jglaser commited on
Commit
8a5e8e2
1 Parent(s): 759172e

add more cutoff distances

Browse files
Untitled.ipynb ADDED
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 11,
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+ "id": "7e0c5ceb-24ca-426a-a99b-5e5f63561246",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "from datasets import load_dataset"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 15,
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+ "id": "03a41685-5d16-4555-a24b-c87d72a233fe",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "Using custom data configuration default\n",
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+ "Reusing dataset protein_ligand_contacts (/ccs/home/glaser/.cache/huggingface/datasets/jglaser___protein_ligand_contacts/default/1.4.1/2aba91a819153bdd9a95ce28edf727166722133978758c388eb80b7d587ecce7)\n"
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+ " 0%| | 0/1 [00:00<?, ?it/s]"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ }
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+ ],
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+ "source": [
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+ "ds = load_dataset('jglaser/protein_ligand_contacts')"
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+ ]
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+ },
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+ "execution_count": null,
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pdbbind.ipynb CHANGED
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- "id": "d6dda488-f709-4fe7-b372-080042cf7c66",
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  "metadata": {},
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  "source": [
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- "df_complex = pd.read_parquet('data/pdbbind_complex.parquet')"
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  "metadata": {},
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  "outputs": [],
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  "source": [
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- "import dask.array as da\n",
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- "import dask.dataframe as dd\n",
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- "from dask.bag import from_delayed\n",
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- "from dask import delayed\n",
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- "import pyarrow as pa\n",
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- "import pyarrow.parquet as pq"
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  ]
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  },
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- "contacts_dask = da.from_npy_stack('data/pdbbind_contacts')\n",
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- "contacts_dask = contacts_dask.reshape(-1,contacts_dask.shape[-2]*contacts_dask.shape[-1])"
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  "</table>\n",
@@ -225,32 +209,58 @@
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  ],
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  "text/plain": [
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  " name seq \\\n",
228
- "0 2lbv MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE... \n",
229
- "1 1lt6 APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE... \n",
230
- "2 4lwi VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE... \n",
231
- "3 6oyz VQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF... \n",
232
- "4 4i11 GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA... \n",
233
  "\n",
234
  " smiles \n",
235
- "0 CCCCCCCCCCCCCCCCCCCC(=O)O \n",
236
- "1 O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1 \n",
237
- "2 COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2... \n",
238
- "3 COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC... \n",
239
- "4 CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1 "
240
  ]
241
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  "metadata": {},
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  "output_type": "execute_result"
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  }
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  ],
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  "source": [
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- "df_complex.head()"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ]
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  },
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  "cell_type": "code",
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  "execution_count": 12,
 
 
 
 
 
 
 
 
 
 
 
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  "id": "9c7c9849-2345-4baf-89e7-d412f52353b6",
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  "metadata": {},
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  "outputs": [
@@ -298,37 +308,106 @@
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  "dask.array<blocks, shape=(438, 1043460), dtype=float32, chunksize=(438, 1043460), chunktype=numpy.ndarray>"
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  ]
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  },
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- "execution_count": 12,
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  "metadata": {},
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  "output_type": "execute_result"
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  }
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- "contacts_dask.blocks[1]"
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  ]
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 19,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  "id": "42e95d84-ef27-4417-9479-8b356462b8c3",
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  "metadata": {},
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  "outputs": [],
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  "source": [
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  "import numpy as np\n",
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- "def chunk_to_sparse(chunk, idx_chunk):\n",
319
- " res = df_complex.iloc[idx_chunk].copy()\n",
320
- " # pad to account for [CLS] and [SEP]\n",
321
- " res['contacts'] = [np.where(np.pad(a,pad_width=(1,1)))[0] for a in chunk]\n",
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- " return res\n",
 
 
323
  "\n",
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- "partitions = [delayed(chunk_to_sparse)(b,k)\n",
325
- " for b,k in zip(contacts_dask.blocks, da.arange(contacts_dask.shape[0],chunks=contacts_dask.chunks[0:1]).blocks)\n",
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- " ]"
 
