Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders

In line with recent advances in neural drug design 1.1 Motivation and sensitivity prediction, we propose a novel Discovery of novel compounds with a desired efficacy and architecture for interpretable prediction of anticancer improving existing therapies are key bottlenecks in the pharmaceutical compound sensitivity using a multimodal industry, which fuel the largest R&D business attention-based convolutional encoder. Our model spending of any industry and account for 19% of the total is based on the three key pillars of drug sensitivity: R&D spending worldwide (Petrova, 2014; Goh et al., compounds' structure in the form of a SMILES 2017). Anticancer compounds, in particular, take the lion's sequence, gene expression profiles of tumors and share of drug discovery R&D efforts, with over 34% of all prior knowledge on intracellular interactions from drugs in the global R&D pipeline in 2018 (5,212 of 15,267 protein-protein interaction networks. We demonstrate drugs) (Lloyd et al., 2017). Despite enormous scientific that our multiscale convolutional attentionbased and technological advances in recent years, serendipity still (MCA) encoder significantly outperforms a plays a major role in anticancer drug discovery (Hargrave-baseline model trained on Morgan fingerprints, a Thomas et al., 2012) without a systematic way to accumulate selection of encoders based on SMILES as well and leverage years of R&D to achieve higher success as previously reported state of the art for multimodal rates in drug discovery. On the other hand, there is strong drug sensitivity prediction (R2 0.86 evidence that the response to anticancer therapy is highly dependent and RMSE 0.89).