add protein datasets

Author: Oxer11

torchdrug/datasets/__init__.py

@@ -20,9 +20,27 @@
 from .zinc2m import ZINC2m
 from .pcqm4m import PCQM4M
 from .pubchem110m import PubChem110m
-
 from .chembl_filtered import ChEMBLFiltered
 
+from .beta_lactamase import BetaLactamase
+from .fluorescence import Fluorescence
+from .stability import Stability
+from .solubility import Solubility
+from .fold import Fold
+from .binary_localization import BinaryLocalization
+from .subcellular_localization import SubcellularLocalization
+from .secondary_structure import SecondaryStructure
+from .human_ppi import HumanPPI
+from .yeast_ppi import YeastPPI
+from .ppi_affinity import PPIAffinity
+from .bindingdb import BindingDB
+from .pdbbind import PDBBind
+from .proteinnet import ProteinNet
+
+from .enzyme_commission import EnzymeCommission
+from .gene_ontology import GeneOntology
+from .alphafolddb import AlphaFoldDB
+
 from .fb15k import FB15k, FB15k237
 from .wn18 import WN18, WN18RR
 from .hetionet import Hetionet
@@ -32,10 +50,14 @@
 from .pubmed import PubMed
 
 __all__ = [
-    "BACE", "BBBP", "CEP", "ChEMBLFiltered", "ClinTox", "Delaney", "FreeSolv", "HIV", "Lipophilicity",
+    "BACE", "BBBP", "CEP", "ClinTox", "Delaney", "FreeSolv", "HIV", "Lipophilicity",
     "Malaria", "MOSES", "MUV", "OPV", "QM8", "QM9", "SIDER", "Tox21", "ToxCast",
     "USPTO50k", "ZINC250k",
-    "ZINC2m", "PCQM4M", "PubChem110m",
+    "ZINC2m", "PCQM4M", "PubChem110m", "ChEMBLFiltered",
+    "EnzymeCommission", "GeneOntology", "AlphaFoldDB",
+    "BetaLactamase", "Fluorescence", "Stability", "Solubility", "Fold", 
+    "BinaryLocalization", "SubcellularLocalization", "SecondaryStructure",
+    "HumanPPI", "YeastPPI", "PPIAffinity", "BindingDB", "PDBBind", "ProteinNet",
     "FB15k", "FB15k237", "WN18", "WN18RR", "Hetionet",
-    "Cora", "CiteSeer",
+    "Cora", "CiteSeer", "PubMed",
 ]

