Olga Tarasova* and Vladimir Poroikov Pages 7840 - 7861 ( 22 )
Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others lead to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction and analysis of virus-host interactions. Our review also covers the perspectives of using the machine learning approaches for antiviral research including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses and some others.
Machine learning, cheminformatics, bioinformatics, antiviral drugs, HIV, drug treatment optimization.
Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow