Update parquet files

.gitattributes

@@ -0,0 +1 @@
+default/protein_ligand_contacts-train.parquet filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,48 +0,0 @@
----
-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
-sequence x smiles tokens (2048x512) matrix. The first and last entries in both dimensions
-are padded to zero, they correspond to [CLS] and [SEP].
-
-It can be used for fine-tuning a language model.
-
-The data solely uses data from PDBind-cn.
-
-Contacts are calculated at four cut-off distances: 5, 8, 11A and 15A.
-
-### 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_with_contacts.parquet → default/protein_ligand_contacts-train.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:90b17f9e0253534a0ffd558c231de5d715c5620ab5985fcec59cb7c759f145ee
-size 267149495
+oid sha256:8483e355645802bbb2f77d40bdde05d42012b52ad2d8951734ac8bec56e6efb4
+size 205345735

pdbbind.ipynb

@@ -1,505 +0,0 @@
-{
- "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": [
-       "<table>\n",
-       "<tr>\n",
-       "<td>\n",
-       "<table>\n",
-       "  <thead>\n",
-       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr><th> Bytes </th><td> 2.72 GiB </td> <td> 2.72 GiB </td></tr>\n",
-       "    <tr><th> Shape </th><td> (700, 2046, 510) </td> <td> (700, 2046, 510) </td></tr>\n",
-       "    <tr><th> Count </th><td> 25 Tasks </td><td> 1 Chunks </td></tr>\n",
-       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
-       "  </tbody>\n",
-       "</table>\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": null,
-   "id": "8f49f871-76f6-4fb2-b2db-c0794d4c07bf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "df_all_contacts = ddf_all.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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": null,
-   "id": "7c3db301-6565-4053-bbd4-139bb41dd1c4",
-   "metadata": {},
-   "outputs": [],
-   "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": null,
-   "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)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0e532f31-9157-47e9-be82-86c13565f3da",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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

@@ -1,111 +0,0 @@
-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])"""
-
-cutoff = int(sys.argv[1])
-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 = 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 = [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]
-        first_residue = next(structure.get_residues()).get_id()[1]
-        residue_id = [[c.get_id()[1]-first_residue for c in query if not c.get_id()[0].startswith(('H','W'))]
-            for query in close_residues] # zero-based, exclude hetero atoms
-
-        # 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/*'))
-    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_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_{}/'.format(cutoff), all_contacts)
-            df.to_parquet('data/pdbbind_complex_{}.parquet'.format(cutoff))

pdbbind.slurm

@@ -1,12 +0,0 @@
-#!/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 5
-srun python pdbbind.py 8
-srun python pdbbind.py 11
-srun python pdbbind.py 15

protein_ligand_contacts.py

@@ -1,133 +0,0 @@
-# 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+'pdbbind_with_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 ProteinLigandContacts(datasets.ArrowBasedBuilder):
-    """List of protein sequences, ligand SMILES, binding affinities and contacts."""
-
-    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_5A": datasets.Sequence(datasets.Value('int64')),
-                "contacts_8A": datasets.Sequence(datasets.Value('int64')),
-                "contacts_11A": datasets.Sequence(datasets.Value('int64')),
-                "contacts_15A": datasets.Sequence(datasets.Value('int64')),
-                # 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)