Cancer Drug Sensitivity Estimation using Modular Deep Graph Neural Networks

Motivation: Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted
drug 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.
Results: 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 metabolite-property and toxicity tasks. 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.