阿尔海姆德:在自动化相对约束自由能源工作流程中桥梁可访问性和准确性
Runduo Liu1, Yilin Zhong1, Yufen Yao1,2
1State Key Laboratory of Anti-Infective Drug Discovery and Development, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.
Journal of chemical theory and computation
|December 31, 2025
概括
在桌面工作站上,ALCHEMD自动化了药物蛋白结合亲和度的自由能量扰动 (FEP) 计算. 这种开源平台使准确的预测可访问,加速药物发现.
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:
- 化学自由能量扰动 (FEP) 对于预测药物与蛋白质的结合亲和力来说是准确的.
- 由于高的计算成本和技术复杂性,FEP的采用受到限制.
研究的目的:
- 介绍ALCHEMD,一个用于相对约束自由能量 (FEP-RBFE) 计算的自动化平台.
- 在使用商品GPU的桌面工作站上启用FEP-RBFE预测.
主要方法:
- ALCHEMD集成了自动化预处理,共同结构映射,组合结构FEP和融合适应性回程算法.
- 该平台利用智能算法进行连接体选择,参数化和自适应采样.
主要成果:
- 在GPU上,ALCHEMD每天可以实现10-20个药物蛋白结合预测.
- 基准测试显示了可比的精度 (MUE = 0.86 kcal/mol) 与显著减少的模拟时间 (29.3 ns/对).
结论:
- 阿尔希姆德使FEP计算民主化,使其超出专家组的范围.
- 该平台通过高效和自动化的结合亲和力预测来加速药物发现工作流.
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