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@@ -354,106 +433,87 @@
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  " <tr style=\"text-align: right;\">\n",
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  " <th></th>\n",
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  " <th>name</th>\n",
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- " <th>seq</th>\n",
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- " <th>smiles</th>\n",
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- " <th>contacts</th>\n",
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  " </tr>\n",
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  " </thead>\n",
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  " <tbody>\n",
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  " <tr>\n",
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  " <th>0</th>\n",
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- " <td>CCCCCCCCCCCCCCCCCCCC(=O)O</td>\n",
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- " <td>[1043, 2569, 2570, 2573, 2575, 6121, 6122, 612...</td>\n",
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  " </tr>\n",
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  " <tr>\n",
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  " <th>1</th>\n",
372
- " <td>1lt6</td>\n",
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- " <td>APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE...</td>\n",
374
- " <td>O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1</td>\n",
375
- " <td>[513, 517, 519, 520, 521, 522, 524, 525, 545, ...</td>\n",
376
  " </tr>\n",
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  " <tr>\n",
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  " <th>2</th>\n",
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- " <td>4lwi</td>\n",
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- " <td>COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2...</td>\n",
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- " <td>[520, 522, 525, 541, 543, 545, 546, 547, 1038,...</td>\n",
383
  " </tr>\n",
384
  " <tr>\n",
385
  " <th>3</th>\n",
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- " <td>6oyz</td>\n",
387
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388
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389
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390
  " </tr>\n",
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392
  " <th>4</th>\n",
393
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394
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  " </tr>\n",
398
  " </tbody>\n",
399
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400
  "</div>"
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  ],
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  "text/plain": [
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- " name seq \\\n",
404
- "0 2lbv MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE... \n",
405
- "1 1lt6 APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE... \n",
406
- "2 4lwi VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE... \n",
407
- "3 6oyz VQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF... \n",
408
- "4 4i11 GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA... \n",
409
- "\n",
410
- " smiles \\\n",
411
- "0 CCCCCCCCCCCCCCCCCCCC(=O)O \n",
412
- "1 O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1 \n",
413
- "2 COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2... \n",
414
- "3 COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC... \n",
415
- "4 CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1 \n",
416
- "\n",
417
- " contacts \n",
418
- "0 [1043, 2569, 2570, 2573, 2575, 6121, 6122, 612... \n",
419
- "1 [513, 517, 519, 520, 521, 522, 524, 525, 545, ... \n",
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- "2 [520, 522, 525, 541, 543, 545, 546, 547, 1038,... \n",
421
- "3 [35195, 35197, 35199, 35201, 35205, 35210, 352... \n",
422
- "4 [36231, 36232, 36234, 36235, 36236, 36237, 362... "
423
  ]
424
  },
425
- "execution_count": 20,
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  "metadata": {},
427
  "output_type": "execute_result"
428
  }
429
  ],
430
  "source": [
431
- "partitions[0].compute().head()"
432
  ]
433
  },
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  {
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  "cell_type": "code",
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- "execution_count": 21,
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  "id": "4982c3b1-5ce9-4f17-9834-a02c4e136bc2",
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  "metadata": {},
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  "outputs": [],
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  "source": [
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- "ddf = dd.from_delayed(partitions)"
442
  ]
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 22,
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  "id": "f6cdee43-33c6-445c-8619-ace20f90638c",
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  "metadata": {},
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  "outputs": [],
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  "source": [
451
- "ddf_all = ddf.merge(df_filter, on='name').drop(columns=['affinity'])"
 
 
 
 
 
 
 
