Mutagenic compounds have the potential to induce genetic mutations in living organisms, thereby increasing the risk of cancer. Detecting mutagenicity is thus a critical endeavour in the field of biomedicine.
This project explores the use of Graph Convolutional Networks (GCNs) to
predict mutagenicity in chemical compounds. Through a rigorous evaluation of
various graph convolutional and pooling techniques on the MUTAG dataset, we find
that GCNs can effectively learn graph representations, achieving high accuracy
in mutagenicity prediction. For details refer to the source code in main.ipynb
and the two-page report detailing the experiment setup and findings in
report.pdf.
This project was carried out for the course Deep Learning in Biomedicine (CS-502) at EPFL. The following graph convolutional layers are implemented in PyTorch:
GraphConv(Kipf & Welling, 2017)GraphSAGEConv(Hamilton et al., 2017)GATConv(Veličković et al., 2018)EdgeConv(Adapted from Gong et al., 2018)