Datasets:

jglaser
/

protein_ligand_contacts

+---
+tags:
+- molecules
+- chemistry
+- SMILES
+---
+## How to use the data sets
+This dataset contains more than 16,000 unique pairs of protein sequences and ligand SMILES with experimentally determined
+binding affinities and protein-ligand contacts (ligand atom/SMILES token vs. Calpha within 5 Angstrom). These
+are represented by a list that contains the positions of non-zero elements of the flattened, sparse (2048,512)
+sequence x smiles tokens matrix.
+It can be used for fine-tuning a language model.
+The data solely uses data from PDBind-cn.
+### Use the already preprocessed data
+Load a test/train split using
+```
+from datasets import load_dataset
+train = load_dataset("jglaser/protein_ligand_contacts",split='train[:90%]')
+validation = load_dataset("jglaser/protein_lgiand_contacts",split='train[90%:]')
+```
+### Pre-process yourself
+To manually perform the preprocessing, download the data sets from P.DBBind-cn
+Register for an account at <https://www.pdbbind.org.cn/>, confirm the validation
+email, then login and download
+- the Index files (1)
+- the general protein-ligand complexes (2)
+- the refined protein-ligand complexes (3)
+Extract those files in `pdbbind/data`
+Run the script `pdbbind.py` in a compute job on an MPI-enabled cluster
+(e.g., `mpirun -n 64 pdbbind.py`).
+Perform the steps in the notebook `pdbbind.ipynb`

