add drug static features and some minor changes

.gitignore

 Drug/Dataset/DDI/SNAP Stanford/ChCh-Miner_durgbank-chem-chem.tsv.gz
 Drug/Dataset/DDI/DrugBank/raw/Drugbank_drug_interactions.tsv
 Drug/Dataset/DDI/SNAP Stanford/ChCh-Miner_durgbank-chem-chem.tsv
+Cell/data/DTI/SNAP Stanford/ChG-Miner_miner-chem-gene.tsv
+Cell/data/DTI/SNAP Stanford/ChG-Miner_miner-chem-gene.tsv.gz
+Drug/Dataset/Smiles/drugbank_all_structure_links.csv.zip

models.py → Drug/models.py

         return x
 
 
-model = GCN(dataset.num_features, dataset.num_classes)
-model.train()
-optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+# model = GCN(dataset.num_features, dataset.num_classes)
+# model.train()
+# optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
 
-print("Training on CPU.")
+# print("Training on CPU.")
 
-for epoch in range(1, 6):
-    optimizer.zero_grad()
-    out = model(data.x, data.edge_index, data.edge_attr)
-    loss = F.cross_entropy(out, data.y)
-    loss.backward()
-    optimizer.step()
-    print(f"Epoch: {epoch}, Loss: {loss}")
+# for epoch in range(1, 6):
+#     optimizer.zero_grad()
+#     out = model(data.x, data.edge_index, data.edge_attr)
+#     loss = F.cross_entropy(out, data.y)
+#     loss.backward()
+#     optimizer.step()
+#     print(f"Epoch: {epoch}, Loss: {loss}")

Drug/train.py

+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+
+from models import GCN
+from datasets import DDInteractionDataset
+
+
+
+if __name__ == '__main__':
+    model = GCN(dataset.num_features, dataset.num_classes)
+    model.train()
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+
+    # training on CPU
+    for epoch in range(1, 6):
+        optimizer.zero_grad()
+        out = model(data.x, data.edge_index, data.edge_attr)
+        loss = F.cross_entropy(out, data.y)
+        loss.backward()
+        optimizer.step()
+        print(f"Epoch: {epoch}, Loss: {loss}")