452
  ]
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  },
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  "outputs": [
@@ -461,8 +521,8 @@
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- "CPU times: user 2min 8s, sys: 3min 26s, total: 5min 35s\n",
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- "Wall time: 2min 12s\n"
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  ]
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  }
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  ],
@@ -473,7 +533,7 @@
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@@ -516,206 +576,6 @@
516
  "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)"
517
  ]
518
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519
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- "outputs": [],
525
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526
- "df_all = df_complex.merge(df_filter,on='name').drop('affinity',axis=1)"
527
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556
- " <th>name</th>\n",
557
- " <th>seq</th>\n",
558
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559
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560
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561
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566
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- " <td>0.026</td>\n",
570
- " <td>Kd</td>\n",
571
- " </tr>\n",
572
- " <tr>\n",
573
- " <th>1</th>\n",
574
- " <td>1lt6</td>\n",
575
- " <td>APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE...</td>\n",
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579
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581
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582
- " <td>4lwi</td>\n",
583
- " <td>VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE...</td>\n",
584
- " <td>COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2...</td>\n",
585
- " <td>0.023</td>\n",
586
- " <td>IC50</td>\n",
587
- " </tr>\n",
588
- " <tr>\n",
589
- " <th>3</th>\n",
590
- " <td>6oyz</td>\n",
591
- " <td>VQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF...</td>\n",
592
- " <td>COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC...</td>\n",
593
- " <td>0.185</td>\n",
594
- " <td>IC50</td>\n",
595
- " </tr>\n",
596
- " <tr>\n",
597
- " <th>4</th>\n",
598
- " <td>4i11</td>\n",
599
- " <td>GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA...</td>\n",
600
- " <td>CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1</td>\n",
601
- " <td>27.200</td>\n",
602
- " <td>IC50</td>\n",
603
- " </tr>\n",
604
- " <tr>\n",
605
- " <th>...</th>\n",
606
- " <td>...</td>\n",
607
- " <td>...</td>\n",
608
- " <td>...</td>\n",
609
- " <td>...</td>\n",
610
- " <td>...</td>\n",
611
- " </tr>\n",
612
- " <tr>\n",
613
- " <th>20822</th>\n",
614
- " <td>2bok</td>\n",
615
- " <td>IVGGQECKDGECPWQALLINEENEGFCGGTILSEFYILTAAHCLYQ...</td>\n",
616
- " <td>C[N+](C)(C)CCCN1C(=O)C2C(C1=O)C(c1ccc(C(=N)N)c...</td>\n",
617
- " <td>0.280</td>\n",
618
- " <td>Ki</td>\n",
619
- " </tr>\n",
620
- " <tr>\n",
621
- " <th>20823</th>\n",
622
- " <td>4j46</td>\n",
623
- " <td>GTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGD...</td>\n",
624
- " <td>CCC(C)C(NC(=O)C1CCCN1C(=O)C(NC(=O)C(C)[NH3+])C...</td>\n",
625
- " <td>5.240</td>\n",
626
- " <td>Ki</td>\n",
627
- " </tr>\n",
628
- " <tr>\n",
629