torchdrug/datasets/alphafolddb.py

@@ -0,0 +1,150 @@
+import os
+import glob
+
+from torchdrug import data, utils
+from torchdrug.core import Registry as R
+
+
+@R.register("datasets.AlphaFoldDB")
+@utils.copy_args(data.ProteinDataset.load_pdbs, ignore=("filtered_pdb",))
+class AlphaFoldDB(data.ProteinDataset):
+    """
+    3D protein structures predicted by AlphaFold.
+    This dataset covers proteomes of 48 organisms, as well as the majority of Swiss-Prot.
+
+    Statistics:
+        See https://alphafold.ebi.ac.uk/download
+
+    Parameters:
+        path (str): path to store the dataset
+        species_id (int, optional): the id of species to be loaded. The species are numbered
+            by the order appeared on https://alphafold.ebi.ac.uk/download (0-20 for model 
+            organism proteomes, 21 for Swiss-Prot)
+        split_id (int, optional): the id of split to be loaded. To avoid large memory consumption 
+            for one dataset, we have cut each species into several splits, each of which contains 
+            at most 22000 proteins.
+        verbose (int, optional): output verbose level
+        **kwargs
+    """
+
+    urls = [
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000006548_3702_ARATH_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000001940_6239_CAEEL_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000000559_237561_CANAL_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000000437_7955_DANRE_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000002195_44689_DICDI_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000000803_7227_DROME_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000000625_83333_ECOLI_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000008827_3847_SOYBN_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000005640_9606_HUMAN_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000008153_5671_LEIIN_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000000805_243232_METJA_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000000589_10090_MOUSE_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000001584_83332_MYCTU_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000059680_39947_ORYSJ_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000001450_36329_PLAF7_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000002494_10116_RAT_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000002311_559292_YEAST_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000002485_284812_SCHPO_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000008816_93061_STAA8_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000002296_353153_TRYCC_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000007305_4577_MAIZE_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/swissprot_pdb_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000001631_447093_AJECG_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000006672_6279_BRUMA_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000000799_192222_CAMJE_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000094526_86049_9EURO1_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000274756_318479_DRAME_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000325664_1352_ENTFC_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000053029_1442368_9EURO2_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000000579_71421_HAEIN_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000000429_85962_HELPY_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000007841_1125630_KLEPH_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000008153_5671_LEIIN_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000078237_100816_9PEZI1_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000000806_272631_MYCLE_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000001584_83332_MYCTU_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000020681_1299332_MYCUL_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000000535_242231_NEIG1_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000006304_1133849_9NOCA1_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000024404_6282_ONCVO_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000002059_502779_PARBA_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000001450_36329_PLAF7_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000002438_208964_PSEAE_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000001014_99287_SALTY_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000008854_6183_SCHMA_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000002716_300267_SHIDS_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000018087_1391915_SPOS1_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000008816_93061_STAA8_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000000586_171101_STRR6_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000035681_6248_STRER_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000030665_36087_TRITR_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000008524_185431_TRYB2_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000002296_353153_TRYCC_v2.tar",
+        "https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000270924_6293_WUCBA_v2.tar"
+    ]
+    md5s = [
+        "82b14d14404e39793cf73c1e0f083865", "9e26602ba2d9f233ef4fcf82703ddb59", 
+        "60a09db1e1c47a98763d09879784f536", "a0ab562b7372f149673c4518f949501f", 
+        "6205138b14fb7e7ec09b366e3e4f294b", "31f31359cd7254f82304e3886440bdd3", 
+        "a590096e65461ed4eb092b2147b97f0b", "8f1e120f372995644a7101ad58e5b2ae", 
+        "9a659c4aed2a8b833478dcd5fffc5fd8", "95d775f2ae271cf50a101c73335cd250", 
+        "e5b12da43f5bd77298ca50e19706bdeb", "90e953abba9c8fe202e0adf825c0dfcc", 
+        "38a11553c7e2d00482281e74f7daf321", "2bcdfe2c37154a355fe4e8150c279c13", 
+        "580a55e56a44fed935f0101c37a8c4ab", "b8d08a9033d111429fadb4e25820f9f7", 
+        "59d1167f414a86cbccfb204791fea0eb", "dfde6b44026f19a88f1abc8ac2798ce6", 
+        "a1c2047a16130d61cac4db23b2f5b560", "e4d4b72df8d075aeb607dcb095210304", 
+        "5cdad48c799ffd723636cae26433f1f9", "98a7c13987f578277bfb66ac48a1e242", 
+    ]
+    species_nsplit = [
+        2, 1, 1, 2, 1, 1, 1, 3, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 20,
+        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+        1, 1, 1, 1, 1, 1, 1, 1, 1, 1
+    ]
+    split_length = 22000
+
+    def __init__(self, path, species_id=0, split_id=0, verbose=1, **kwargs):
+        path = os.path.expanduser(path)
+        if not os.path.exists(path):
+            os.makedirs(path)
+        self.path = path
+
+        species_name = os.path.basename(self.urls[species_id])[:-4]
+        if split_id >= self.species_nsplit[species_id]:
+            raise ValueError("Split id %d should be less than %d in species %s" % 
+                            (split_id, self.species_nsplit[species_id], species_name))
+        self.processed_file = "%s_%d.pkl.gz" % (species_name, split_id)
+        pkl_file = os.path.join(path, self.processed_file)
+
+        if os.path.exists(pkl_file):
+            self.load_pickle(pkl_file, verbose=verbose, **kwargs)
+        else:
+            tar_file = utils.download(self.urls[species_id], path, md5=self.md5s[species_id])
+            pdb_path = utils.extract(tar_file)
+            gz_files = sorted(glob.glob(os.path.join(pdb_path, "*.pdb.gz")))
+            pdb_files = []
+            index = slice(split_id * self.split_length, (split_id + 1) * self.split_length)
+            for gz_file in gz_files[index]:
+                pdb_files.append(utils.extract(gz_file))
+            self.load_pdbs(pdb_files, verbose=verbose, **kwargs)
+            self.save_pickle(pkl_file, verbose=verbose)
+
+    def get_item(self, index):
+        if getattr(self, "lazy", False):
+            protein = data.Protein.from_pdb(self.pdb_files[index], self.kwargs)
+        else:
+            protein = self.data[index].clone()
+        # Zhaocheng: I didn't see any code that creates sparse residue features
+        if hasattr(protein, "residue_feature"):
+            with protein.residue():
+                protein.residue_feature = protein.residue_feature.to_dense()
+        item = {"graph": protein}
+        if self.transform:
+            item = self.transform(item)
+        return item
+
+    def __repr__(self):
+        lines = [
+            "#sample: %d" % len(self),
+        ]
+        return "%s(\n  %s\n)" % (self.__class__.__name__, "\n  ".join(lines))

