网络药理学和机器学习确定了黄类药物作为潜在的老年疗法
Jose Alberto Santiago-de-la-Cruz1,2, Nadia Alejandra Rivero-Segura1, María Elizbeth Alvarez-Sánchez2
1Dirección de Investigación, Instituto Nacional Geriatría (INGER), Mexico City 10200, Mexico.
Pharmaceuticals (Basel, Switzerland)
|August 28, 2025
概括
通过计算方法确定了714种潜在的老年治疗化合物,包括18种新型黄,通过向细胞衰老及其相关分泌表型 (SASP) 来对抗与年龄相关的疾病.
科学领域:
- 老年学
- 计算生物学
- 药理学
背景情况:
- 细胞衰老的特征是细胞循环停止和前炎性SASP,与衰老和慢性疾病有关.
- 老年治疗药物,包括老年化药物和老年化药物,为与年龄相关的疾病提供潜在的治疗方法.
- 网络药理学和机器学习等计算方法加速了新药的发现.
研究的目的:
- 使用计算方法识别潜在的老年治疗化合物.
- 分析与细胞衰老相关的药物蛋白相互作用.
- 对抗衰老的治疗潜力进行分类.
主要方法:
- 用于细胞衰老的药物蛋白相互作用网络的构建.
- 机器学习模型 (随机森林,SVM,KNN) 的应用用于化合物分类.
- 对化合物的治疗潜力和药用化学特性进行评估.
主要成果:
- 发现了714种可能具有老年治疗作用的化合物.
- 选择了270种具有良好的药用化学特性的化合物.
- 开发一个互动的网络工具,
结论:
- 在已知的老年治疗药物中,黄类是最为丰富的类别.
- 鉴定出了18种新的黄类药物,之前没有报告过其老年治疗活性.
- 这项研究为老年治疗药物发现提供了宝贵的资源.
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