- " <th>20824</th>\n",
630
- " <td>4j46</td>\n",
631
- " <td>GTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGD...</td>\n",
632
- " <td>CCC(C)C(NC(=O)C1CCCN1C(=O)C(NC(=O)C(C)[NH3+])C...</td>\n",
633
- " <td>5.240</td>\n",
634
- " <td>Ki</td>\n",
635
- " </tr>\n",
636
- " <tr>\n",
637
- " <th>20825</th>\n",
638
- " <td>2c80</td>\n",
639
- " <td>DHIKVIYFNGRGRAESIRMTLVAAGVNYEDERISFQDWPKIKPTIP...</td>\n",
640
- " <td>CCCCCCSCC(NC(=O)CCC([NH3+])C(=O)O)C(=O)NCC(=O)O</td>\n",
641
- " <td>4.700</td>\n",
642
- " <td>Kd</td>\n",
643
- " </tr>\n",
644
- " <tr>\n",
645
- " <th>20826</th>\n",
646
- " <td>2c80</td>\n",
647
- " <td>DHIKVIYFNGRGRAESIRMTLVAAGVNYEDERISFQDWPKIKPTIP...</td>\n",
648
- " <td>CCCCCCSCC(NC(=O)CCC([NH3+])C(=O)O)C(=O)NCC(=O)O</td>\n",
649
- " <td>4.700</td>\n",
650
- " <td>Kd</td>\n",
651
- " </tr>\n",
652
- " </tbody>\n",
653
- "</table>\n",
654
- "<p>20827 rows × 5 columns</p>\n",
655
- "</div>"
656
- ],
657
- "text/plain": [
658
- " name seq \\\n",
659
- "0 2lbv MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE... \n",
660
- "1 1lt6 APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE... \n",
661
- "2 4lwi VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE... \n",
662
- "3 6oyz VQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF... \n",
663
- "4 4i11 GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA... \n",
664
- "... ... ... \n",
665
- "20822 2bok IVGGQECKDGECPWQALLINEENEGFCGGTILSEFYILTAAHCLYQ... \n",
666
- "20823 4j46 GTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGD... \n",
667
- "20824 4j46 GTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGD... \n",
668
- "20825 2c80 DHIKVIYFNGRGRAESIRMTLVAAGVNYEDERISFQDWPKIKPTIP... \n",
669
- "20826 2c80 DHIKVIYFNGRGRAESIRMTLVAAGVNYEDERISFQDWPKIKPTIP... \n",
670
- "\n",
671
- " smiles affinity_uM \\\n",
672
- "0 CCCCCCCCCCCCCCCCCCCC(=O)O 0.026 \n",
673
- "1 O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1 500.000 \n",
674
- "2 COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2... 0.023 \n",
675
- "3 COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC... 0.185 \n",
676
- "4 CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1 27.200 \n",
677
- "... ... ... \n",
678
- "20822 C[N+](C)(C)CCCN1C(=O)C2C(C1=O)C(c1ccc(C(=N)N)c... 0.280 \n",
679
- "20823 CCC(C)C(NC(=O)C1CCCN1C(=O)C(NC(=O)C(C)[NH3+])C... 5.240 \n",
680
- "20824 CCC(C)C(NC(=O)C1CCCN1C(=O)C(NC(=O)C(C)[NH3+])C... 5.240 \n",
681
- "20825 CCCCCCSCC(NC(=O)CCC([NH3+])C(=O)O)C(=O)NCC(=O)O 4.700 \n",
682
- "20826 CCCCCCSCC(NC(=O)CCC([NH3+])C(=O)O)C(=O)NCC(=O)O 4.700 \n",
683
- "\n",
684
- " affinity_quantity \n",
685
- "0 Kd \n",
686
- "1 IC50 \n",
687
- "2 IC50 \n",
688
- "3 IC50 \n",
689
- "4 IC50 \n",
690
- "... ... \n",
691
- "20822 Ki \n",
692
- "20823 Ki \n",
693
- "20824 Ki \n",
694
- "20825 Kd \n",
695
- "20826 Kd \n",
696
- "\n",
697
- "[20827 rows x 5 columns]"
698
- ]
699
- },
700
- "execution_count": 128,
701
- "metadata": {},
702
- "output_type": "execute_result"
703
- }
704
- ],
705
- "source": [
706
- "df_all"
707
- ]
708
- },
709
- {
710
- "cell_type": "code",
711
- "execution_count": 14,
712
- "id": "8f75499c-8895-4395-867e-7d9a9d394910",
713
- "metadata": {},
714
- "outputs": [],
715
- "source": [
716
- "df_all.to_parquet('data/pdbbind.parquet')"
717
- ]
718
- },
719
  {
720
  "cell_type": "code",
721
  "execution_count": null,
 