data/pdbbind_with_contacts.parquet ADDED Viewed

	@@ -0,0 +1,3 @@

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+size 16032905

pdbbind.ipynb ADDED Viewed

	@@ -0,0 +1,783 @@

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "834aeced-c3c5-42a0-bad1-41e009dd86ee",
+   "metadata": {},
+   "source": [
+    "### Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "86476f6e-802a-463b-a1b0-2ae228bb92af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9b2be11c-f4bb-4107-af49-abd78052afcf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_table('data/pdbbind/index/INDEX_general_PL_data.2020',skiprows=4,sep=r'\\s+',usecols=[0,4]).drop(0)\n",
+    "df = df.rename(columns={'#': 'name','release': 'affinity'})\n",
+    "df_refined = pd.read_table('data/pdbbind/index/INDEX_refined_data.2020',skiprows=4,sep=r'\\s+',usecols=[0,4]).drop(0)\n",
+    "df_refined = df_refined.rename(columns={'#': 'name','release': 'affinity'})\n",
+    "df = pd.concat([df,df_refined])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "68983ab8-bf11-4ed6-ba06-f962dbdc077e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quantities = ['ki','kd','ka','k1/2','kb','ic50','ec50']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3acbca3c-9c0b-43a1-a45e-331bf153bcfa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pint import UnitRegistry\n",
+    "ureg = UnitRegistry()\n",
+    "\n",
+    "def to_uM(affinity):\n",
+    "    val = ureg(affinity)\n",
+    "    try:\n",
+    "        return val.m_as(ureg.uM)\n",
+    "    except Exception:\n",
+    "        pass\n",
+    "    \n",
+    "    try:\n",
+    "        return 1/val.m_as(1/ureg.uM)\n",
+    "    except Exception:\n",
+    "        pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "58e5748b-2cea-43ff-ab51-85a5021bd50b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['affinity_uM'] = df['affinity'].str.split('[=\\~><]').str[1].apply(to_uM)\n",
+    "df['affinity_quantity'] = df['affinity'].str.split('[=\\~><]').str[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d92f0004-68c1-4487-94b9-56b4fd598de4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['affinity_quantity'].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "aa358835-55f3-4551-9217-e76a15de4fe8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_filter = df[df['affinity_quantity'].str.lower().isin(quantities)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d6dda488-f709-4fe7-b372-080042cf7c66",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_complex = pd.read_parquet('data/pdbbind_complex.parquet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "ed3fe035-6035-4d39-b072-d12dc0a95857",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import dask.array as da\n",
+    "import dask.dataframe as dd\n",
+    "from dask.bag import from_delayed\n",
+    "from dask import delayed\n",
+    "import pyarrow as pa\n",
+    "import pyarrow.parquet as pq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "cd26125b-e68b-4fa3-846e-2b6e7f635fe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "contacts_dask = da.from_npy_stack('data/pdbbind_contacts')\n",
+    "contacts_dask = contacts_dask.reshape(-1,contacts_dask.shape[-2]*contacts_dask.shape[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0e773f9d-6555-49c0-b0af-202c5e19b2cc",
+   "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>name</th>\n",
+       "      <th>seq</th>\n",
+       "      <th>smiles</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2lbv</td>\n",
+       "      <td>MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE...</td>\n",
+       "      <td>CCCCCCCCCCCCCCCCCCCC(=O)O</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1lt6</td>\n",
+       "      <td>APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE...</td>\n",
+       "      <td>O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4lwi</td>\n",
+       "      <td>VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE...</td>\n",
+       "      <td>COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6oyz</td>\n",
+       "      <td>VQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF...</td>\n",
+       "      <td>COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4i11</td>\n",
+       "      <td>GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA...</td>\n",
+       "      <td>CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   name                                                seq  \\\n",
+       "0  2lbv  MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE...   \n",
+       "1  1lt6  APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE...   \n",
+       "2  4lwi  VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE...   \n",
+       "3  6oyz  VQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF...   \n",
+       "4  4i11  GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA...   \n",
+       "\n",
+       "                                              smiles  \n",
+       "0                          CCCCCCCCCCCCCCCCCCCC(=O)O  \n",
+       "1         O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1  \n",
+       "2  COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2...  \n",
+       "3  COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC...  \n",
+       "4           CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_complex.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9c7c9849-2345-4baf-89e7-d412f52353b6",
+   "metadata": {},
+   "outputs": [
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+       "dask.array<blocks, shape=(438, 1048576), dtype=float32, chunksize=(438, 1048576), chunktype=numpy.ndarray>"
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+     "metadata": {},
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+    }
+   ],
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+    "contacts_dask.blocks[1]"
+   ]
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+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "d21a5d74-69f1-4a5a-85f8-917d3c1d690a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 1046,  2578,  2579,  2582,  2584,  6144,  6145,  6146,  6149,\n",
