堆叠组合学习和in-silico分析揭示了来自Moringa oleifera代谢物的双重DPP-IV和SGLT2抑制剂
M K Letuku1, M G Mohlala1, P Appiah-Kub2
1Pharmacy Department, School of Healthcare Sciences, University of Limpopo, Polokwane, South Africa.
Scientific reports
|February 18, 2026
概括
这项研究确定了来自M. oleifera的天然化合物作为糖尿病治疗中的二二酶IV (DPP-IV) 和-葡萄糖共载体2 (SGLT2) 的潜在双重抑制剂. 计算方法加速了有前途的抗糖尿病药物候选药物的发现.
科学领域:
- 计算化学和药理学计算化学和药理学
- 自然产品药物发现自然产品药物发现
- 机器学习在药物开发中的作用
背景情况:
- 糖尿病 (DM) 构成了全球健康的重大负担,特别是在资源有限的地区.
- 双重抑制DPP-IV和SGLT2通过改善胰岛素分泌和葡萄糖分泌来提供一种协同方法来控制糖尿病.
研究的目的:
- 为从M. oleifera代谢物中识别双重DPP-IV/SGLT2抑制剂开发一个in silico框架.
- 为了利用堆叠组合机器学习,分子对接和MD模拟用于药物发现.
主要方法:
- 集成的计算框架:堆叠集团ML,分子对接,MD模拟,ADMET分析.
- 使用FDA批准的抑制剂进行模型验证.
- 对M. oleifera代谢物进行LC-MS/MS分析.
主要成果:
- 堆叠组合模型实现了高预测准确性 (MCC>0.937对于DPP-IV,>0.861对于SGLT2).
- 鉴定出几种M. oleifera代谢物,包括维特辛和N,α-L-rhamnopyranosyl vincosamide,具有高预测活性和有利的特性.
- 根据结合亲和和MD模拟,N,α-L-rhamnopyranosyl vincosamide成为一种潜在的双重DPP-IV/SGLT2抑制剂.
结论:
- 梅是多重目标抗糖尿病化合物的宝贵来源.
- 堆叠组合学习有效地加速了基于天然产品的糖尿病药物发现.
- 这些已识别的化合物显示出开发新型抗糖尿病疗法的前景.
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