123
  {
124
  "cell_type": "code",
125
  "execution_count": 8,
126
+ "id": "802cb9bc-2563-4d7f-9a76-3be2d9263a36",
127
  "metadata": {},
128
  "outputs": [],
129
  "source": [
130
+ "cutoffs = [5,8,11]"
131
  ]
132
  },
133
  {
134
  "cell_type": "code",
135
  "execution_count": 9,
136
+ "id": "d6dda488-f709-4fe7-b372-080042cf7c66",
137
  "metadata": {},
138
  "outputs": [],
139
  "source": [
140
+ "dfs_complex = {c: pd.read_parquet('data/pdbbind_complex_{}.parquet'.format(c)) for c in cutoffs}"
 
 
 
 
 
141
  ]
142
  },
143
  {
144
  "cell_type": "code",
145
  "execution_count": 10,
146
+ "id": "aebc6791-5bb0-4828-9697-79bc243f8992",
 
 
 
 
 
 
 
 
 
 
 
147
  "metadata": {},
148
  "outputs": [
149
  {
 
175
  " <tbody>\n",
176
  " <tr>\n",
177
  " <th>0</th>\n",
178
+ " <td>10gs</td>\n",
179
+ " <td>PYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKASC...</td>\n",
180
+ " <td>[NH3+]C(CCC(=O)NC(CSCc1ccccc1)C(=O)NC(C(=O)O)c...</td>\n",
181
  " </tr>\n",
182
  " <tr>\n",
183
  " <th>1</th>\n",
184
+ " <td>184l</td>\n",
185
+ " <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL...</td>\n",
186
+ " <td>CC(C)Cc1ccccc1</td>\n",
187
  " </tr>\n",
188
  " <tr>\n",
189
  " <th>2</th>\n",
190
+ " <td>186l</td>\n",
191
+ " <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL...</td>\n",
192
+ " <td>CCCCc1ccccc1</td>\n",
193
  " </tr>\n",
194
  " <tr>\n",
195
  " <th>3</th>\n",
196
+ " <td>187l</td>\n",
197
+ " <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL...</td>\n",
198
+ " <td>Cc1ccc(C)cc1</td>\n",
199
  " </tr>\n",
200
  " <tr>\n",
201
  " <th>4</th>\n",
202
+ " <td>188l</td>\n",
203
+ " <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL...</td>\n",
204
+ " <td>Cc1ccccc1C</td>\n",
205
  " </tr>\n",
206
  " </tbody>\n",
207
  "</table>\n",
 
209
  ],
210
  "text/plain": [
211
  " name seq \\\n",
212
+ "0 10gs PYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKASC... \n",
213
+ "1 184l MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL... \n",
214
+ "2 186l MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL... \n",
215
+ "3 187l MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL... \n",
216
+ "4 188l MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL... \n",
217
  "\n",
218
  " smiles \n",
219
+ "0 [NH3+]C(CCC(=O)NC(CSCc1ccccc1)C(=O)NC(C(=O)O)c... \n",
220
+ "1 CC(C)Cc1ccccc1 \n",
221
+ "2 CCCCc1ccccc1 \n",
222
+ "3 Cc1ccc(C)cc1 \n",
223
+ "4 Cc1ccccc1C "
224
  ]
225
  },
226
+ "execution_count": 10,
227
  "metadata": {},
228
  "output_type": "execute_result"
229
  }
230
  ],
231
  "source": [
232
+ "list(dfs_complex.values())[0].head()"
233
+ ]
234
+ },
235
+ {
236
+ "cell_type": "code",
237
+ "execution_count": 11,
238
+ "id": "ed3fe035-6035-4d39-b072-d12dc0a95857",
239
+ "metadata": {},
240
+ "outputs": [],
241
+ "source": [
242
+ "import dask.array as da\n",
243
+ "import dask.dataframe as dd\n",
244
+ "from dask.bag import from_delayed\n",
245
+ "from dask import delayed\n",
246
+ "import pyarrow as pa\n",
247
+ "import pyarrow.parquet as pq"
248
  ]
249
  },
250
  {
251
  "cell_type": "code",
252
  "execution_count": 12,
253
+ "id": "cd26125b-e68b-4fa3-846e-2b6e7f635fe0",
254
+ "metadata": {},
255
+ "outputs": [],
256
+ "source": [
257
+ "contacts_dask = [da.from_npy_stack('data/pdbbind_contacts_{}'.format(c)) for c in cutoffs]\n",
258
+ "contacts_dask = [c.reshape(-1,c.shape[-2]*c.shape[-1]) for c in contacts_dask]"
259
+ ]
260
+ },
261
+ {
262
+ "cell_type": "code",
263
+ "execution_count": 13,
264
  "id": "9c7c9849-2345-4baf-89e7-d412f52353b6",
265
  "metadata": {},
266
  "outputs": [
 
