Interpretable Drug Synergy Prediction with Graph Neural Networks for Human-AI Collaboration in Healthcare

Zehao Dong, Heming Zhang, Yixin Chen, Fuhai Li

Abstract:

We investigate molecular mechanisms of resistant or sensitive response of cancer drug combination
therapies in an inductive and interpretable manner. Though deep learning algorithms are widely used in the
drug synergy prediction problem, it is still an open problem to formulate the prediction model with
biological meaning to investigate the mysterious mechanisms of synergy (MoS) for the human-AI
collaboration in healthcare systems. To address the challenges, we propose a deep graph neural network,
IDSP (Interpretable Deep Signaling Pathways), to incorporate the gene-gene as well as gene-drug
regulatory relationships in synergic drug combination predictions. IDSP automatically learns weights of
edges based on the gene and drug node relations, i.e., signaling interactions, by a multi-layer perceptron
(MLP) and aggregates information in an inductive manner. The proposed architecture generates
interpretable drug synergy prediction by detecting important signaling interactions, and can be implemented
when the underlying molecular mechanism encounters unseen genes or signaling pathways. We test IDWSP
on signaling networks formulated by genes from 46 core cancer signaling pathways and drug combinations
from NCI ALMANAC drug combination screening data. The experimental results demonstrated that 1)
IDSP can learn from the underlying molecular mechanism to make prediction without additional drug
chemical information while achieving highly comparable performance with current state-of-art methods; 2)
IDSP show superior generality and flexibility to implement the synergy prediction task on both transductive
tasks and inductive tasks. 3) IDSP can generate interpretable results by detecting different salient signaling
patterns (i.e. MoS) for different cell lines.