fix indexing for [CLS] and [SEP]

README.md

@@ -10,7 +10,8 @@ tags:
 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.
+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.
 

data/pdbbind_with_contacts.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ddad766228c134cc220959f62e9e95bce250083963079386be1d23e7eae78f7d
-size 16022320
+oid sha256:46dfc8d5f369cfe05c12955b45e15b3d8991014d01e0d5f28530b0bb0c6a6eb3
+size 16075354

pdbbind.ipynb

@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "9b2be11c-f4bb-4107-af49-abd78052afcf",
    "metadata": {},
    "outputs": [],
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "68983ab8-bf11-4ed6-ba06-f962dbdc077e",
    "metadata": {},
    "outputs": [],
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "3acbca3c-9c0b-43a1-a45e-331bf153bcfa",
    "metadata": {},
    "outputs": [],
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "58e5748b-2cea-43ff-ab51-85a5021bd50b",
    "metadata": {},
    "outputs": [],
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "d92f0004-68c1-4487-94b9-56b4fd598de4",
    "metadata": {},
    "outputs": [
@@ -88,7 +88,7 @@
        "<AxesSubplot:>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 7,
    "id": "aa358835-55f3-4551-9217-e76a15de4fe8",
    "metadata": {},
    "outputs": [],
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "id": "d6dda488-f709-4fe7-b372-080042cf7c66",
    "metadata": {},
    "outputs": [],
@@ -132,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "id": "ed3fe035-6035-4d39-b072-d12dc0a95857",
    "metadata": {},
    "outputs": [],
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "id": "cd26125b-e68b-4fa3-846e-2b6e7f635fe0",
    "metadata": {},
    "outputs": [],
@@ -158,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "id": "0e773f9d-6555-49c0-b0af-202c5e19b2cc",
    "metadata": {},
    "outputs": [
@@ -239,7 +239,7 @@
        "4           CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -250,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "id": "9c7c9849-2345-4baf-89e7-d412f52353b6",
    "metadata": {},
    "outputs": [
@@ -265,8 +265,8 @@
        "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
-       "    <tr><th> Bytes </th><td> 1.71 GiB </td> <td> 1.71 GiB </td></tr>\n",
-       "    <tr><th> Shape </th><td> (438, 1048576) </td> <td> (438, 1048576) </td></tr>\n",
+       "    <tr><th> Bytes </th><td> 1.70 GiB </td> <td> 1.70 GiB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (438, 1043460) </td> <td> (438, 1043460) </td></tr>\n",
        "    <tr><th> Count </th><td> 75 Tasks </td><td> 1 Chunks </td></tr>\n",
        "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
        "  </tbody>\n",
@@ -287,7 +287,7 @@
        "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1048576</text>\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1043460</text>\n",
        "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,12.706308)\">438</text>\n",
        "</svg>\n",
        "</td>\n",
@@ -295,10 +295,10 @@
        "</table>"
       ],
       "text/plain": [
-       "dask.array<blocks, shape=(438, 1048576), dtype=float32, chunksize=(438, 1048576), chunktype=numpy.ndarray>"
+       "dask.array<blocks, shape=(438, 1043460), dtype=float32, chunksize=(438, 1043460), chunktype=numpy.ndarray>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -309,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "id": "42e95d84-ef27-4417-9479-8b356462b8c3",
    "metadata": {},
    "outputs": [],
@@ -317,7 +317,8 @@
     "import numpy as np\n",
     "def chunk_to_sparse(chunk, idx_chunk):\n",
     "    res = df_complex.iloc[idx_chunk].copy()\n",
-    "    res['contacts'] = [np.where(a)[0] for a in chunk]\n",
+    "    # pad to account for [CLS] and [SEP]\n",
+    "    res['contacts'] = [np.where(np.pad(a,pad_width=(1,1)))[0] for a in chunk]\n",
     "    return res\n",
     "\n",
     "partitions = [delayed(chunk_to_sparse)(b,k)\n",
@@ -364,35 +365,35 @@
        "      <td>2lbv</td>\n",
        "      <td>MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE...</td>\n",
        "      <td>CCCCCCCCCCCCCCCCCCCC(=O)O</td>\n",
-       "      <td>[1046, 2578, 2579, 2582, 2584, 6144, 6145, 614...</td>\n",
+       "      <td>[1043, 2569, 2570, 2573, 2575, 6121, 6122, 612...</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",
+       "      <td>[513, 517, 519, 520, 521, 522, 524, 525, 545, ...</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",
+       "      <td>[520, 522, 525, 541, 543, 545, 546, 547, 1038,...</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, 353...</td>\n",
+       "      <td>[35195, 35197, 35199, 35201, 35205, 35210, 352...</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, 363...</td>\n",
+       "      <td>[36231, 36232, 36234, 36235, 36236, 36237, 362...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -414,11 +415,11 @@
        "4           CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1   \n",
        "\n",
        "                                            contacts  \n",
-       "0  [1046, 2578, 2579, 2582, 2584, 6144, 6145, 614...  \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, 353...  \n",
-       "4  [36372, 36373, 36375, 36376, 36377, 36378, 363...  "
+       "0  [1043, 2569, 2570, 2573, 2575, 6121, 6122, 612...  \n",
+       "1  [513, 517, 519, 520, 521, 522, 524, 525, 545, ...  \n",
+       "2  [520, 522, 525, 541, 543, 545, 546, 547, 1038,...  \n",
+       "3  [35195, 35197, 35199, 35201, 35205, 35210, 352...  \n",
+       "4  [36231, 36232, 36234, 36235, 36236, 36237, 362...  "
       ]
      },
      "execution_count": 20,
@@ -442,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 22,
    "id": "f6cdee43-33c6-445c-8619-ace20f90638c",
    "metadata": {},
    "outputs": [],
@@ -452,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 23,
    "id": "8f49f871-76f6-4fb2-b2db-c0794d4c07bf",
    "metadata": {},
    "outputs": [
@@ -460,8 +461,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1min 42s, sys: 3min 24s, total: 5min 6s\n",
-      "Wall time: 28.9 s\n"
+      "CPU times: user 2min 8s, sys: 3min 26s, total: 5min 35s\n",
+      "Wall time: 2min 12s\n"
      ]
     }
    ],
@@ -472,7 +473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 24,
    "id": "45e4b4fa-6338-4abe-bd6e-8aea46e2a09c",
    "metadata": {},
    "outputs": [],
@@ -482,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 25,
    "id": "7c3db301-6565-4053-bbd4-139bb41dd1c4",
    "metadata": {},
    "outputs": [
@@ -492,7 +493,7 @@
        "(array([6.3455065]), array([3.57430038]))"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -506,11 +507,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 26,
    "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)"
    ]
   },

pdbbind.py

@@ -21,8 +21,8 @@ punctuation_regex  = r"""(\(|\)|\.|=|#|-|\+|\\|\/|:|~|@|\?|>>?|\*|\$|\%[0-9]{2}|
 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
+max_seq = 2046 # = 2048 - 2 (accounting for [CLS] and [SEP])
+max_smiles = 510 # = 512 - 2
 chunk_size = '1G'
 
 def parse_complex(fn):
@@ -48,7 +48,6 @@ def parse_complex(fn):
         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