利用病毒基因组序列和机器学习模型来识别潜在的选择性抗病毒剂
1Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA.
Communications chemistry
|June 20, 2025
概括
这项研究将病毒基因组测序与用于抗病毒药物发现的机器学习相结合. 它通过虚拟查和体外检测成功识别了潜在的病毒选择性和泛抗病毒化合物.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 病毒学 病毒学
背景情况:
- 病毒基因组测序对于抗病毒开发至关重要.
- 针对抗病毒药物的虚拟查的机器学习集成尚未得到充分探索.
- 识别病毒选择性剂和泛抗病毒药物对于新出现的病原体至关重要.
研究的目的:
- 结合病毒基因组序列和结构数据来识别病毒选择性抗病毒药物.
- 开发定量结构-活性关系 (QSAR) 模型来预测泛抗病毒药物.
- 建立一个针对新兴病毒威胁的抗病毒药物的虚拟查框架.
主要方法:
- 病毒基因组序列与抗病毒药物的结构数据的整合.
- 开发QSAR模型来预测泛抗病毒药物 (AUC-ROC>0.79).
- 虚拟查大约36万种化合物的抗SARS-CoV-2活性.
- 在实验室验证使用伪型粒子 (PP) 输入和依赖RNA的RNA聚合酶 (RdRp) 试验.
主要成果:
- 强大的QSAR模型实现了AUC-ROC> 0.72对病毒选择性和> 0.79对泛抗病毒预测.
- 虚拟查确定了346种抗SARS-CoV-2活性化合物.
- 在体外测试中,PP输入试验的命中率为9.4%,RdRp试验的命中率为37%.
- 已识别的顶级化合物表现出大约1μM的强度.
结论:
- 这项研究为基于机器学习的虚拟查提供了有效的框架.
- 这种方法可以加速发现选择性病毒和广泛的抗病毒药物的发现.
- 这些发现适用于识别新出现的病毒性传染病的治疗方法.
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