Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russian Federation.
Curr Med Chem. 2021;28(38):7840-7861. doi: 10.2174/0929867328666210504114351.
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.
如今，计算方法在新的类药物化合物的设计和疾病的药物治疗优化中发挥着重要作用。包括人类免疫缺陷病毒（HIV）、埃博拉病毒、最近发现的冠状病毒在内的病毒感染的不断增加，导致许多新的感染者面临高死亡风险或严重并发症。大量的化学、生物、临床数据摆在研究人员面前。因此，有许多机会可以基于机器学习方法找到化学数据的特定特征与生物活性化合物的抗病毒活性之间的关系。生物和临床数据也可用于建立模型来预测病毒基因型与药物抗性之间的关系，这可能有助于确定治疗的临床结果。在当前的研究中，我们考虑了过去十年中在抗病毒研究中使用的机器学习方法。我们详细综述了机器学习方法在设计新的潜在抗病毒药物和疫苗、药物抗性预测以及病毒-宿主相互作用分析中的应用。我们的综述还涵盖了使用机器学习方法进行抗病毒研究的观点，包括登革热、埃博拉病毒、甲型流感、人类免疫缺陷病毒、冠状病毒等。
