The Graph Convolutional Network with Multi-representation Alignment for
  Drug Synergy Prediction
Xinxing Yang Genke Yang Jian Chu
Drug combination refers to the use of two or more drugs to treat a specific
disease at the same time. It is currently the mainstream way to treat complex
diseases. Compared with single drugs, drug combinations have better efficacy
and can better inhibit toxicity and drug resistance. The computational model
based on deep learning concatenates the representation of multiple drugs and
the corresponding cell line feature as input, and the output is whether the
drug combination can have an inhibitory effect on the cell line. However, this
strategy of concatenating multiple representations has the following defects:
the alignment of drug representation and cell line representation is ignored,
resulting in the synergistic relationship not being reflected positionally in
the embedding space. Moreover, the alignment measurement function in deep
learning cannot be suitable for drug synergy prediction tasks due to
differences in input types. Therefore, in this work, we propose a graph
convolutional network with multi-representation alignment (GCNMRA) for
predicting drug synergy. In the GCNMRA model, we designed a
multi-representation alignment function suitable for the drug synergy
prediction task so that the positional relationship between drug
representations and cell line representation is reflected in the embedding
space. In addition, the vector modulus of drug representations and cell line
representation is considered to improve the accuracy of calculation results and
accelerate model convergence. Finally, many relevant experiments were run on
multiple drug synergy datasets to verify the effectiveness of the above
innovative elements and the excellence of the GCNMRA model.