen_percent(gen_smi, orig_smi):\n",
    "    unique = 0\n",
    "    with open(gen_smi) as file1:\n",
    "        lines = file1.readlines()\n",
    "        gen_lines = [line.rstrip() for line in lines]\n",
    "    with open(orig_smi) as file2:\n",
    "        lines = file2.readlines()\n",
    "        orig_lines = [line.rstrip() for line in lines]\n",
    "    for smi in gen_lines:\n",
    "        if smi in orig_lines:\n",
    "            pass\n",
    "        else:\n",
    "            unique+=1\n",
    "    return (len(gen_lines)-unique)/len(gen_lines)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Duplicate Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def duplicate_percent(gen_smi):\n",
    "    duplicates = 0\n",
    "    unique_lst = {}\n",
    "    with open(gen_smi) as file1:\n",
    "        lines = file1.readlines()\n",
    "        gen_lines = [line.rstrip().split()[0] for line in lines]\n",
    "    for smi in gen_lines:\n",
    "        if smi in unique_lst:\n",
    "            duplicates+=1\n",
    "            unique_lst[smi] +=1\n",
    "        else:\n",
    "            unique_lst[smi] = 1\n",
    "    unique_smi = unique_lst.keys()\n",
    "    with open('data/generated_protacs_after_finetune/e3_unique_filtered.smi', 'w') as f:\n",
    "        for smi in unique_smi:\n",
    "            if len(smi)>70:\n",
    "                f.write(f\"{smi}\\n\")\n",
    "    return duplicates/len(gen_lines), unique_lst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avg N Nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import rdkit\n",
    "from rdkit import Chem\n",
    "from statistics import mean\n",
    "def avg_n_nodes(gen_smi):\n",
    "    n_counts = []\n",
    "    with open(gen_smi) as file1:\n",
    "        lines = file1.readlines()\n",
    "        gen_lines = [line.rstrip().split()[0] for line in lines]\n",
    "    for smi in gen_lines:\n",
    "        if \"Xe\" not in smi:\n",
    "            n = Chem.MolFromSmiles(smi).GetNumAtoms()\n",
    "            n_counts.append(n)\n",
    "    return mean(n_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_n_nodes('data/protac_train.smi')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Percent Active"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "import requests as r\n",
    "def predictDegradation(mol, cellType='SRD15', receptor='Q9Y616', e3Ligase='CRBN'):\n",
    "    scoring_model = pickle.load(open('surrogate_model.pkl', 'rb'))\n",
    "    with open('features.pkl','rb') as fp:\n",
    "        features = pickle.load(fp)\n",
    "\n",
    "    ngrams_array = np.zeros((1,7841), dtype=np.int8)\n",
    "    # baseUrl=\"http://www.uniprot.org/uniprot/\"\n",
    "    # currentUrl=baseUrl+receptor+\".fasta\"\n",
    "    # response = r.post(currentUrl)\n",
    "    # cData=''.join(response.text)\n",
    "    # i = cData.index('\\n')+1\n",
    "    # seq = cData[i:].strip().lower()\n",
    "    seq = \"MAGNCGARGALSAHTLLFDLPPALLGELCAVLDSCDGALGWRGLAERLSSSWLDVRHIEKYVDQGKSGTRELLWSWAQKNKTIGDLLQVLQEMGHRRAIHLITNYGAVLSPSEKSYQEGGFPNILFKETANVTVDNVLIPEHNEKGILLKSSISFQNIIEGTRNFHKDFLIGEGEIFEVYRVEIQNLTYAVKLFKQEKKMQCKKHWKRFLSELEVLLLFHHPNILELAAYFTETEKFCLIYPYMRNGTLFDRLQCVGDTAPLPWHIRIGILIGISKAIHYLHNVQPCSVICGSISSANILLDDQFQPKLTDFAMAHFRSHLEHQSCTINMTSSSSKHLWYMPEEYIRQGKLSIKTDVYSFGIVIMEVLTGCRVVLDDPKHIQLRDLLRELMEKRGLDSCLSFLDKKVPPCPRNFSAKLFCLAGRCAATRAKLRPSMDEVLNTLESTQASLYFAEDPPTSLKSFRCPSPLFLENVPSIPVEDDESQNNNLLPSDEGLRIDRMTQKTPFECSQSEVMFLSLDKKPESKRNEEACNMPSSSCEESWFPKYIVPSQDLRPYKVNIDPSSEAPGHSCRSRPVESSCSSKFSWDEYEQYKKE\".lower()\n",
    "    ngrams = features[1238:]\n",
    "    for i in range(len(ngrams)):\n",
    "        n = seq.count(ngrams[i])\n",
    "        ngrams_array[0][i] = n\n",
    "        \n",
