Antiviral Activity of Medicinal Plants against Human Coronavirus: a systematic scoping review of and experimentations.

OBJECTIVE: To investigate the and studies of natural compounds and medicinal plants with anti-coronavirus activity. METHODS: A systematic review was performed based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Animal Research: Reporting of experiments guidelines to find data for medicinal plants and natural products effective against human coronaviruses in or studies. Studies published up to September 6, 2020 were included. Studies ( or ) reporting the effect of medicinal plants and natural products or their derivatives on human coronavirus were included RESULTS: Promising anti-coronavirus effects are seen with different herbal compounds like some diterpenoids, sesquiterpenoids, and three compounds in tea with 3CLpro inhibiting effect of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV); Hirsutenone, Six cinnamic amides and bavachinin are PLpro inhibitors and Tanshinones are active on both 3CLpro and PLpro. Some flavonoid compounds of Citrus fruits act on Immun-oregulation and target angiotensin-converting enzyme 2 which is used by SARS-COV for entry. Virus helicase is possibly inhibited by two compounds myricetin and scutellarein. CONCLUSION: This review shows that complementary medicine have the potential for new drug discovery against coronavirus. Further research is needed before definitive conclusions can be made concerning the safety and efficacy of the use of these medicinal plants.

A multimodal deep learning-based drug repurposing approach for treatment of COVID-19.

Recently, various computational methods have been proposed to find new therapeutic applications of the existing drugs. The Multimodal Restricted Boltzmann Machine approach (MM-RBM), which has the capability to connect the information about the multiple modalities, can be applied to the problem of drug repurposing. The present study utilized MM-RBM to combine two types of data, including the chemical structures data of small molecules and differentially expressed genes as well as small molecules perturbations. In the proposed method, two separate RBMs were applied to find out the features and the specific probability distribution of each datum (modality). Besides, RBM was used to integrate the discovered features, resulting in the identification of the probability distribution of the combined data. The results demonstrated the significance of the clusters acquired by our model. These clusters were used to discover the medicines which were remarkably similar to the proposed medications to treat COVID-19. Moreover, the chemical structures of some small molecules as well as dysregulated genes' effect led us to suggest using these molecules to treat COVID-19. The results also showed that the proposed method might prove useful in detecting the highly promising remedies for COVID-19 with minimum side effects. All the source codes are accessible using https://github.com/LBBSoft/Multimodal-Drug-Repurposing.git.