集成机器学习驱动的QSAR建模和系统生物学方法用于识别潜在的SARS-CoV-2 3CLpro抑制剂
A Manaithiya1,2, R Bhowmik1, R Ray1
1Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
SAR and QSAR in environmental research
|November 19, 2025
概括
这项研究开发了一个计算框架来识别SARS-CoV-2 3C类蛋白酶 (3CLpro) 抑制剂. 该方法通过预测化合物生物活性和分析宿主-病原体相互作用,加速发现潜在的抗病毒药物.
科学领域:
- 计算化学是一种计算化学.
- 系统生物学 系统生物学
- 药物发现 药物发现
背景情况:
- SARS-CoV-2 3C样蛋白酶 (3CLpro) 是病毒复制的关键酶,也是关键的治疗点.
- 为3CLpro开发有效的小分子抑制剂对于抗病毒药物开发至关重要.
研究的目的:
- 创建一个集成的计算框架,用于识别和评估潜在的3CLpro抑制剂.
- 加速发现针对SARS-CoV-2的新型抗病毒剂.
主要方法:
- 利用化学信息学构建定量结构-活性关系 (QSAR) 模型,使用919种化合物的精选数据集.
- 采用分子对接和动力学模拟来验证QSAR预测并了解原子级相互作用.
- 进行系统生物学分析,包括基因本体学和KEGG通路丰富,以评估宿主-病原体相互作用.
主要成果:
- 开发了具有高准确性的预测QSAR模型 (R2train=0.9736,R2test=0.7413).
- 创建了一个基于网络的工具,3CLpro-Pred,用于快速预测化合物的生物活性.
- 确定了由潜在抑制剂调节的关键宿主调节基因 (例如,TBK1,PIK3CA),将药物作用与抗病毒和免疫反应联系起来.
结论:
- 组合计算管道有效地加速了3CLpro抑制剂的发现.
- 提供了对宿主-病原体界面药物作用的机制性见解.
- 创建了一个有前途的候选药物列表,以便进一步进行实验验证.
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