卡特格拉夫:用于混合拓的相对自由能计算的几何精确的原子映射器
Benjamin Ries1,2, Irfan Alibay2, David W H Swenson2
1Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co KG, Birkendorfer Str 65, 88397 Biberach an der Riss, Germany.
Journal of chemical theory and computation
|February 8, 2024
概括
新的几何图算法Kartograf通过使用3D坐标来增强自动化相对结合自由能 (RBFE) 计算,以在药物发现中实现更快,更强大的原子映射.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 分子建模分子建模
背景情况:
- 相对结合自由能量 (RBFE) 的计算对于药物发现中的联体优化至关重要.
- 自动化RBFE计算,特别是原子映射设置,仍然是一个重大挑战.
- 现有的二维子图异构 (SIS) 方法用于原子映射是计算密集的,可以错过几何细节.
研究的目的:
- 开发一种基于几何学的新型原子映射算法,以克服传统SIS方法的局限性.
- 提高建立大型RBFE计算的自动化和稳定性.
- 提供一个更快,更准确的方法来绘制计算化学中的联体.
主要方法:
- 开发了Kartograf,一种基于几何图的算法,利用3D原子坐标进行连接物映射.
- 将原子映射问题转换为简化的二分位图匹配问题.
- 通过对各种小分子进行RBFE计算并将结果与基于SIS的方法进行比较,验证了Kartograf.
主要成果:
- 通过利用空间关系,Kartograf有效地绕过了计算上昂贵的子图匹配.
- 该算法在处理分子对称性和立体异构性方面表现出强大.
- 对比分析显示,Kartograf是传统的基于SIS的原子映射算法的一个显著更快的替代方案.
结论:
- 在RBFE计算中,Kartograf为原子映射提供了一个计算效率高且在几何学上强大的解决方案.
- 开发的算法增强了药物发现中的联结体优化工作流程的自动化.
- Kartograf可以作为一个独立的Python包,并与OpenFE生态系统集成.
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