clean up

Author: KiddoZhu

doc/source/quick_start.rst

@@ -74,7 +74,7 @@ utilization of hardware. They can also be transferred between CPUs and GPUs usin
 
 .. code:: bash
 
-    PackedMolecule(batch_size=4, num_nodes=[12, 6, 14, 9], num_edges=[22, 10, 30, 18],
+    PackedMolecule(batch_size=4, num_atoms=[12, 6, 14, 9], num_bonds=[22, 10, 30, 18],
                    device='cuda:0')
 
 Just like original PyTorch tensors, graphs support a wide range of indexing

torchdrug/data/dataset.py

@@ -116,6 +116,13 @@ def load_csv(self, csv_file, smiles_field="smiles", target_fields=None, verbose=
         self.load_smiles(smiles, targets, verbose=verbose, **kwargs)
 
     def load_pickle(self, pkl_file, verbose=0):
+        """
+        Load the dataset from a pickle file.
+
+        Parameters:
+            pkl_file (str): file name
+            verbose (int, optional): output verbose level
+        """
         with utils.smart_open(pkl_file, "rb") as fin:
             num_sample, tasks = pickle.load(fin)
 
@@ -133,6 +140,13 @@ def load_pickle(self, pkl_file, verbose=0):
                     self.targets[task] = value
 
     def save_pickle(self, pkl_file, verbose=0):
+        """
+        Save the dataset to a pickle file.
+
+        Parameters:
+            pkl_file (str): file name
+            verbose (int, optional): output verbose level
+        """
         with utils.smart_open(pkl_file, "wb") as fout:
             num_sample = len(self.data)
             tasks = self.targets.keys()
@@ -659,16 +673,16 @@ def load_sequence(self, sequences, targets, attributes=None, transform=None, laz
                 self.targets[field].append(targets[field][i])
 
     @utils.copy_args(load_sequence)
-    def load_lmdbs(self, lmdb_files, number_field="num_examples", sequence_field="primary", target_fields=None,
+    def load_lmdbs(self, lmdb_files, sequence_field="primary", target_fields=None, number_field="num_examples",
                    transform=None, lazy=False, verbose=0, **kwargs):
         """
         Load the dataset from lmdb files.
 
         Parameters:
             lmdb_files (list of str): list of lmdb files
-            number_field (str, optional): name of the field of sample count in lmdb files
             sequence_field (str, optional): name of the field of protein sequence in lmdb files
             target_fields (list of str, optional): name of target fields in lmdb files
+            number_field (str, optional): name of the field of sample count in lmdb files
             transform (Callable, optional): protein sequence transformation function
             lazy (bool, optional): if lazy mode is used, the proteins are processed in the dataloader.
                 This may slow down the data loading process, but save a lot of CPU memory and dataset loading time.
@@ -701,12 +715,13 @@ def load_lmdbs(self, lmdb_files, number_field="num_examples", sequence_field="pr
         self.num_samples = num_samples
 
     @utils.copy_args(data.Protein.from_molecule)
-    def load_pdbs(self, pdb_files, transform=None, lazy=False, verbose=0, **kwargs):
+    def load_pdbs(self, pdb_files, sanitize=True, transform=None, lazy=False, verbose=0, **kwargs):
         """
         Load the dataset from pdb files.
 
         Parameters:
             pdb_files (list of str): pdb file names
+            sanitize (bool, optional): whether to sanitize the molecule
             transform (Callable, optional): protein sequence transformation function
             lazy (bool, optional): if lazy mode is used, the proteins are processed in the dataloader.
                 This may slow down the data loading process, but save a lot of CPU memory and dataset loading time.
@@ -729,7 +744,6 @@ def load_pdbs(self, pdb_files, transform=None, lazy=False, verbose=0, **kwargs):
             pdb_files = tqdm(pdb_files, "Constructing proteins from pdbs")
         for i, pdb_file in enumerate(pdb_files):
             if not lazy or i == 0:
-                sanitize = kwargs.pop("sanitize", True)
                 mol = Chem.MolFromPDBFile(pdb_file, sanitize=sanitize)
                 if not mol:
                     logger.debug("Can't construct molecule from pdb file `%s`. Ignore this sample." % pdb_file)
@@ -779,10 +793,10 @@ def load_fasta(self, fasta_file, verbose=0, **kwargs):
     @utils.copy_args(data.Protein.from_molecule)
     def load_pickle(self, pkl_file, transform=None, lazy=False, verbose=0, **kwargs):
         """
-        Load the dataset from pickle files.
+        Load the dataset from a pickle file.
 