    "    fingerprint = AllChem.GetMorganFingerprintAsBitVect(mol, 2, nBits=1024)\n",
    "    fp_array = np.zeros((0,), dtype=np.int8)\n",
    "    DataStructs.ConvertToNumpyArray(fingerprint, fp_array)\n",
    "\n",
    "    ct_ind = features.index(\"ct_\"+cellType)\n",
    "    e3_ind = features.index(\"e3_\"+e3Ligase)\n",
    "\n",
    "    input = list(0 for i in range(5)) + list(fp_array) + list(0 for i in range(207))+ list(ngrams_array[0])\n",
    "    input[ct_ind] = 1\n",
    "    input[e3_ind] = 1\n",
    "\n",
    "    output = scoring_model.predict([input])\n",
    "    if output[0][0]-output[0][1] < 0:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    #return output[0].argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def score(smi_file):\n",
    "    # load molecules from file\n",
    "    mols = SmilesMolSupplier(smi_file, sanitize=True, nameColumn=-1,titleLine=True)\n",
    "    mols_list = [mol for mol in mols]\n",
    "    print('created mols list')\n",
    "\n",
    "    activity_scores = []\n",
    "    pos_mols_list = []\n",
    "    neg_mols_list = []\n",
    "    for m in mols_list:\n",
    "        if m is None:\n",
    "            pass\n",
    "        else:\n",
    "            s = predictDegradation(m)\n",
    "            activity_scores.append(s)\n",
    "            if s>0:\n",
    "                pos_mols_list.append(m)\n",
    "            else:\n",
    "                neg_mols_list.append(m)\n",
    "    active_percent = sum(activity_scores)/len(activity_scores)\n",
    "    neg_smi_list = [Chem.MolToSmiles(mol) for mol in neg_mols_list]\n",
    "    pos_smi_list = [Chem.MolToSmiles(mol) for mol in pos_mols_list]\n",
    "    return active_percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "created mols list\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.3473389355742297"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score(\"data/generated_protacs_before_finetune/epoch114_preft_2500_zenodo.smi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "created mols list\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5078031212484994"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score(\"data/generated_protacs_before_finetune/epoch192_preft_2500_zenodo.smi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "created mols list\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.59343737494998"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score(\"data/generated_protacs_after_finetune/epoch114_postft_2500_zenodo.smi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RDKit ERROR: [12:29:29] SMILES Parse Error: extra open parentheses for input: 'O=C1CCC(N2C(=O)c3cccc(O'\n",
      "[12:29:29] SMILES Parse Error: extra open parentheses for input: 'O=C1CCC(N2C(=O)c3cccc(O'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "created mols list\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8381676335267053"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score(\"data/generated_protacs_after_finetune/epoch192_postft_10000_zenodo.smi\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diversity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit.DataStructs import FingerprintSimilarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarity = []\n",
    "for i_mol, smiles_1 in enumerate(['O=C1CCC(N2C(=O)c3cccc(NCCOCCCC(=O)Nc4ccc(N5Cc6ccc(O)cc6OC5=O)cc4)c3C2=O)C(=O)N1','NC1NCC1NC(=O)C1CCN=CCCCCCC(=O)Nc2cccc3c2C(=O)N(C3=O)C2CCC(=O)N1C2=O']):\n",
    "    mol_similarity = []\n",
    "    for j_mol, smiles_2 in enumerate(['CCCCCCC(C)C(N)=O','CC(C)(C)C(NC(=O)CCCCC(=O)NCCOCCOCCCCCN1Cc2c(NC(=O)NCCCCCCCCC(=O)NO)cccc2C1=O)C(=O)N1CC(O)CC1C(=O)NCc1ccccc1']):\n",
    "        if i_mol != j_mol and  smiles_1 and smiles_2 and smiles_1 != \"[Xe]\" and smiles_2 != \"[Xe]\":\n",
