开发和应用一种解性预测剂,用于发现新的解性化合物和草药
Jinjun Li1,2, Kai Zhao3, Guotai Yang3
1State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China.
Molecules (Basel, Switzerland)
|June 27, 2025
概括
研究人员开发了一种机器学习模型,以预测 senolytic 化合物,清除衰老细胞. 该模型确定了新型药物候选物和草药,实验验证显示了模型生物中的老化活性和寿命延长.
科学领域:
- 生物遗传学 生物遗传学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 细胞衰老是衰老和与年龄有关的疾病的关键驱动因素.
- 开发老化化合物来消除衰老细胞至关重要,但受到已知的代理和机制的限制.
- 识别新型老化药物需要先进的预测工具.
研究的目的:
- 利用表型数据开发基于机器学习的老化预测器.
- 识别新型老化化合物和潜在的治疗点.
- 通过实验查和药物重新使用来验证预测模型.
主要方法:
- 策划了111个阳性和3951个阴性解化合物的训练数据集.
- 训练有素的机器学习模型 (SVM,MLP) 使用分子指纹,描述符和MoLFormer嵌入.
- 使用表现最佳的模型对DrugBank和TCMbank数据库进行虚拟选.
- 进行了路径丰富分析和预测化合物的实验验证.
主要成果:
- 使用MoLFormer嵌入式,通过SVM和MLP模型 (AUC> 0.997,F1> 0.941) 实现了高性能.
- 确定了DrugBank的98种新型候选化合物和TCMbank的714种新型候选化合物,其中包括81种药草.
- 帕纳克萨醇在体外显示出老化活性;沃克洛斯波林在C. elegans中延长了寿命.
- 路径分析揭示了潜在的老化机制和目标.
结论:
- 开发的老化预测剂是发现新型老化剂的有效框架.
- 已识别的化合物和草药可以作为 senolytic 药物开发的有价值的起点.
- 该模型展示了药物重新利用和推进衰老研究的潜力.
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