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发现基于四化类昆的SARS-CoV-2酶抑制剂与代,深度学习增强的虚拟查

Alma C Castañeda-Leautaud¹, Ambuj Srivastava², Eunjung Kim³

¹Department of Chemistry & Biochemistry, University of California, La Jolla, California 92093-0533, United States.

Journal of chemical information and modeling

|December 16, 2025

概括

我们使用人工智能增强的虚拟查来发现SARS-CoV-2的螺旋酶抑制剂. 一个深度神经网络改善了命中识别,导致了像MWAC-3429.9这样的有希望的抗病毒化合物.

科学领域:

药物的发现和开发.
计算化学是一种计算化学.
病毒学病毒学

背景情况:

在抗病毒药物研发过程中,SARS-CoV-2螺旋酶 (Nsp13) 是一个关键的标.
之前的基于结构的药物发现工作需要有效的选方法.

研究的目的:

通过人工智能增强的虚拟查来识别新的SARS-CoV-2螺旋酶抑制剂.
探索3 - -1,2,3,4-四二诺 (THIQ) 衍生物的抗病毒潜力.

主要方法:

三轮基于结构的虚拟选 (VS) 采用人工智能 (AI).
在第三轮VS中使用了深度神经网络 (DNN) 来优先考虑分子.
用分子动力学模拟来阐明结合机制.

主要成果:

人工智能增强的VS提高了21%的命中识别效率.
确定了六个具有选择性指数> 3 的有前途的命中.
一种THIQ衍生物MWAC-3429显示出强大的基于细胞的抗病毒活性 (EC50 = 5.4μM),没有细胞毒性.
为THIQ化型提出了NSP13酶的新型全抑制机制.

结论:

人工智能驱动的VS有效用于识别SARS-CoV-2直酶抑制剂.
THIQ衍生品代表了针对SARS-CoV-2的有前途的抗病毒药物类别.
了解全抑制机制可以指导未来的药物设计.

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