torchdrug/datasets/beta_lactamase.py

@@ -0,0 +1,51 @@
+import os
+
+from torch.utils import data as torch_data
+
+from torchdrug import data, utils
+from torchdrug.core import Registry as R
+
+
+@R.register("datasets.BetaLactamase")
+@utils.copy_args(data.ProteinDataset.load_lmdbs, ignore=("target_fields",))
+class BetaLactamase(data.ProteinDataset):
+    """
+    The activity values of first-order mutants of the TEM-1 beta-lactamase protein.
+
+    Statistics:
+        - #Train: 4,158
+        - #Valid: 520
+        - #Test: 520
+
+    Parameters:
+        path (str): the path to store the dataset
+        verbose (int, optional): output verbose level
+        **kwargs
+    """
+
+    url = "https://miladeepgraphlearningproteindata.s3.us-east-2.amazonaws.com/peerdata/beta_lactamase.tar.gz"
+    md5 = "65766a3969cc0e94b101d4063d204ba4"
+    splits = ["train", "valid", "test"]
+    target_fields = ["scaled_effect1"]
+
+    def __init__(self, path, verbose=1, **kwargs):
+        path = os.path.expanduser(path)
+        if not os.path.exists(path):
+            os.makedirs(path)
+        self.path = path
+
+        zip_file = utils.download(self.url, path, md5=self.md5)
+        data_path = utils.extract(zip_file)
+        lmdb_files = [os.path.join(data_path, "beta_lactamase/beta_lactamase_%s.lmdb" % split)
+                      for split in self.splits]
+
+        self.load_lmdbs(lmdb_files, target_fields=self.target_fields, verbose=verbose, **kwargs)
+
+    def split(self):
+        offset = 0
+        splits = []
+        for num_sample in self.num_samples:
+            split = torch_data.Subset(self, range(offset, offset + num_sample))
+            splits.append(split)
+            offset += num_sample
+        return splits