         Parameters:
-            pkl_file (str): pickle file name
+            pkl_file (str): file name
             transform (Callable, optional): protein sequence transformation function
             lazy (bool, optional): if lazy mode is used, the proteins are processed in the dataloader.
                 This may slow down the data loading process, but save a lot of CPU memory and dataset loading time.
@@ -808,13 +822,6 @@ def load_pickle(self, pkl_file, transform=None, lazy=False, verbose=0, **kwargs)
                 self.data.append(protein)
 
     def save_pickle(self, pkl_file, verbose=0):
-        """
-        Save the dataset to pickle files.
-
-        Parameters:
-            pkl_file (str): pickle file name
-            verbose (int, optional): output verbose level
-        """
         with utils.smart_open(pkl_file, "wb") as fout:
             num_sample = len(self.data)
             pickle.dump(num_sample, fout)
@@ -890,16 +897,16 @@ def load_sequence(self, sequences, targets, attributes=None, transform=None, laz
                 self.targets[field].append(targets[field][i])
 
     @utils.copy_args(load_sequence)
-    def load_lmdbs(self, lmdb_files, number_field="num_examples", sequence_field="primary", target_fields=None,
+    def load_lmdbs(self, lmdb_files, sequence_field="primary", target_fields=None, number_field="num_examples",
                    transform=None, lazy=False, verbose=0, **kwargs):
         """
         Load the dataset from lmdb files.
 
         Parameters:
             lmdb_files (list of str): file names
-            number_field (str, optional): name of the field of sample count in lmdb files
             sequence_field (str or list of str, optional): names of the fields of protein sequence in lmdb files
             target_fields (list of str, optional): name of target fields in lmdb files
+            number_field (str, optional): name of the field of sample count in lmdb files
             transform (Callable, optional): protein sequence transformation function
             lazy (bool, optional): if lazy mode is used, the protein pairs are processed in the dataloader.
                 This may slow down the data loading process, but save a lot of CPU memory and dataset loading time.
@@ -1022,17 +1029,17 @@ def load_sequence(self, sequences, smiles, targets, num_samples, attributes=None
         return num_samples
 
     @utils.copy_args(load_sequence)
-    def load_lmdbs(self, lmdb_files, number_field="num_examples", sequence_field="target", smiles_field="drug",
-                   target_fields=None, transform=None, lazy=False, verbose=0, **kwargs):
+    def load_lmdbs(self, lmdb_files, sequence_field="target", smiles_field="drug", target_fields=None,
+                   number_field="num_examples", transform=None, lazy=False, verbose=0, **kwargs):
         """
         Load the dataset from lmdb files.
 
         Parameters:
             lmdb_files (list of str): file names
-            number_field (str, optional): name of the field of sample count in lmdb files
             sequence_field (str, optional): name of the field of protein sequence in lmdb files
             smiles_field (str, optional): name of the field of ligand SMILES string in lmdb files
             target_fields (list of str, optional): name of target fields in lmdb files
+            number_field (str, optional): name of the field of sample count in lmdb files
             transform (Callable, optional): protein sequence transformation function
             lazy (bool, optional): if lazy mode is used, the protein-ligand pairs are processed in the dataloader.
                 This may slow down the data loading process, but save a lot of CPU memory and dataset loading time.

torchdrug/data/feature.py

@@ -307,7 +307,7 @@ def residue_symbol(residue):
 
 @R.register("features.residue.default")
 def residue_default(residue):
-    """Default atom feature.
+    """Default residue feature.
 
     Features:
         GetResidueName(): one-hot embedding for the residue symbol

torchdrug/data/protein.py

@@ -252,13 +252,7 @@ def from_molecule(cls, mol, atom_feature="default", bond_feature="default", resi
                    meta_dict=protein.meta_dict, **protein.data_dict)
 
     @classmethod
-    def from_sequence_fast(cls, sequence):
-        """
-        A faster version of creating a protein from a sequence.
-
-        Parameters:
-            sequence (str): string
-        """
+    def _residue_from_sequence(cls, sequence):
         residue_type = []
         residue_feature = []
         sequence = sequence + "G"
@@ -278,10 +272,16 @@ def from_sequence_fast(cls, sequence):
     @classmethod
     @utils.deprecated_alias(node_feature="atom_feature", edge_feature="bond_feature", graph_feature="mol_feature")
     def from_sequence(cls, sequence, atom_feature="default", bond_feature="default", residue_feature="default",
-                      mol_feature=None, kekulize=False, residue_only=False):
+                      mol_feature=None, kekulize=False):
         """
         Create a protein from a sequence.
 
