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预测药物样分子的亚特罗皮索默性
Ty Balduf1, Philip A Gerken1, Mee Y Shelley2
1Schrödinger, Inc., 1540 Broadway, Floor 24, New York 10036, New York, United States.
Journal of chemical information and modeling
|January 16, 2026
概括
一个新的计算工作流程使用计算的屏障高度准确地将有机分子分类为atropisomer类. 这种方法通过提供快速而精确的分析分子结构的工具来增强药物发现.
科学领域:
- 计算化学是一种计算化学.
- 有机化学 有机化学
- 药物发现 药物发现
背景情况:
- 亚特罗皮索默是可以通过单键旋转相互转换的异构体.
- 精确的类别对药物发现和开发至关重要.
- 目前用于亚特罗皮索默分类的方法可能耗时且计算成本昂贵.
研究的目的:
- 开发一个多步骤的计算工作流程,以准确地分类体.
- 在药物发现的计算化学中实现速度和准确性之间的平衡.
主要方法:
- 使用了多步计算工作流程,涉及力场 (OPLS4) 和神经网络 (QRNN-TB) 能量扫描.
- 使用过渡状态搜索和密度函数理论 (DFT) 计算 (ωB97X-D3/def2-TZVP(-f)).
- 与高级计算方法 (ωB97M-V/cc-pVTZ和DLPNO-CCSD) /def2-TZVPP) 进行基准准确度.
主要成果:
- 开发了一种自动化协议,用于将有机分子分配到三个类型的亚特罗皮索默.
- 在65个分子的测试组中,成功率超过90%.
- 在基准测试预测旋转障碍时表现出良好的相关性.
结论:
- 计算工作流提供了一个准确和高效的方法,用于类别的atropisomer.
- 开发的协议适用于药物发现计划中的高通量查.
- 这种方法为实际应用提供了有希望的速度和准确性的平衡.
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