308
  "dask.array<blocks, shape=(438, 1043460), dtype=float32, chunksize=(438, 1043460), chunktype=numpy.ndarray>"
309
  ]
310
  },
311
+ "execution_count": 13,
312
  "metadata": {},
313
  "output_type": "execute_result"
314
  }
315
  ],
316
  "source": [
317
+ "contacts_dask[0].blocks[1]"
318
  ]
319
  },
320
  {
321
  "cell_type": "code",
322
+ "execution_count": 14,
323
+ "id": "0bd8e9b9-9713-4572-bd7f-dc47da9fce91",
324
+ "metadata": {},
325
+ "outputs": [
326
+ {
327
+ "data": {
328
+ "text/plain": [
329
+ "[16206, 16181, 16172]"
330
+ ]
331
+ },
332
+ "execution_count": 14,
333
+ "metadata": {},
334
+ "output_type": "execute_result"
335
+ }
336
+ ],
337
+ "source": [
338
+ "[len(c) for c in contacts_dask]"
339
+ ]
340
+ },
341
+ {
342
+ "cell_type": "code",
343
+ "execution_count": 15,
344
+ "id": "87493934-3839-476a-a975-7da057c320da",
345
+ "metadata": {},
346
+ "outputs": [
347
+ {
348
+ "data": {
349
+ "text/plain": [
350
+ "16206"
351
+ ]
352
+ },
353
+ "execution_count": 15,
354
+ "metadata": {},
355
+ "output_type": "execute_result"
356
+ }
357
+ ],
358
+ "source": [
359
+ "contacts_dask[0].shape[0]"
360
+ ]
361
+ },
362
+ {
363
+ "cell_type": "code",
364
+ "execution_count": 16,
365
+ "id": "1f7815ec-cddf-4bae-b72c-89cdf56cc1f9",
366
+ "metadata": {},
367
+ "outputs": [
368
+ {
369
+ "data": {
370
+ "text/plain": [
371
+ "name 9lpr\n",
372
+ "seq ANIVGGIEYSINNASLCSVGFSVTRGATKGFVTAGHCGTVNATARI...\n",
373
+ "smiles CC(C)CC(NC(=O)C1CCCN1C(=O)C(C)NC(=O)C(C)[NH3+]...\n",
374
+ "Name: 16205, dtype: object"
375
+ ]
376
+ },
377
+ "execution_count": 16,
378
+ "metadata": {},
379
+ "output_type": "execute_result"
380
+ }
381
+ ],
382
+ "source": [
383
+ "dfs_complex[5].iloc[16205]"
384
+ ]
385
+ },
386
+ {
387
+ "cell_type": "code",
388
+ "execution_count": 17,
389
  "id": "42e95d84-ef27-4417-9479-8b356462b8c3",
390
  "metadata": {},
391
  "outputs": [],
392
  "source": [
393
  "import numpy as np\n",
394
+ "all_partitions = []\n",
395
+ "for c, cutoff in zip(contacts_dask,cutoffs):\n",
396
+ " def chunk_to_sparse(rcut, chunk, idx_chunk):\n",
397
+ " res = dfs_complex[rcut].iloc[idx_chunk][['name']].copy()\n",
398
+ " # pad to account for [CLS] and [SEP]\n",
399
+ " res['contacts_{}A'.format(rcut)] = [np.where(np.pad(a,pad_width=(1,1)))[0] for a in chunk]\n",
400
+ " return res\n",
401
  "\n",
402
+ " partitions = [delayed(chunk_to_sparse)(cutoff,b,k)\n",
403
+ " for b,k in zip(c.blocks, da.arange(c.shape[0],chunks=c.chunks[0:1]).blocks)\n",
404
+ " ]\n",
405
+ " all_partitions.append(partitions)"
406
  ]
407
  },
408
  {
409
  "cell_type": "code",
410
+ "execution_count": 18,
411
  "id": "5520a925-693f-43f0-9e76-df2e128f272e",
412
  "metadata": {},
413
  "outputs": [
 