torchdrug/datasets/binary_localization.py

@@ -0,0 +1,52 @@
+import os
+
+from torch.utils import data as torch_data
+
+from torchdrug import data, utils
+from torchdrug.core import Registry as R
+
+
+@R.register("datasets.BinaryLocalization")
+@utils.copy_args(data.ProteinDataset.load_lmdbs, ignore=("target_fields",))
+class BinaryLocalization(data.ProteinDataset):
+    """
+    Simpler version of the Subcellular Localization with binary labels indicating
+    whether a protein is membrane-bound or soluble.
+
+    Statistics:
+        - #Train: 5,161
+        - #Valid: 1,727
+        - #Test: 1,746
+
+    Parameters:
+        path (str): the path to store the dataset
+        verbose (int, optional): output verbose level
+        **kwargs
+    """
+
+    url = "https://miladeepgraphlearningproteindata.s3.us-east-2.amazonaws.com/peerdata/subcellular_localization_2.tar.gz"
+    md5 = "5d2309bf1c0c2aed450102578e434f4e"
+    splits = ["train", "valid", "test"]
+    target_fields = ["localization"]
+
+    def __init__(self, path, verbose=1, **kwargs):
+        path = os.path.expanduser(path)
+        if not os.path.exists(path):
+            os.makedirs(path)
+        self.path = path
+
+        zip_file = utils.download(self.url, path, md5=self.md5)
+        data_path = utils.extract(zip_file)
+        lmdb_files = [os.path.join(data_path, "subcellular_localization_2/subcellular_localization_2_%s.lmdb" % split)
+                      for split in self.splits]
+
+        self.load_lmdbs(lmdb_files, target_fields=self.target_fields, verbose=verbose, **kwargs)
+
+    def split(self):
+        offset = 0
+        splits = []
+        for num_sample in self.num_samples:
+            split = torch_data.Subset(self, range(offset, offset + num_sample))
+            splits.append(split)
+            offset += num_sample
+        return splits

torchdrug/datasets/bindingdb.py

@@ -0,0 +1,72 @@
+import os
+
+from rdkit import Chem
+
+from torch.utils import data as torch_data
+
+from torchdrug import data, utils
+from torchdrug.core import Registry as R
+
+
+@R.register("datasets.BindingDB")
+@utils.copy_args(data.ProteinLigandDataset.load_lmdbs, ignore=("sequence_field", "smiles_field", "target_fields"))
+class BindingDB(data.ProteinLigandDataset):
+    """
+    The BindingDB dataset with binding affinity indicating the interaction strength 
+    between pairs of protein and ligand.
+
+    Statistics:
+        - #Train: 7,900
+        - #Valid: 878
+        - #Test: 5,230
+
+    Parameters:
+        path (str): the path to store the dataset
+        verbose (int, optional): output verbose level
+        **kwargs
+    """
+
+    url = "https://miladeepgraphlearningproteindata.s3.us-east-2.amazonaws.com/peerdata/BindingDB_Kd.tar.gz"
+    md5 = "0b207cb962c4945f9003fc020b415a74"
+    splits = ["train", "valid", "random_test", "holdout_test"]
+    target_fields = ["affinity"]
+
+    def __init__(self, path, verbose=1, **kwargs):
+        path = os.path.expanduser(path)
+        if not os.path.exists(path):
+            os.makedirs(path)
+        self.path = path
+        zip_file = utils.download(self.url, path, md5=self.md5)
+        data_path = utils.extract(zip_file)
+        lmdb_files = [os.path.join(data_path, "BindingDB_Kd_%s.lmdb" % split) for split in self.splits]
+
+        self.load_lmdbs(lmdb_files, sequence_field="target", smiles_field="drug",
+                        target_fields=self.target_fields, verbose=verbose, **kwargs)
+
+    def split(self, keys=None):
+        keys = keys or self.splits
+        offset = 0
+        splits = []
+        for split_name, num_sample in zip(self.splits, self.num_samples):
+            if split_name in keys:
+                split = torch_data.Subset(self, range(offset, offset + num_sample))
+                splits.append(split)
+            offset += num_sample
+        return splits
+
+    def get_item(self, index):
+        if self.lazy:
+            graph1 = data.Protein.from_sequence(self.sequences[index], **self.kwargs)
+            mol = Chem.MolFromSmiles(self.smiles[index])
+            if not mol:
+                graph2 = None
+            else:
+                graph2 = data.Molecule.from_molecule(mol, **self.kwargs)
+        else:
+            graph1 = self.data[index][0]
+            graph2 = self.data[index][1]
+        item = {"graph1": graph1, "graph2": graph2}
+        item.update({k: v[index] for k, v in self.targets.items()})
+        if self.transform:
+            item = self.transform(item)
+        return item