  Synergistic drug combinations provide huge potentials to enhance therapeutic
efficacy and to reduce adverse reactions. However, effective and synergistic
drug combination prediction remains an open question because of the unknown
causal disease signaling pathways. Though various deep learning (AI) models
have been proposed to quantitatively predict the synergism of drug
combinations, the major limitation of existing deep learning methods is that
they are inherently not interpretable, which makes the conclusions of AI models
untransparent to human experts, henceforth limiting the robustness of the model
conclusion and the implementation ability of these models in real-world
human--AI healthcare. In this paper, we develop an interpretable graph neural
network (GNN) that reveals the underlying essential therapeutic targets and the
mechanism of the synergy (MoS) by mining the sub-molecular network of great
importance. The key point of the interpretable GNN prediction model is a novel
graph pooling layer, a self-attention-based node and edge pool (henceforth
SANEpool), that can compute the attention score (importance) of genes and
connections based on the genomic features and topology. As such, the proposed
GNN model provides a systematic way to predict and interpret the drug
combination synergism based on the detected crucial sub-molecular network.
Experiments on various well-adopted drug-synergy-prediction datasets
demonstrate that (1) the SANEpool model has superior predictive ability to
generate accurate synergy score prediction, and (2) the sub-molecular networks
detected by the SANEpool are self-explainable and salient for identifying
synergistic drug combinations.
