Identify compound-protein interaction with knowledge graph embedding of perturbation transcriptomics

Abstract

The emergence of perturbation transcriptomics provides a new perspective and opportunity for drug discovery, but existing analysis methods suffer from inadequate performance and limited applicability. In this work, we present PertKGE, a method designed to improve compound-protein interaction with knowledge graph embedding of perturbation transcriptomics. PertKGE incorporates diverse regulatory elements and accounts for multi-level regulatory events within biological systems, leading to significant improvements compared to existing baselines in two critical "cold-start" settings: inferring binding targets for new compounds and conducting virtual ligand screening for new targets. We further demonstrate the pivotal role of incorporating multi-level regulatory events in alleviating dataset bias. Notably, it enables the identification of ectonucleotide pyrophosphatase/phosphodiesterase-1 as the target responsible for the unique anti-tumor immunotherapy effect of tankyrase inhibitor K-756, and the discovery of five novel hits targeting the emerging cancer therapeutic target, aldehyde dehydrogenase 1B1, with a remarkable hit rate of 10.2%. These findings highlight the potential of PertKGE to accelerate drug discovery by elucidating mechanisms of action and identifying novel therapeutic compounds.