+        .. note::
+
+            It takes considerable time to construct proteins with a large number of atoms and bonds.
+            If you only need residue information, you may speed up the construction by setting
+            ``atom_feature`` and ``bond_feature`` to ``None``.
+
         Parameters:
             sequence (str): protein sequence
             atom_feature (str or list of str, optional): atom features to extract
@@ -292,14 +292,9 @@ def from_sequence(cls, sequence, atom_feature="default", bond_feature="default",
                 Note this only affects the relation in ``edge_list``.
                 For ``bond_type``, aromatic bonds are always stored explicitly.
                 By default, aromatic bonds are stored.
-            residue_only (bool, optional): only store residue information without atom information.
-                This can speed up the processing.
         """
-        if residue_only:
-            if residue_feature != "default":
-                raise ValueError("`residue_only` only supports the default residue feature, "
-                                 "but found `%s` for `residue_feature`" % residue_feature)
-            return cls.from_sequence_fast(sequence)
+        if atom_feature is None and bond_feature is None and residue_feature == "default":
+            return cls._residue_from_sequence(sequence)
 
         mol = Chem.MolFromSequence(sequence)
         if mol is None:
@@ -324,7 +319,7 @@ def from_pdb(cls, pdb_file, atom_feature="default", bond_feature="default", resi
                 Note this only affects the relation in ``edge_list``.
                 For ``bond_type``, aromatic bonds are always stored explicitly.
                 By default, aromatic bonds are stored.
-            sanitize (bool, optional): whether to sanitize the molecule.
+            sanitize (bool, optional): whether to sanitize the molecule
         """
         if not os.path.exists(pdb_file):
             raise FileNotFoundError("No such file `%s`" % pdb_file)
@@ -524,7 +519,7 @@ def repeat(self, count):
                                 num_relation=num_relation, meta_dict=self.meta_dict, **data_dict)
 
     def residue2atom(self, residue_index):
-        """Map residue id to atom ids."""
+        """Map residue ids to atom ids."""
         residue_index = self._standarize_index(residue_index, self.num_residue)
         if not hasattr(self, "node_inverted_index"):
             self.node_inverted_index = self._build_node_inverted_index()
@@ -992,7 +987,7 @@ def from_molecule(cls, mols, atom_feature="default", bond_feature="default", res
                    offsets=protein._offsets, meta_dict=protein.meta_dict, **protein.data_dict)
 
     @classmethod
-    def from_sequence_fast(cls, sequences):
+    def _residue_from_sequence(cls, sequences):
         num_residues = []
         residue_type = []
         residue_feature = []
@@ -1021,10 +1016,16 @@ def from_sequence_fast(cls, sequences):
     @classmethod
     @utils.deprecated_alias(node_feature="atom_feature", edge_feature="bond_feature", graph_feature="mol_feature")
     def from_sequence(cls, sequences, atom_feature="default", bond_feature="default", residue_feature="default",
-                      mol_feature=None, kekulize=False, residue_only=False):
+                      mol_feature=None, kekulize=False):
         """
         Create a packed protein from a list of sequences.
 
+        .. note::
+
+            It takes considerable time to construct proteins with a large number of atoms and bonds.
+            If you only need residue information, you may speed up the construction by setting
+            ``atom_feature`` and ``bond_feature`` to ``None``.
+
         Parameters:
             sequences (str): list of protein sequences
             atom_feature (str or list of str, optional): atom features to extract
@@ -1035,14 +1036,9 @@ def from_sequence(cls, sequences, atom_feature="default", bond_feature="default"
                 Note this only affects the relation in ``edge_list``.
                 For ``bond_type``, aromatic bonds are always stored explicitly.
                 By default, aromatic bonds are stored.
-            residue_only (bool, optional): only store residue information without atom information.
-                This can speed up the processing.
         """
-        if residue_only:
-            if residue_feature != "default":
-                raise ValueError("`residue_only` only supports the default residue feature, "
-                                 "but found `%s` for `residue_feature`" % residue_feature)
-            return cls.from_sequence_fast(sequences)
+        if atom_feature is None and bond_feature is None and residue_feature == "default":
+            return cls._residue_from_sequence(sequences)
 
         mols = []
         for sequence in sequences:
@@ -1056,7 +1052,7 @@ def from_sequence(cls, sequences, atom_feature="default", bond_feature="default"
     @classmethod
     @utils.deprecated_alias(node_feature="atom_feature", edge_feature="bond_feature", graph_feature="mol_feature")
     def from_pdb(cls, pdb_files, atom_feature="default", bond_feature="default", residue_feature="default",
-                 mol_feature=None, kekulize=False):
+                 mol_feature=None, kekulize=False, sanitize=False):
         """
         Create a protein from a list of PDB files.
 
