StrEAMM-Thioether:对Thioether-Linked循环的高效结构预测
Minh Ngoc Ho1, Jiayuan Miao1, Yi Shan2
1Department of Chemistry, School of Arts and Sciences, Tufts University, Medford, Massachusetts 02155, United States.
The journal of physical chemistry. B
|February 6, 2026
概括
我们开发了一种机器学习方法来预测循环结构,改善药物发现. 这种计算工具准确地模拟了与结合的循环,有助于设计新疗法.
科学领域:
- 计算化学是一种计算化学.
- 生物物理学的生物物理.
- 药品化学 药品化学 是一个
背景情况:
- 循环是一种有前途的治疗药物,向蛋白质-蛋白质相互作用和细胞膜.
- 预测循环结构组合对于合理的药物设计至关重要,但在实验上具有挑战性.
- 像分子动力学 (MD) 这样的现有计算方法是计算密集的.
研究的目的:
- 扩展StrEAMM (通过分子动力学和机器学习实现的结构合集) 平台,用于以太结合的循环.
- 开发一种快速而准确的计算方法,用于预测乙烯结合循环的结构组合.
- 促进设计和发现与mRNA显示查兼容的循环疗法.
主要方法:
- 在StrEAMM框架内开发和训练图形神经网络模型.
- 应用这些模型来预测乙烯结合的循环的结构组合.
- 使用溶液核磁共振 (NMR) 实验合成和表征预测的循环.
主要成果:
- StrEAMM-thioether模型准确地预测了与thioether结合的循环的结构组合.
- 根据模型预测,确定了四种与乙烯结合的循环五,结构良好.
- 实验性NMR特征表现出与计算预测的结构普遍一致.
结论:
- StrEAMM-thioether模型提供了一种快速可靠的方法来预测循环结构组合.
- 这种方法简化了循环疗法的设计和发现.
- 这些模型可以与mRNA显示平台集成,以实现高效的药物发现.
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