433
  " <tr style=\"text-align: right;\">\n",
434
  " <th></th>\n",
435
  " <th>name</th>\n",
436
+ " <th>contacts_5A</th>\n",
 
 
437
  " </tr>\n",
438
  " </thead>\n",
439
  " <tbody>\n",
440
  " <tr>\n",
441
  " <th>0</th>\n",
442
+ " <td>10gs</td>\n",
443
+ " <td>[1021, 1022, 1070, 1073, 1075, 3071, 3072, 307...</td>\n",
 
 
444
  " </tr>\n",
445
  " <tr>\n",
446
  " <th>1</th>\n",
447
+ " <td>184l</td>\n",
448
+ " <td>[39279, 39280, 39281, 39282, 42332, 42334, 423...</td>\n",
 
 
449
  " </tr>\n",
450
  " <tr>\n",
451
  " <th>2</th>\n",
452
+ " <td>186l</td>\n",
453
+ " <td>[39277, 39278, 39279, 42332, 42333, 42334, 423...</td>\n",
 
 
454
  " </tr>\n",
455
  " <tr>\n",
456
  " <th>3</th>\n",
457
+ " <td>187l</td>\n",
458
+ " <td>[39271, 39272, 39281, 42331, 42332, 42334, 423...</td>\n",
 
 
459
  " </tr>\n",
460
  " <tr>\n",
461
  " <th>4</th>\n",
462
+ " <td>188l</td>\n",
463
+ " <td>[39271, 39272, 39274, 39275, 42331, 42332, 423...</td>\n",
 
 
464
  " </tr>\n",
465
  " </tbody>\n",
466
  "</table>\n",
467
  "</div>"
468
  ],
469
  "text/plain": [
470
+ " name contacts_5A\n",
471
+ "0 10gs [1021, 1022, 1070, 1073, 1075, 3071, 3072, 307...\n",
472
+ "1 184l [39279, 39280, 39281, 39282, 42332, 42334, 423...\n",
473
+ "2 186l [39277, 39278, 39279, 42332, 42333, 42334, 423...\n",
474
+ "3 187l [39271, 39272, 39281, 42331, 42332, 42334, 423...\n",
475
+ "4 188l [39271, 39272, 39274, 39275, 42331, 42332, 423..."
 
 
 
 
 
 
 
 
 
 
 
 
 
 
476
  ]
477
  },
478
+ "execution_count": 18,
479
  "metadata": {},
480
  "output_type": "execute_result"
481
  }
482
  ],
483
  "source": [
484
+ "all_partitions[0][0].compute().head()"
485
  ]
486
  },
487
  {
488
  "cell_type": "code",
489
+ "execution_count": 19,
490
  "id": "4982c3b1-5ce9-4f17-9834-a02c4e136bc2",
491
  "metadata": {},
492
  "outputs": [],
493
  "source": [
494
+ "ddfs = [dd.from_delayed(p) for p in all_partitions]"
495
  ]
496
  },
497
  {
498
  "cell_type": "code",
499
+ "execution_count": 20,
500
  "id": "f6cdee43-33c6-445c-8619-ace20f90638c",
501
  "metadata": {},
502
  "outputs": [],
503
  "source": [
504
+ "ddf_all = None\n",
505
+ "for d in ddfs:\n",
506
+ " if ddf_all is not None:\n",
507
+ " ddf_all = ddf_all.merge(d, on='name')\n",
508
+ " else:\n",
509
+ " ddf_all = d\n",
510
+ "ddf_all = ddf_all.merge(df_filter,on='name')\n",
511
+ "ddf_all = ddf_all.merge(list(dfs_complex.values())[0],on='name')"
512
  ]
513
  },
514
  {
515
  "cell_type": "code",
516
+ "execution_count": 21,
517
  "id": "8f49f871-76f6-4fb2-b2db-c0794d4c07bf",
518
  "metadata": {},
519
  "outputs": [
 
