通过多阶段的虚拟查和实验验证实,通过可解释机器学习驱动的新型DENV NS2B-NS3蛋白酶抑制剂的识别
Shengjie Hu1,2, Yan Xiao1,2, Hailun Jiang3
1Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education, and Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang, 110016, China.
Molecular diversity
|February 4, 2026
概括
研究人员开发了机器学习模型,以寻找新的登革热药物. 他们通过预测登革热病毒NS2B-NS3蛋白酶的抑制,将化合物5确定为一个有前途的候选物.
科学领域:
- 病毒学 病毒学
- 药物发现 药物发现 药物发现
- 计算化学的计算化学
背景情况:
- 登革热是由登革热病毒 (DENV) 引起的全球重大健康问题.
- 目前没有FDA批准的抗病毒药物用于登革热治疗.
- DENV NS2B-NS3蛋白酶对于病毒复制至关重要,使其成为一种关键的药物标.
研究的目的:
- 为了识别具有针对DENV NS2B-NS3蛋白酶的抑制活性的新型小分子.
- 利用机器学习来预测潜在的抗登革热化合物.
- 通过实验分析验证有前途的候选人.
主要方法:
- 用于活动预测的机器学习模型 (RF-ECFP,XGBoost-ECFP) 的构建和优化.
- 应用夏普利添加式解释用于模型可解释性.
- 多步虚拟选,包括分子对接,动态和轨道计算.
- 使用表面等离子体共振和酶抑制试验进行实验验证.
主要成果:
- 确定了最佳的机器学习模型 (RF-ECFP,XGBoost-ECFP).
- 从一千万个分子的图书馆中发现了六种命中化合物.
- 化合物5与DENV NS2B-NS3蛋白酶活性位点进行了稳定的相互作用.
- 在实验测试中,化合物5的Kd值为31.2μM,IC50为31.44μM.
结论:
- 化合物5是DENV NS2B-NS3蛋白酶的强有力的抑制剂.
- 这项研究为开发新的登革热抗病毒疗法提供了潜在的化合物.
- 综合计算和实验方法在药物发现方面是有效的.
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