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add drug static features and some minor changes

main
MahsaYazdani 2 years ago
parent
commit
a6f8e419b9

+ 3
- 0
.gitignore View File

@@ -3,3 +3,6 @@ Drug/Dataset/DDI/DrugBank/drugbank_all_full_database.xml.zip
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

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Drug/Dataset/RDkit extracted/drug2FP.csv
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Drug/Dataset/RDkit extracted/drug2descriptor.csv
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Drug/Dataset/Smiles/drug2smiles.csv
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datasets.py → Drug/datasets.py View File


models.py → Drug/models.py View File

@@ -15,16 +15,16 @@ class GCN(torch.nn.Module):
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}")

+ 23
- 0
Drug/train.py View File

@@ -0,0 +1,23 @@
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}")

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