@@ -1070,10 +1066,11 @@ def from_pdb(cls, pdb_files, atom_feature="default", bond_feature="default", res
                 Note this only affects the relation in ``edge_list``.
                 For ``bond_type``, aromatic bonds are always stored explicitly.
                 By default, aromatic bonds are stored.
+            sanitize (bool, optional): whether to sanitize the molecule
         """
         mols = []
         for pdb_file in pdb_files:
-            mol = Chem.MolFromPDBFile(pdb_file)
+            mol = Chem.MolFromPDBFile(pdb_file, sanitize=sanitize)
             mols.append(mol)
 
         return cls.from_molecule(mols, atom_feature, bond_feature, residue_feature, mol_feature, kekulize)

torchdrug/datasets/alphafolddb.py

@@ -6,7 +6,7 @@
 
 
 @R.register("datasets.AlphaFoldDB")
-@utils.copy_args(data.ProteinDataset.load_pdbs, ignore=("filtered_pdb",))
+@utils.copy_args(data.ProteinDataset.load_pdbs)
 class AlphaFoldDB(data.ProteinDataset):
     """
     3D protein structures predicted by AlphaFold.

torchdrug/datasets/enzyme_commission.py

@@ -10,7 +10,7 @@
 
 
 @R.register("datasets.EnzymeCommission")
-@utils.copy_args(data.ProteinDataset.load_pdbs, ignore=("filtered_pdb",))
+@utils.copy_args(data.ProteinDataset.load_pdbs)
 class EnzymeCommission(data.ProteinDataset):
     """
     A set of proteins with their 3D structures and EC numbers, which describes their
@@ -23,7 +23,7 @@ class EnzymeCommission(data.ProteinDataset):
 
     Parameters:
         path (str): the path to store the dataset
-        test_cutoff (float): the test cutoff used to split the dataset
+        test_cutoff (float, optional): the test cutoff used to split the dataset
         verbose (int, optional): output verbose level
         **kwargs
     """
@@ -47,13 +47,14 @@ def __init__(self, path, test_cutoff=0.95, verbose=1, **kwargs):
         pkl_file = os.path.join(path, self.processed_file)
 
         csv_file = os.path.join(path, "nrPDB-EC_test.csv")
-        filtered_pdb = set()
+        pdb_ids = []
         with open(csv_file, "r") as fin:
             reader = csv.reader(fin, delimiter=",")
             idx = self.test_cutoffs.index(test_cutoff) + 1
             _ = next(reader)
             for line in reader:
-                if line[idx] == "0": filtered_pdb.add(line[0])
+                if line[idx] == "0":
+                    pdb_ids.append(line[0])
 
         if os.path.exists(pkl_file):
             self.load_pickle(pkl_file, verbose=verbose, **kwargs)
@@ -64,8 +65,8 @@ def __init__(self, path, test_cutoff=0.95, verbose=1, **kwargs):
                 pdb_files += sorted(glob.glob(os.path.join(split_path, split, "*.pdb")))
             self.load_pdbs(pdb_files, verbose=verbose, **kwargs)
             self.save_pickle(pkl_file, verbose=verbose)
-        if len(filtered_pdb) > 0:
-            self.filter_pdb(filtered_pdb)
+        if len(pdb_ids) > 0:
+            self.filter_pdb(pdb_ids)
 
         tsv_file = os.path.join(path, "nrPDB-EC_annot.tsv")
         pdb_ids = [os.path.basename(pdb_file).split("_")[0] for pdb_file in self.pdb_files]
@@ -74,12 +75,13 @@ def __init__(self, path, test_cutoff=0.95, verbose=1, **kwargs):
         splits = [os.path.basename(os.path.dirname(pdb_file)) for pdb_file in self.pdb_files]
         self.num_samples = [splits.count("train"), splits.count("valid"), splits.count("test")]
 
-    def filter_pdb(self, filtered_pdb):
+    def filter_pdb(self, pdb_ids):
+        pdb_ids = set(pdb_ids)
         sequences = []
         pdb_files = []
         data = []
         for sequence, pdb_file, protein in zip(self.sequences, self.pdb_files, self.data):
-            if os.path.basename(pdb_file).split("_")[0] in filtered_pdb:
+            if os.path.basename(pdb_file).split("_")[0] in pdb_ids:
                 continue
             sequences.append(sequence)
             pdb_files.append(pdb_file)