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+       "        58374, 58375, 58376, 58377, 58378, 58379, 58380, 58381]),)"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.where(contacts_dask.blocks[0].compute()[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "42e95d84-ef27-4417-9479-8b356462b8c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def chunk_to_sparse(chunk, idx_chunk):\n",
+    "    res = df_complex.iloc[idx_chunk].copy()\n",
+    "    res['contacts'] = [np.where(a) for a in chunk]\n",
+    "    return res\n",
+    "\n",
+    "partitions = [delayed(chunk_to_sparse)(b,k)\n",
+    "              for b,k in zip(contacts_dask.blocks, da.arange(contacts_dask.shape[0],chunks=contacts_dask.chunks[0:1]).blocks)\n",
+    "             ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "5520a925-693f-43f0-9e76-df2e128f272e",
+   "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>name</th>\n",
+       "      <th>seq</th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>contacts</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2lbv</td>\n",
+       "      <td>MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE...</td>\n",
+       "      <td>CCCCCCCCCCCCCCCCCCCC(=O)O</td>\n",
+       "      <td>([1046, 2578, 2579, 2582, 2584, 6144, 6145, 61...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1lt6</td>\n",
+       "      <td>APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE...</td>\n",
+       "      <td>O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1</td>\n",
+       "      <td>([514, 518, 520, 521, 522, 523, 525, 526, 546,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4lwi</td>\n",
+       "      <td>VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE...</td>\n",
+       "      <td>COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2...</td>\n",
+       "      <td>([521, 523, 526, 542, 544, 546, 547, 548, 1041...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6oyz</td>\n",
+       "      <td>VQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF...</td>\n",
+       "      <td>COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC...</td>\n",
+       "      <td>([35332, 35334, 35336, 35338, 35342, 35347, 35...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4i11</td>\n",
+       "      <td>GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA...</td>\n",
+       "      <td>CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1</td>\n",
+       "      <td>([36372, 36373, 36375, 36376, 36377, 36378, 36...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   name                                                seq  \\\n",
+       "0  2lbv  MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE...   \n",
+       "1  1lt6  APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE...   \n",
+       "2  4lwi  VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE...   \n",
+       "3  6oyz  VQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF...   \n",
+       "4  4i11  GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA...   \n",
+       "\n",
+       "                                              smiles  \\\n",
+       "0                          CCCCCCCCCCCCCCCCCCCC(=O)O   \n",
+       "1         O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1   \n",
+       "2  COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2...   \n",
+       "3  COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC...   \n",
+       "4           CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1   \n",
+       "\n",
+       "                                            contacts  \n",
+       "0  ([1046, 2578, 2579, 2582, 2584, 6144, 6145, 61...  \n",
+       "1  ([514, 518, 520, 521, 522, 523, 525, 526, 546,...  \n",
+       "2  ([521, 523, 526, 542, 544, 546, 547, 548, 1041...  \n",
+       "3  ([35332, 35334, 35336, 35338, 35342, 35347, 35...  \n",
+       "4  ([36372, 36373, 36375, 36376, 36377, 36378, 36...  "
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "partitions[0].compute().head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "4982c3b1-5ce9-4f17-9834-a02c4e136bc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ddf = dd.from_delayed(partitions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "f6cdee43-33c6-445c-8619-ace20f90638c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ddf_all = ddf.merge(df_filter, on='name').drop(columns=['affinity','affinity_quantity'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "8f49f871-76f6-4fb2-b2db-c0794d4c07bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_all_contacts = ddf_all.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "45e4b4fa-6338-4abe-bd6e-8aea46e2a09c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_all_contacts['neg_log10_affinity_M'] = 6-np.log10(df_all_contacts['affinity_uM'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "7c3db301-6565-4053-bbd4-139bb41dd1c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([6.3455065]), array([3.57430038]))"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "df_all_contacts['affinity'] = scaler.fit_transform(df_all_contacts['neg_log10_affinity_M'].values.reshape(-1,1))\n",
+    "scaler.mean_, scaler.var_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "c9d674bb-d6a2-4810-aa2b-e3bc3b4bbc98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_all_contacts.drop(columns=['name'],axis=1).to_parquet('data/pdbbind_with_contacts.parquet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "ec1afbc1-8072-4818-a925-0a9a6ad04759",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_all = df_complex.merge(df_filter,on='name').drop('affinity',axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "744f1e6e-a097-4b8f-8d4b-1a54f564dab8",
+   "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>name</th>\n",
+       "      <th>seq</th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>affinity_uM</th>\n",
+       "      <th>affinity_quantity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2lbv</td>\n",
