Deep Learning-based Network Pharmacology for Exploring the Mechanism of Licorice for the Treatment of COVID-19

Abstract

Licorice, a traditional Chinese medicine, has been widely used for the treatment of COVID-19, but all active compounds and the corresponding targets are still not clear. Therefore, this study proposed a deep learning-based network pharmacology approach to identify more potential active compounds and targets of licorice and to collect information regarding different representative compounds. A graph convolutional neural network was used to construct a molecular map and a convolutional neural network was used to develop a Morgan fingerprint. Twenty core compounds and 6 core targets were predicted, among which 4 compounds (quercetin, naringenin, liquiritigenin, and licoisoflavanone), 2 targets (SYK and JAK2) and the relevant pathways (P53, cAMP, and NF-kB) were associated with SARS-CoV-2-infection, which were confirmed by previous studies. In addition, 2 new active compounds (glabrone and vestitol) and 2 new targets (PTEN and MAP3K8) were further validated by molecular docking, and the results showed that these active compounds bound to SARS-CoV-2 related targets, including the main protease (Mpro, also called 3CLpro), the spike protein (S protein), and the angiotensin-converting enzyme 2 (ACE2). Overall, we conclude that the findings of this study has the value of further exploration in the following experiment and clinical application.

Exploring the action mechanism of Jinxin oral liquid on asthma by network pharmacology, molecular docking, and microRNA recognition

“…[77][78][79] Deep learning can also be applied for target identification and supplementation, disease-related component recognition, and screening potential natural products in some TCM studies integrating network pharmacology with machine learning. [80][81][82][83] Furthermore, our team intends to attempt using deep learning in additional research to investigate the efficacy and mechanism of JXOL in asthma, including the assessment of side effects, the prediction of new targets, the enhancement of JXOL composition, etc.…”