SSYA10-001 for COVID-19

Coronaviruses in general are a zoonotic pathogen with significant cross-species transmission. They are widely distributed in nature and have recently become a major threat to global public health. Vaccines are the preferred strategy for the prevention of coronaviruses. However, the rapid rate of virus mutation, large number of prevalent strains, and lag in vaccine development contribute to the continuing frequent occurrence of coronavirus diseases. There is an urgent need for new antiviral strategies to address coronavirus infections effectively. Antiviral drugs are important in the prevention and control of viral diseases. Members of the genus coronavirus are highly similar in life-cycle processes such as viral invasion and replication. These, together with the high degree of similarity in the protein sequences and structures of viruses in the same genus, provide common targets for antiviral drug screening of coronaviruses and have led to important advances in recent years. In this review, we summarize the pathogenic mechanisms of coronavirus, common drugs targeting coronavirus entry into host cells, and common drug targets against coronaviruses based on biosynthesis and on viral assembly and release. We also describe the common targets of antiviral drugs against coronaviruses and the progress of antiviral drug research. Our aim is to provide a theoretical basis for the development of antiviral drugs and to accelerate the development and utilization of commonly used antiviral drugs in China.

AbstractThe search for an effective drug is still urgent for COVID-19 as no drug with proven clinical efficacy is available. Finding the new purpose of an approved or investigational drug, known as drug repurposing, has become increasingly popular in recent years. We propose here a new drug repurposing approach for COVID-19, based on knowledge graph (KG) embeddings. Our approach learns “ensemble embeddings” of entities and relations in a COVID-19 centric KG, in order to get a better latent representation of the graph elements. Ensemble KG-embeddings are subsequently used in a deep neural network trained for discovering potential drugs for COVID-19. Compared to related works, we retrieve more in-trial drugs among our top-ranked predictions, thus giving greater confidence in our prediction for out-of-trial drugs. For the first time to our knowledge, molecular docking is then used to evaluate the predictions obtained from drug repurposing using KG embedding. We show that Fosinopril is a potential ligand for the SARS-CoV-2 nsp13 target. We also provide explanations of our predictions thanks to rules extracted from the KG and instanciated by KG-derived explanatory paths. Molecular evaluation and explanatory paths bring reliability to our results and constitute new complementary and reusable methods for assessing KG-based drug repurposing.