diff --git a/docker/postgresql/Dockerfile b/docker/postgresql/Dockerfile
index dcfa052..137526d 100644
--- a/docker/postgresql/Dockerfile
+++ b/docker/postgresql/Dockerfile
@@ -1,3 +1,6 @@
 FROM alpine:latest
 
 RUN apk add postgresql
+
+RUN mkdir /data && \
+  chown postgres:postgres /data
diff --git a/docker/postgresql/docker-init.sh b/docker/postgresql/docker-init.sh
new file mode 100644
index 0000000..a69650a
--- /dev/null
+++ b/docker/postgresql/docker-init.sh
@@ -0,0 +1,5 @@
+#!/bin/sh
+
+if [ ! -f /data/PG_VERSION ]; then
+  initdb -D /data --pwfile=/superuser_password
+fi
diff --git a/experiments/decagon_run/decagon_run.py b/experiments/decagon_run/decagon_run.py
new file mode 100644
index 0000000..239250e
--- /dev/null
+++ b/experiments/decagon_run/decagon_run.py
@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+
+from icosagon.data import Data
+import os
+import pandas as pd
+from bisect import bisect_left
+import torch
+
+
+def index(a, x):
+    i = bisect_left(a, x)
+    if i != len(a) and a[i] == x:
+        return i
+    raise ValueError
+
+
+def main():
+    path = '/pstore/data/data_science/ref/decagon'
+    df_combo = pd.read_csv(os.path.join(path, 'bio-decagon-combo.csv'))
+    df_effcat = pd.read_csv(os.path.join(path, 'bio-decagon-effectcategories.csv'))
+    df_mono = pd.read_csv(os.path.join(path, 'bio-decagon-mono.csv'))
+    df_ppi = pd.read_csv(os.path.join(path, 'bio-decagon-ppi.csv'))
+    df_tgtall = pd.read_csv(os.path.join(path, 'bio-decagon-targets-all.csv'))
+    df_tgt = pd.read_csv(os.path.join(path, 'bio-decagon-targets.csv'))
+    lst = [ 'df_combo', 'df_effcat', 'df_mono', 'df_ppi', 'df_tgtall', 'df_tgt' ]
+    for nam in lst:
+        print(f'len({nam}): {len(locals()[nam])}')
+        print(f'{nam}.columns: {locals()[nam].columns}')
+
+    genes = set()
+    genes = genes.union(df_ppi['Gene 1']).union(df_ppi['Gene 2']) \
+        .union(df_tgtall['Gene']).union(df_tgt['Gene'])
+    genes = sorted(genes)
+    print('len(genes):', len(genes))
+
+    drugs = set()
+    drugs = drugs.union(df_combo['STITCH 1']).union(df_combo['STITCH 2']) \
+        .union(df_mono['STITCH']).union(df_tgtall['STITCH']).union(df_tgt['STITCH'])
+    drugs = sorted(drugs)
+    print('len(drugs):', len(drugs))
+
+    data = Data()
+    data.add_node_type('Gene', len(genes))
+    data.add_node_type('Drug', len(drugs))
+
+    print('Indexing rows...')
+    rows = [index(genes, g) for g in df_ppi['Gene 1']]
+    print('Indexing cols...')
+    cols = [index(genes, g) for g in df_ppi['Gene 2']]
+    indices = list(zip(rows, cols))
+    indices = torch.tensor(indices).transpose(0, 1)
+    values = torch.ones(len(rows))
+    print('indices.shape:', indices.shape, 'values.shape:', values.shape)
+    adj_mat = torch.sparse_coo_tensor(indices, values, size=(len(genes),) * 2)
+    adj_mat = (adj_mat + adj_mat.transpose(0, 1)) / 2
+    print('adj_mat created')
+    fam = data.add_relation_family('PPI', 0, 0, True)
+    rel = fam.add_relation_type('PPI', adj_mat)
+
+
+if __name__ == '__main__':
+    main()