+       "      <td>MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE...</td>\n",
+       "      <td>CCCCCCCCCCCCCCCCCCCC(=O)O</td>\n",
+       "      <td>0.026</td>\n",
+       "      <td>Kd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1lt6</td>\n",
+       "      <td>APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE...</td>\n",
+       "      <td>O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1</td>\n",
+       "      <td>500.000</td>\n",
+       "      <td>IC50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4lwi</td>\n",
+       "      <td>VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE...</td>\n",
+       "      <td>COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2...</td>\n",
+       "      <td>0.023</td>\n",
+       "      <td>IC50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6oyz</td>\n",
+       "      <td>VQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF...</td>\n",
+       "      <td>COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC...</td>\n",
+       "      <td>0.185</td>\n",
+       "      <td>IC50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4i11</td>\n",
+       "      <td>GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA...</td>\n",
+       "      <td>CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1</td>\n",
+       "      <td>27.200</td>\n",
+       "      <td>IC50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20822</th>\n",
+       "      <td>2bok</td>\n",
+       "      <td>IVGGQECKDGECPWQALLINEENEGFCGGTILSEFYILTAAHCLYQ...</td>\n",
+       "      <td>C[N+](C)(C)CCCN1C(=O)C2C(C1=O)C(c1ccc(C(=N)N)c...</td>\n",
+       "      <td>0.280</td>\n",
+       "      <td>Ki</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20823</th>\n",
+       "      <td>4j46</td>\n",
+       "      <td>GTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGD...</td>\n",
+       "      <td>CCC(C)C(NC(=O)C1CCCN1C(=O)C(NC(=O)C(C)[NH3+])C...</td>\n",
+       "      <td>5.240</td>\n",
+       "      <td>Ki</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20824</th>\n",
+       "      <td>4j46</td>\n",
+       "      <td>GTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGD...</td>\n",
+       "      <td>CCC(C)C(NC(=O)C1CCCN1C(=O)C(NC(=O)C(C)[NH3+])C...</td>\n",
+       "      <td>5.240</td>\n",
+       "      <td>Ki</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20825</th>\n",
+       "      <td>2c80</td>\n",
+       "      <td>DHIKVIYFNGRGRAESIRMTLVAAGVNYEDERISFQDWPKIKPTIP...</td>\n",
+       "      <td>CCCCCCSCC(NC(=O)CCC([NH3+])C(=O)O)C(=O)NCC(=O)O</td>\n",
+       "      <td>4.700</td>\n",
+       "      <td>Kd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20826</th>\n",
+       "      <td>2c80</td>\n",
+       "      <td>DHIKVIYFNGRGRAESIRMTLVAAGVNYEDERISFQDWPKIKPTIP...</td>\n",
+       "      <td>CCCCCCSCC(NC(=O)CCC([NH3+])C(=O)O)C(=O)NCC(=O)O</td>\n",
+       "      <td>4.700</td>\n",
+       "      <td>Kd</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20827 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       name                                                seq  \\\n",
+       "0      2lbv  MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE...   \n",
+       "1      1lt6  APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE...   \n",
+       "2      4lwi  VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE...   \n",
+       "3      6oyz  VQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF...   \n",
+       "4      4i11  GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA...   \n",
+       "...     ...                                                ...   \n",
+       "20822  2bok  IVGGQECKDGECPWQALLINEENEGFCGGTILSEFYILTAAHCLYQ...   \n",
+       "20823  4j46  GTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGD...   \n",
+       "20824  4j46  GTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGD...   \n",
+       "20825  2c80  DHIKVIYFNGRGRAESIRMTLVAAGVNYEDERISFQDWPKIKPTIP...   \n",
+       "20826  2c80  DHIKVIYFNGRGRAESIRMTLVAAGVNYEDERISFQDWPKIKPTIP...   \n",
+       "\n",
+       "                                                  smiles  affinity_uM  \\\n",
+       "0                              CCCCCCCCCCCCCCCCCCCC(=O)O        0.026   \n",
+       "1             O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1      500.000   \n",
+       "2      COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2...        0.023   \n",
+       "3      COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC...        0.185   \n",
+       "4               CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1       27.200   \n",
+       "...                                                  ...          ...   \n",
+       "20822  C[N+](C)(C)CCCN1C(=O)C2C(C1=O)C(c1ccc(C(=N)N)c...        0.280   \n",
+       "20823  CCC(C)C(NC(=O)C1CCCN1C(=O)C(NC(=O)C(C)[NH3+])C...        5.240   \n",
+       "20824  CCC(C)C(NC(=O)C1CCCN1C(=O)C(NC(=O)C(C)[NH3+])C...        5.240   \n",
+       "20825    CCCCCCSCC(NC(=O)CCC([NH3+])C(=O)O)C(=O)NCC(=O)O        4.700   \n",
+       "20826    CCCCCCSCC(NC(=O)CCC([NH3+])C(=O)O)C(=O)NCC(=O)O        4.700   \n",
+       "\n",
+       "      affinity_quantity  \n",
+       "0                    Kd  \n",
+       "1                  IC50  \n",
+       "2                  IC50  \n",
+       "3                  IC50  \n",
+       "4                  IC50  \n",
+       "...                 ...  \n",
+       "20822                Ki  \n",
+       "20823                Ki  \n",
+       "20824                Ki  \n",
+       "20825                Kd  \n",
+       "20826                Kd  \n",
+       "\n",
+       "[20827 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8f75499c-8895-4395-867e-7d9a9d394910",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_all.to_parquet('data/pdbbind.parquet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0af83f72-5bf6-4643-aa0e-0a94d51e7da7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20827"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(df_all)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

