Integrating multi-modal deep learning on knowledge graph for the discovery of synergistic drug combinations against infectious diseases
Qing Ye, Ruolan Xu, Dan Li, Yu Kang, Yafeng Deng, Feng Zhu, Jiming Chen, Shibo He, Chang‐Yu Hsieh, Tingjun Hou
Abstract
The threat to global health posed by unpredictable infections and increasing antimicrobial resistance necessitates the urgent development of drug combination therapies (DCBs) for infectious diseases. Substantial efforts have been devoted to perfecting predictions for DCBs, but data scarcity and poor model interpretability continue to present significant barriers to the development of novel DCBs. To address these issues, here we propose a framework for predicting DCBs by combining knowledge graph representation learning and the technique of community discovery for complex networks. Within this framework, we demonstrate that multi-modal information and multiple types of DCBs could significantly facilitate the predictive performance and improve hit rates in realistic virtual screening scenarios. The high hit rate of 85% for experimental validation strongly supports the proposal that our approach could effectively harness useful information hidden in highly complex biological networks and accelerate in silico discovery of pairwise DCBs for infectious diseases and beyond.