摘要
如今,计算方法在设计新的类药物化合物和优化疾病的药物治疗方面发挥着重要作用。病毒感染的新增长,包括由人类免疫缺陷病毒 (HIV)、埃博拉病毒、最近检测到的冠状病毒和其他一些病毒引起的感染,导致许多新感染的人具有很高的死亡或严重并发症风险。大量的化学、生物学和临床数据可供研究人员使用。因此,有很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。生物学和临床数据也可用于建立模型以预测病毒基因型和耐药性之间的关系,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进行的抗病毒研究中的机器学习方法。我们详细概述了机器学习方法在设计新的潜在抗病毒剂和疫苗、耐药性预测和病毒-宿主相互作用分析中的应用。我们的评论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。
关键词: 机器学习、化学信息学、生物信息学、抗病毒药物、HIV、药物治疗优化。
Current Medicinal Chemistry
Title:Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy
Volume: 28 Issue: 38
关键词: 机器学习、化学信息学、生物信息学、抗病毒药物、HIV、药物治疗优化。
摘要: 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.
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Cite this article as:
Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy, Current Medicinal Chemistry 2021; 28 (38) . https://dx.doi.org/10.2174/0929867328666210504114351
DOI https://dx.doi.org/10.2174/0929867328666210504114351 |
Print ISSN 0929-8673 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-533X |
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