目前对抗冠状病毒的数据资源和生物信息学工具的发展
Bowen Li1, Min Li1, Chunying Lu1
1Medical College, Guizhou University, Guiyang, 550025, Guizhou, China.
Current medicinal chemistry
|January 24, 2024
概括
这项研究审查了用于识别抗冠状病毒 (ACVP) 的计算工具,这对于打击COVID-19至关重要. 机器学习加速了有效的抗病毒的发现,帮助药物和疫苗的开发.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 严重急性呼吸道综合征冠状病毒2 (SARS-CoV-2) 导致了COVID-19大流行,需要有效的抗病毒策略.
- 鉴定抗冠状病毒 (ACVP) 对于开发治疗和疫苗至关重要.
- 传统的发现方法很慢;生物信息学和机器学习提供了更快的替代方案.
研究的目的:
- 审查ACVPs的现有数据资源和预测指标.
- 分析影响ACVP识别计算模型性能的因素.
- 为ACVP发现提供生物信息学工具的有效性提供见解.
主要方法:
- 在过去二十年中,对ACVP数据库和预测器进行了全面的文献审查.
- 分析影响计算模型性能的因素.
- 对15种不同的抗病毒活性预测剂的评估.
主要成果:
- 审查了9个ACVP数据库,整合了有效的抗冠状病毒信息.
- 评估了15种针对抗病毒或特定抗冠状病毒活性的预测剂.
- 计算模型可以快速选潜在的抗病毒候选者.
结论:
- 该研究提供了对ACVP识别各种计算方法的性能的见解.
- 它强调了生物信息学工具在ACVP领域的未来方向.
- 这项工作强调了ACVP在持续打击COVID-19中的关键作用.
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