Drug Components-Disease Network Related to Acute Lung Injury Inference Based on Forest Graph-embedded Deep Feedforward Network

Background: Acute lung injury (ALI) is a serious respiratory disease, which can lead to acute respiratory failure or death. It is closely related to the pathogenesis of New Coronavirus pneumonia (COVID-19). Many researches showed that traditional Chinese medicine (TCM) had a good effect on its intervention, and network pharmacology could play a very important role. Results: : In order to construct "disease-gene-target-drug" interaction network more accurately, deep learning algorithm is utilized in this paper. Two ALI-related target genes (REAL and SATA3) are considered, and the active and inactive compounds of the two corresponding target genes are collected as training data, respectively. Molecular descriptors and molecular fingerprints are utilized to characterize each compound. Forest graph embedded deep feed forward network (forgeNet) is proposed to train and identify 19 compounds in Erhuang decoction (EhD) and Dexamethasone (DXMS). Conclusions: : The experiment results show that forgeNet performs better than support vector machines (SVM), random forest (RF) and gcForest.