initial commit

README.md

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+---
+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, and the coordinates
+of their complexes.
+
+SMILES are assumed to be tokenized by the regex from P. Schwaller
+
+Every (x,y,z) ligand coordinate maps onto a SMILES token, and is *nan* if the token does not represent an atom
+
+Every receptor coordinate maps onto the Calpha coordinate of that residue.
+
+The dataset can be used to fine-tune a language model, all data comes 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_ligand_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.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3e7a0f58df3e45dec1c90b30f2bd8ab62c601a1073dafa1d426e0f36413ecf30
+size 141173725

pdbbind.ipynb

@@ -0,0 +1,517 @@
+{
+ "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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+      "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)]\n",
+    "df_filter = df_filter.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "802cb9bc-2563-4d7f-9a76-3be2d9263a36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cutoffs = [5,8,11,15]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d8e71a8c-11a3-41f0-ab61-3ddc57e10961",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfs_complex = {c: pd.read_parquet('data/pdbbind_complex_{}.parquet'.format(c)) for c in cutoffs}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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": 11,
+   "id": "cd26125b-e68b-4fa3-846e-2b6e7f635fe0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2046, 510)\n"
+     ]
+    }
+   ],
+   "source": [
+    "contacts_dask = [da.from_npy_stack('data/pdbbind_contacts_{}'.format(c)) for c in cutoffs]\n",
+    "shape = contacts_dask[0][0].shape\n",
+    "print(shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9c7c9849-2345-4baf-89e7-d412f52353b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
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+       "    <tr><th> Shape </th><td> (700, 2046, 510) </td> <td> (700, 2046, 510) </td></tr>\n",
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+       "\n",
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+       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"145\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
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+       "</svg>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "dask.array<blocks, shape=(700, 2046, 510), dtype=float32, chunksize=(700, 2046, 510), chunktype=numpy.ndarray>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "contacts_dask[0].blocks[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0bd8e9b9-9713-4572-bd7f-dc47da9fce91",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[16232, 16228, 16226, 16223]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[len(c) for c in contacts_dask]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "87493934-3839-476a-a975-7da057c320da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16232"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "contacts_dask[0].shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "42e95d84-ef27-4417-9479-8b356462b8c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "all_partitions = []\n",
+    "for c, cutoff in zip(contacts_dask,cutoffs):\n",
+    "    def chunk_to_sparse(rcut, chunk, idx_chunk):\n",
+    "        res = dfs_complex[rcut].iloc[idx_chunk][['name']].copy()\n",
+    "        # pad to account for [CLS] and [SEP]\n",
+    "        res['contacts_{}A'.format(rcut)] = [np.where(np.pad(a,pad_width=(1,1)).flatten())[0] for a in chunk]\n",
+    "        return res\n",
+    "\n",
+    "    partitions = [delayed(chunk_to_sparse)(cutoff,b,k)\n",
+    "                  for b,k in zip(c.blocks, da.arange(c.shape[0],chunks=c.chunks[0:1]).blocks)\n",
+    "                 ]\n",
+    "    all_partitions.append(partitions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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>contacts_5A</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10gs</td>\n",
+       "      <td>[3083, 3084, 3086, 3087, 3088, 3089, 3094, 309...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>184l</td>\n",
+       "      <td>[39945, 39946, 39947, 39948, 43010, 43012, 430...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>186l</td>\n",
+       "      <td>[39943, 39944, 39945, 43010, 43011, 43012, 430...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>187l</td>\n",
+       "      <td>[39937, 39938, 39947, 43009, 43010, 43012, 430...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>188l</td>\n",
+       "      <td>[39937, 39938, 39940, 39941, 43009, 43010, 430...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   name                                        contacts_5A\n",
+       "0  10gs  [3083, 3084, 3086, 3087, 3088, 3089, 3094, 309...\n",
+       "1  184l  [39945, 39946, 39947, 39948, 43010, 43012, 430...\n",
+       "2  186l  [39943, 39944, 39945, 43010, 43011, 43012, 430...\n",
+       "3  187l  [39937, 39938, 39947, 43009, 43010, 43012, 430...\n",
+       "4  188l  [39937, 39938, 39940, 39941, 43009, 43010, 430..."
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_partitions[0][0].compute().head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "4982c3b1-5ce9-4f17-9834-a02c4e136bc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ddfs = [dd.from_delayed(p) for p in all_partitions]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "f6cdee43-33c6-445c-8619-ace20f90638c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ddf_all = None\n",
+    "for d in ddfs:\n",
+    "    if ddf_all is not None:\n",
+    "        ddf_all = ddf_all.merge(d, on='name')\n",
+    "    else:\n",
+    "        ddf_all = d\n",
+    "ddf_all = ddf_all.merge(df_filter,on='name')\n",
+    "ddf_all = ddf_all.merge(list(dfs_complex.values())[0],on='name')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "8f49f871-76f6-4fb2-b2db-c0794d4c07bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 8min 53s, sys: 11min 31s, total: 20min 24s\n",
+      "Wall time: 3min 29s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "df_all_contacts = ddf_all.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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": 21,
+   "id": "7c3db301-6565-4053-bbd4-139bb41dd1c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([6.34387834]), array([3.57815698]))"
+      ]
+     },
+     "execution_count": 21,
+     "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": 22,
+   "id": "c9d674bb-d6a2-4810-aa2b-e3bc3b4bbc98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save to parquet\n",
+    "df_all_contacts.drop(columns=['name','affinity_quantity']).astype({'affinity': 'float32','neg_log10_affinity_M': 'float32'}).to_parquet('data/pdbbind_with_contacts.parquet',index=False)"
+   ]
+  }
+ ],
+ "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

