Disease-related compound identification based on deeping learning method

Bin Yang, Wenzheng Bao, Jinglong Wang, Baitong Chen, Naoki Iwamori, Jiazi Chen, Yuehui Chen

Research output: Contribution to journalArticlepeer-review

Abstract

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. 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. The experimental results show that forgeNet performs better than support vector machines (SVM), random forest (RF), logical regression (LR), Naive Bayes (NB), XGBoost, LightGBM and gcForest. forgeNet could identify 19 compounds in Erhuang decoction (EhD) and Dexamethasone (DXMS) more accurately.

Original languageEnglish
Article number20594
JournalScientific reports
Volume12
Issue number1
DOIs
Publication statusPublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • General

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