--- a
+++ b/torchdrug/datasets/fold.py
@@ -0,0 +1,53 @@
+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.Fold")
+@utils.copy_args(data.ProteinDataset.load_lmdbs, ignore=("target_fields",))
+class Fold(data.ProteinDataset):
+    """
+    Fold labels for a set of proteins determined by the global structural topology.
+
+    Statistics:
+        - #Train: 12,312
+        - #Valid: 736
+        - #Test: 718
+
+    Parameters:
+        path (str): the path to store the dataset
+        verbose (int, optional): output verbose level
+        **kwargs
+    """
+
+    url = "http://s3.amazonaws.com/songlabdata/proteindata/data_pytorch/remote_homology.tar.gz"
+    md5 = "1d687bdeb9e3866f77504d6079eed00a"
+    splits = ["train", "valid", "test_fold_holdout", "test_family_holdout", "test_superfamily_holdout"]
+    target_fields = ["fold_label"]
+
+    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, "remote_homology/remote_homology_%s.lmdb" % split)
+                      for split in self.splits]
+
+        self.load_lmdbs(lmdb_files, 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
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