治疗缺血性中风的多目标天然化合物:深度学习预测和实验验证的整合
Junyu Zhou1,2, Chen Li2, Yu Yue2
1Institute of Advanced Clinical Medicine, Peking University, Beijing 100191, China.
Journal of chemical information and modeling
|March 14, 2025
概括
研究人员开发了一个深度学习药物发现管道,以找到用于缺血性中风治疗的天然化合物. 四种化合物,特别是mulberrin和ellagic酸,在实验室测试中显示出显著的神经保护作用.
科学领域:
- 神经科学是一个神经科学.
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
背景情况:
- 缺血性中风治疗由于复杂的病理生理学而受到限制.
- 需要多目标治疗方法.
研究的目的:
- 开发一种深度学习框架,用于识别具有对缺血性中风神经保护性质的自然化合物 (NC).
- 通过计算预测和实验验证多目标NCs.
主要方法:
- 集成的深度学习 (SELFormer) 与药物发现的实验验证.
- 使用IC50预测,QSAR建模,UMAP和分子对接.
- 在氧-葡萄糖剥夺下对PC12细胞进行了体外研究.
主要成果:
- 确定了57个高活性和11个中活性NC.
- 四种化合物 (费鲁醇葡萄糖,l-基-l-三,mulberrin,酸) 显示出显著的神经保护作用.
- 穆尔贝林和基酸在减少氧化应激,炎症和增强神经营养信号传递方面表现出卓越的疗效.
结论:
- 建立了一个基于深度学习的框架,用于识别多目标自然疗法.
- 穆尔贝林和酸是缺血性中风治疗开发的有希望的候选人.
- 综合计算预测和实验验证加速了神经系统疾病的药物发现.
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