Cancer Drug Sensitivity Estimation using Modular Deep Graph Neural Networks

Computational drug sensitivity models have the
potential to improve therapeutic outcomes by
identifying targeted drugs components that are tailored
to the transcriptomic profile of a given primary
tumor. The SMILES representation of molecules that
is used by state-of-the-art drug-sensitivity models is
not conducive for neural networks to generalize to
new drugs, in part because the distance between
atoms does not generally correspond to the distance
between their representation in the SMILES strings.
Graph-attention networks, on the other hand, are
high-capacity models that require large training-
data volumes which are not available for drug-
sensitivity estimation. We develop a modular drug-
sensitivity graph-attentional neural network. The
modular architecture allows us to separately pre-
train the graph encoder and graph-attentional pooling
layer on related tasks for which more data are
available. We observe that this model outperforms
reference models for the use cases of precision
oncology and drug discovery; in particular, it is
better able to predict the specific interaction
between drug and cell line that is not explained
by the general cytotoxicity of the drug and the
overall survivability of the cell line.