  We present a novel approach for the prediction of anticancer compound
sensitivity by means of multi-modal attention-based neural networks (PaccMann).
In our approach, we integrate three key pillars of drug sensitivity, namely,
the molecular structure of compounds, transcriptomic profiles of cancer cells
as well as prior knowledge about interactions among proteins within cells. Our
models ingest a drug-cell pair consisting of SMILES encoding of a compound and
the gene expression profile of a cancer cell and predicts an IC50 sensitivity
value. Gene expression profiles are encoded using an attention-based encoding
mechanism that assigns high weights to the most informative genes. We present
and study three encoders for SMILES string of compounds: 1) bidirectional
recurrent 2) convolutional 3) attention-based encoders. We compare our devised
models against a baseline model that ingests engineered fingerprints to
represent the molecular structure. We demonstrate that using our
attention-based encoders, we can surpass the baseline model. The use of
attention-based encoders enhance interpretability and enable us to identify
genes, bonds and atoms that were used by the network to make a prediction.
