基于晶网络的建议:用于虚拟查药物-药物晶的多目标随机步行网络模型
Wenxiang Song1, Juan Wu2, Changda Zhou2
1Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
Journal of medicinal chemistry
|October 16, 2025
概括
一个新的计算模型,CCNBR,有效地选药物-药物共晶体 (DDC). 它确定了有前途的候选药物,包括一种具有增强治疗效益和生物可用性的Furosemide-Telmisartan联合晶体.
科学领域:
- 计算化学和材料科学计算化学和材料科学
- 制药科学和药物发现.
背景情况:
- 药物-药物共晶体 (DDC) 提供协同治疗潜力,但缺乏有效的选方法.
- 需要系统的计算方法来识别有前途的DDC候选药物开发.
研究的目的:
- 引入CCNBR,一个新的多目标随机步行网络模型用于DDC查.
- 评估使用CCNBR.BR.的抗高血压药物的共晶形成潜力.
主要方法:
- 开发了CCNBR,整合了共晶网络拓和分子结构特征.
- 采用基于第三阶路径的加权随机步行算法来捕捉超分子相互作用.
- 将CCNBR应用于15种抗高血压药物,实验性测试105种组合.
主要成果:
- CCNBR成功预测了共晶体的形成,确定了两种药物对.
- 富洛塞米德-泰尔米萨坦共晶的排名很高,并显示出更好的溶解性和生物可用性.
- 实验验证证了CCNBR在识别可行的DDC候选者的有效性.
结论:
- CCNBR为DDC选提供了一种快速有效的计算方法.
- 鉴定到的Furosemide-Telmisartan共晶体显示出改善治疗结果的显著潜力.
- 这种方法加快了发现具有增强药物特性的新型DDC的速度.
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