pdbbind.py ADDED Viewed

	@@ -0,0 +1,108 @@

+from mpi4py import MPI
+from mpi4py.futures import MPICommExecutor
+import warnings
+from Bio.PDB import PDBParser, PPBuilder, CaPPBuilder
+from Bio.PDB.NeighborSearch import NeighborSearch
+from Bio.PDB.Selection import unfold_entities
+import numpy as np
+import dask.array as da
+from rdkit import Chem
+import os
+import re
+# all punctuation
+punctuation_regex  = r"""(\(|\)|\.|=|#|-|\+|\\|\/|:|~|@|\?|>>?|\*|\$|\%[0-9]{2}|[0-9])"""
+# tokenization regex (Schwaller)
+molecule_regex = r"""(\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\(|\)|\.|=|#|-|\+|\\|\/|:|~|@|\?|>>?|\*|\$|\%[0-9]{2}|[0-9])"""
+cutoff = 5
+max_seq = 2048
+max_smiles = 512
+chunk_size = '1G'
+def parse_complex(fn):
+    try:
+        name = os.path.basename(fn)
+        # parse protein sequence and coordinates
+        parser = PDBParser()
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            structure = parser.get_structure('protein',fn+'/'+name+'_protein.pdb')
+#        ppb = PPBuilder()
+        ppb = CaPPBuilder()
+        seq = []
+        for pp in ppb.build_peptides(structure):
+            seq.append(str(pp.get_sequence()))
+        seq = ''.join(seq)
+        # parse ligand, convert to SMILES and map atoms
+        suppl = Chem.SDMolSupplier(fn+'/'+name+'_ligand.sdf')
+        mol = next(suppl)
+        smi = Chem.MolToSmiles(mol)
+        # position of atoms in SMILES (not counting punctuation)
+        atom_order = mol.GetProp("_smilesAtomOutputOrder")
+        atom_order = [int(s) for s in list(filter(None,re.sub(r'[\[\]]','',mol.GetProp("_smilesAtomOutputOrder")).split(',')))]
+        # tokenize the SMILES
+        tokens = list(filter(None, re.split(molecule_regex, smi)))
+        # remove punctuation
+        masked_tokens = [re.sub(punctuation_regex,'',s) for s in tokens]
+        k = 0
+        token_pos = []
+        token_id = []
+        for i,token in enumerate(masked_tokens):
+            if token != '':
+                token_pos.append(tuple(mol.GetConformer().GetAtomPosition(atom_order[k])))
+                token_id.append(i)
+                k += 1
+        # query protein for ligand contacts
+        atoms  = unfold_entities(structure, 'A')
+        neighbor_search = NeighborSearch(atoms)
+        close_residues = [neighbor_search.search(center=t, level='R', radius=cutoff) for t in token_pos]
+        residue_id = [[c.get_id()[1]-1 for c in query] for query in close_residues] # zero-based
+        # contact map
+        contact_map = np.zeros((max_seq, max_smiles),dtype=np.float32)
+        for query,t in zip(residue_id,token_id):
+            for r in query:
+                contact_map[r,t] = 1
+        return name, seq, smi, contact_map
+    except Exception as e:
+        print(e)
+        return None
+if __name__ == '__main__':
+    import glob
+    filenames = glob.glob('data/pdbbind/v2020-other-PL/*')
+    filenames.extend(glob.glob('data/pdbbind/refined-set/*'))
+    comm = MPI.COMM_WORLD
+    with MPICommExecutor(comm, root=0) as executor:
+        if executor is not None:
+            result = executor.map(parse_complex, filenames)
+            result = list(result)
+            names = [r[0] for r in result if r is not None]
+            seqs = [r[1] for r in result if r is not None]
+            all_smiles = [r[2] for r in result if r is not None]
+            all_contacts = [r[3] for r in result if r is not None]
+            import pandas as pd
+            df = pd.DataFrame({'name': names, 'seq': seqs, 'smiles': all_smiles})
+            all_contacts = da.from_array(all_contacts, chunks=chunk_size)
+            da.to_npy_stack('data/pdbbind_contacts/', all_contacts)
+            df.to_parquet('data/pdbbind_complex.parquet')

