  In line with recent advances in neural drug design and sensitivity
prediction, we propose a novel architecture for interpretable prediction of
anticancer compound sensitivity using a multimodal attention-based
convolutional encoder. Our model is based on the three key pillars of drug
sensitivity: compounds' structure in the form of a SMILES sequence, gene
expression profiles of tumors and prior knowledge on intracellular interactions
from protein-protein interaction networks. We demonstrate that our multiscale
convolutional attention-based (MCA) encoder significantly outperforms a
baseline model trained on Morgan fingerprints, a selection of encoders based on
SMILES as well as previously reported state of the art for multimodal drug
sensitivity prediction (R2 = 0.86 and RMSE = 0.89). Moreover, the
explainability of our approach is demonstrated by a thorough analysis of the
attention weights. We show that the attended genes significantly enrich
apoptotic processes and that the drug attention is strongly correlated with a
standard chemical structure similarity index. Finally, we report a case study
of two receptor tyrosine kinase (RTK) inhibitors acting on a leukemia cell
line, showcasing the ability of the model to focus on informative genes and
submolecular regions of the two compounds. The demonstrated generalizability
and the interpretability of our model testify its potential for in-silico
prediction of anticancer compound efficacy on unseen cancer cells, positioning
it as a valid solution for the development of personalized therapies as well as
for the evaluation of candidate compounds in de novo drug design.