521
  "name": "stdout",
522
  "output_type": "stream",
523
  "text": [
524
+ "CPU times: user 6min 9s, sys: 12min 7s, total: 18min 17s\n",
525
+ "Wall time: 7min 36s\n"
526
  ]
527
  }
528
  ],
 
533
  },
534
  {
535
  "cell_type": "code",
536
+ "execution_count": 22,
537
  "id": "45e4b4fa-6338-4abe-bd6e-8aea46e2a09c",
538
  "metadata": {},
539
  "outputs": [],
 
543
  },
544
  {
545
  "cell_type": "code",
546
+ "execution_count": 23,
547
  "id": "7c3db301-6565-4053-bbd4-139bb41dd1c4",
548
  "metadata": {},
549
  "outputs": [
550
  {
551
  "data": {
552
  "text/plain": [
553
+ "(array([6.35008182]), array([3.56554195]))"
554
  ]
555
  },
556
+ "execution_count": 23,
557
  "metadata": {},
558
  "output_type": "execute_result"
559
  }
 
567
  },
568
  {
569
  "cell_type": "code",
570
+ "execution_count": 24,
571
  "id": "c9d674bb-d6a2-4810-aa2b-e3bc3b4bbc98",
572
  "metadata": {},
573
  "outputs": [],
 
576
  "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)"
577
  ]
578
  },
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
579
  {
580
  "cell_type": "code",
581
  "execution_count": null,
pdbbind.py CHANGED
@@ -90,6 +90,7 @@ if __name__ == '__main__':
90
 
91
  filenames = glob.glob('data/pdbbind/v2020-other-PL/*')
92
  filenames.extend(glob.glob('data/pdbbind/refined-set/*'))
 
93
  comm = MPI.COMM_WORLD
94
  with MPICommExecutor(comm, root=0) as executor:
95
  if executor is not None:
@@ -103,5 +104,5 @@ if __name__ == '__main__':
103
  import pandas as pd
104
  df = pd.DataFrame({'name': names, 'seq': seqs, 'smiles': all_smiles})
105
  all_contacts = da.from_array(all_contacts, chunks=chunk_size)
106
- da.to_npy_stack('data/pdbbind_contacts/', all_contacts)
107
- df.to_parquet('data/pdbbind_complex.parquet')
 
90
 
91
  filenames = glob.glob('data/pdbbind/v2020-other-PL/*')
92
  filenames.extend(glob.glob('data/pdbbind/refined-set/*'))
93
+ filenames = sorted(filenames)
94
  comm = MPI.COMM_WORLD
95
  with MPICommExecutor(comm, root=0) as executor:
96
  if executor is not None:
 
104
  import pandas as pd
105
  df = pd.DataFrame({'name': names, 'seq': seqs, 'smiles': all_smiles})
106
  all_contacts = da.from_array(all_contacts, chunks=chunk_size)
107
+ da.to_npy_stack('data/pdbbind_contacts_{}/'.format(cutoff), all_contacts)
108
+ df.to_parquet('data/pdbbind_complex_{}.parquet'.format(cutoff))
protein_ligand_contacts.py CHANGED
@@ -78,7 +78,9 @@ class ProteinLigandContacts(datasets.ArrowBasedBuilder):
78
  "affinity_uM": datasets.Value("float"),
79
  "neg_log10_affinity_M": datasets.Value("float"),
80
  "affinity": datasets.Value("float"),
81
- "contacts": datasets.Sequence(datasets.Value('int64')),
 
 
82
  # These are the features of your dataset like images, labels ...
83
  }
84
  )
 
78
  "affinity_uM": datasets.Value("float"),
79
  "neg_log10_affinity_M": datasets.Value("float"),
80
  "affinity": datasets.Value("float"),
81
+ "contacts_5A": datasets.Sequence(datasets.Value('int64')),
82
+ "contacts_8A": datasets.Sequence(datasets.Value('int64')),
83
+ "contacts_11A": datasets.Sequence(datasets.Value('int64')),
84
  # These are the features of your dataset like images, labels ...
85
  }
86
  )