Abstract
Phenotype-based compound screening has advantages over target-based drug discovery, but is unscalable and lacks understanding of mechanism of drug action. A chemical-induced gene expression profile provides a mechanistic signature of phenotypic response; however, the use of such data is limited by their sparseness, unreliability and relatively low throughput. Few methods can perform phenotype-based de novo chemical compound screening. Here we propose a mechanism-driven neural network-based method, DeepCE—which utilizes a graph neural network and multihead attention mechanism to model chemical substructure–gene and gene–gene associations—for predicting the differential gene expression profile perturbed by de novo chemicals. Moreover, we propose a novel data augmentation method that extracts useful information from unreliable experiments in the L1000 dataset. The experimental results show that DeepCE achieves superior performances to state-of-the-art methods. The effectiveness of gene expression profiles generated from DeepCE is further supported by comparing them with observed data for downstream classification tasks. To demonstrate the value of DeepCE, we apply it to drug repurposing of COVID-19 and generate novel lead compounds consistent with clinical evidence. DeepCE thus provides a potentially powerful framework for robust predictive modelling by utilizing noisy omics data and screening novel chemicals for the modulation of a systemic response to disease.
