药物发现的自动绝对约束自由能量计算工作流程
Benjamin Ries1, Irfan Alibay2, Nithishwer Mouroug Anand2
1Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str 65, 88397 Biberach an der Riss, Germany.
Journal of chemical information and modeling
|July 2, 2024
概括
本研究介绍了一种自动化的Python工作流程,用于计算绝对结合自由能量 (ABFEs),以加速药物发现. 该系统对潜在的候选药物进行排名,简化了选择有利化合物的过程.
科学领域:
- 计算化学是一种计算化学.
- 药物的发现和开发.
背景情况:
- 绝对结合的自由能量 (ABFEs) 对于药物开发中的发现至关重要.
- 之前的研究表明,in silico预测对于对候选药物进行排名的有用性.
研究的目的:
- 为高效的ABFE计算开发一个自动化的Python工作流.
- 为了尽量减少药物发现管道中的手工工作.
主要方法:
- 使用Python和Snakemake进行工作流自动化和任务管理.
- 输入:受体 PDB 和带 SDF 文件.
- 输出: .tsv 文件,包含排序联体及其ABFE.
主要成果:
- 展示了一个自动化工作流来计算ABFEs,用户输入最小.
- 根据它们的结合自由能量成功排列了连接体.
- 使用基准系统验证工作流的有效性.
结论:
- 自动化工作流大大减少了ABFE计算中的手工工作量.
- 这种方法通过排名和推有利的候选药物来支持高效的药物发现.
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