pdbbind.slurm ADDED Viewed

	@@ -0,0 +1,9 @@

+#!/bin/bash
+#SBATCH -J preprocess_pdbbind
+#SBATCH -p gpu
+#SBATCH -A STF006
+#SBATCH -t 3:00:00
+#SBATCH -N 2
+#SBATCH --ntasks-per-node=16
+srun python pdbbind.py

protein_ligand_contacts.py ADDED Viewed

	@@ -0,0 +1,130 @@

+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TODO: A dataset of protein sequences, ligand SMILES, binding affinities and contacts."""
+import huggingface_hub
+import os
+import pyarrow.parquet as pq
+import datasets
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {jglaser/protein_ligand_contacts},
+author={Jens Glaser, ORNL
+},
+year={2022}
+}
+"""
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+A dataset to fine-tune language models on protein-ligand binding affinity and contact prediction.
+"""
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "BSD two-clause"
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URL = "https://huggingface.co/datasets/jglaser/protein_ligand_contacts/resolve/main/"
+_data_dir = "data/"
+_file_names = {'default': _data_dir+'pddbind_contacts.parquet'}
+_URLs = {name: _URL+_file_names[name] for name in _file_names}
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class BindingAffinity(datasets.ArrowBasedBuilder):
+    """List of protein sequences, ligand SMILES and binding affinities."""
+    VERSION = datasets.Version("1.4.1")
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        #if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+        #    features = datasets.Features(
+        #        {
+        #            "sentence": datasets.Value("string"),
+        #             "option1": datasets.Value("string"),
+        #            "answer": datasets.Value("string")
+        #            # These are the features of your dataset like images, labels ...
+        #        }
+        #    )
+        #else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
+        features = datasets.Features(
+            {
+                "seq": datasets.Value("string"),
+                "smiles": datasets.Value("string"),
+                "affinity_uM": datasets.Value("float"),
+                "neg_log10_affinity_M": datasets.Value("float"),
+                "affinity": datasets.Value("float"),
+                "contacts": datasets.Sequence('int'),
+                # These are the features of your dataset like images, labels ...
+            }
+        )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        files = dl_manager.download_and_extract(_URLs)
+        return [
+            datasets.SplitGenerator(
+                # These kwargs will be passed to _generate_examples
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    'filepath': files["default"],
+                },
+            ),
+        ]
+    def _generate_tables(
+        self, filepath
+    ):
+        from pyarrow import fs
+        local = fs.LocalFileSystem()
+        for i, f in enumerate([filepath]):
+            yield i, pq.read_table(f,filesystem=local)