  Drug resistance is still a major challenge in cancer therapy. Drug
combination is expected to overcome drug resistance. However, the number of
possible drug combinations is enormous, and thus it is infeasible to
experimentally screen all effective drug combinations considering the limited
resources. Therefore, computational models to predict and prioritize effective
drug combinations is important for combinatory therapy discovery in cancer. In
this study, we proposed a novel deep learning model, AuDNNsynergy, to
prediction drug combinations by integrating multi-omics data and chemical
structure data. In specific, three autoencoders were trained using the gene
expression, copy number and genetic mutation data of all tumor samples from The
Cancer Genome Atlas. Then the physicochemical properties of drugs combined with
the output of the three autoencoders, characterizing the individual cancer
cell-lines, were used as the input of a deep neural network that predicts the
synergy value of given pair-wise drug combinations against the specific cancer
cell-lines. The comparison results showed the proposed AuDNNsynergy model
outperforms four state-of-art approaches, namely DeepSynergy, Gradient Boosting
Machines, Random Forests, and Elastic Nets. Moreover, we conducted the
interpretation analysis of the deep learning model to investigate potential
vital genetic predictors and the underlying mechanism of synergistic drug
combinations on specific cancer cell-lines.