@@ -0,0 +1,95 @@
+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
+import sys
+
+# 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])"""
+
+max_seq = 2046 # = 2048 - 2 (accounting for [CLS] and [SEP])
+max_smiles = 510 # = 512 - 2
+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 = CaPPBuilder()
+        seq = []
+        xyz_receptor = []
+        for pp in ppb.build_peptides(structure):
+            seq.append(str(pp.get_sequence()))
+            xyz_receptor += [tuple(a.get_vector()) for a in pp.get_ca_list()]
+        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 = [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 = []
+        for i,token in enumerate(masked_tokens):
+            if token != '':
+                token_pos.append(tuple(mol.GetConformer().GetAtomPosition(atom_order[k])))
+                k += 1
+            else:
+                token_pos.append((np.nan, np.nan, np.nan))
+
+        return name, seq, smi, xyz_receptor, token_pos
+
+    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/*'))
+    filenames = sorted(filenames)
+    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_xyz_receptor = [r[3] for r in result if r is not None]
+            all_xyz_ligand = [r[4] for r in result if r is not None]
+
+            import pandas as pd
+            df = pd.DataFrame({'name': names, 'seq': seqs, 'smiles': all_smiles, 'receptor_xyz': all_xyz_receptor, 'ligand_xyz': all_xyz_ligand})
+            df.to_parquet('data/pdbbind.parquet')

pdbbind.slurm

@@ -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

pdbbind_complexes.py

@@ -0,0 +1,128 @@
+# 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/pdbbind_complexes},
+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/pdbbind_complexes/resolve/main/"
+_data_dir = "data/"
+_file_names = {'default': _data_dir+'pdbbind.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 ProteinLigandContacts(datasets.ArrowBasedBuilder):
+    """List of protein sequences, ligand SMILES, binding affinities and contacts."""
+
+    VERSION = datasets.Version("1.0")
+
+    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"),
+                "ligand_xyz": datasets.Sequence(datasets.Sequence(datasets.Value('float32'))),
+                "receptor_xyz": datasets.Sequence(datasets.Sequence(datasets.Value('float32'))),
+                # 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)