    "            mol_1 = Chem.MolFromSmiles(smiles_1)\n",
    "            mol_2 = Chem.MolFromSmiles(smiles_2)\n",
    "            mol_similarity.append(FingerprintSimilarity(Chem.RDKFingerprint(mol_1), Chem.RDKFingerprint(mol_2)) )\n",
    "    similarity.append(mol_similarity) #append mean similarity of smiles_1\n",
    "mean_similarity = np.mean(np.mean(mol_similarity))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit.Chem.Scaffolds import MurckoScaffold\n",
    "def count_murcko_scaffolds(file):\n",
    "    with open(file) as file1:\n",
    "        lines = file1.readlines()\n",
    "        smis = [line.rstrip().split()[0] for line in lines]\n",
    "    scaffolds = []\n",
    "    for smi in smis:\n",
    "        scaffolds.append(MurckoScaffold.MurckoScaffoldSmiles(smi))\n",
    "    print(len(smis))\n",
    "    print((len(set(scaffolds))))\n",
    "    return scaffolds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5152\n",
      "2907\n"
     ]
    }
   ],
   "source": [
    "scaffolds_orig = count_murcko_scaffolds('data/protac/orig.smi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n",
      "537\n"
     ]
    }
   ],
   "source": [
    "scaffolds_protac = count_murcko_scaffolds('data/generated_protacs_after_finetune/epoch192_postft.smi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaffolds_shared = set(scaffolds_orig) & set(scaffolds_protac))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f7f682d0a00>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#most common scaffold\n",
    "Chem.MolFromSmiles('O=C1CCC(N2Cc3ccccc3C2=O)C(=O)N1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "scaffolds_to_count = (scaf for scaf in scaffolds_orig if scaf[:1].isupper())\n",
    "c = Counter(scaffolds_to_count)\n",
    "orig_most_common = c.most_common(25)\n",
    "orig_most_common = [x[0] for x in orig_most_common]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "scaffolds_to_count = (scaf for scaf in scaffolds_protac if scaf[:1].isupper())\n",
    "c = Counter(scaffolds_to_count)\n",
    "protac_most_common = c.most_common(25)\n",
    "protac_most_common = [x[0] for x in protac_most_common]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaffolds_shared = set(orig_most_common) & set(protac_most_common)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'O=C1CCC(N2C(=O)c3cccc(NCCCCCCCCN4CCC(NC(=O)c5[nH]ncc5NC(=O)c5ccccc5)CC4)c3C2=O)C(=O)N1',\n",
       " 'O=C1CCC(N2Cc3ccccc3C2=O)C(=O)N1'}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaffolds_shared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drug-Likeness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit.Chem import Lipinski\n",
    "\n",
    "def lipinski_rules(file):\n",
    "    with open(file) as file1:\n",
    "        lines = file1.readlines()\n",
    "        smis = [line.rstrip().split()[0] for line in lines]\n",
    "    sum_h_acceptors = 0\n",
    "    sum_h_donors = 0\n",
    "    for smi in smis:\n",
    "        sum_h_acceptors += Lipinski.NumHAcceptors(Chem.MolFromSmiles(smi))\n",
    "        sum_h_donors += Lipinski.NumHDonors(Chem.MolFromSmiles(smi))\n",
    "    print(f\"avg number of hydrogen bond acceptors {sum_h_acceptors/len(smis)}\")\n",
    "    print(f\"avg number of hydrogen bond donors {sum_h_donors/len(smis)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lipinski_rules('data/protac/orig.smi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lipinski_rules('data/generated_protacs_before_finetune_0810/epoch192_preft.smi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lipinski_rules('data/generated_protacs_after_finetune/epoch192_postft.smi')"
   ]
  }
 ],
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