在GPU上加速GROMACS自由能量扰动计算
ACS omega
|June 16, 2025
概括
我们使用图形处理单元 (GPU) 开发了一种更快的方法来预测分子与蛋白质的结合强度. 这大大加快了自由能量扰动 (FEP) 的计算速度,使药物发现更有效率.
科学领域:
- 计算化学是一种计算化学.
- 分子动力学分子动力学
- 药物发现 药物发现
背景情况:
- 自由能量扰动 (FEP) 的计算对于预测联体蛋白结合亲缘关系至关重要.
- 由于计算时间长且工作流程复杂,FEP的广泛使用受到限制.
研究的目的:
- 在GROMACS.中引入一个优化的GPU-resident FEP实现.
- 为了加速药物发现管道的FEP计算.
主要方法:
- 在GROMACS软件中实现和优化了GPU居民的FEP计算.
- 在一个基准系统上验证了支持GPU的FEP实现,该基准系统使用了8个配体-蛋白质对,包括充电配体.
- 在Nvidia A100和MetaX C500 GPU平台上测试了性能.
主要成果:
- 用GPU加速的FEP计算取得了很好的一致性 (约. 1.0 kcal/mol) 与 CPU 计算的绝对结合自由能量的结果.
- 与32核CPU相比,Nvidia A100上的加速率高达~800%,MetaX C500上的速度高达~400%.
- 在A100 GPU上,基准系统的计算时间从400小时减少到48小时.
结论:
- 在GPU-FEP实现提供了大量加快炼化自由能计算.
- 这一进步提供了一种快速,高效和准确的方法来预测联结蛋白结合的自由能量.
- 有一个开源的工作流 (FEP-on-GPU) 可用,促进在计算化学和药物发现更广泛的采用.
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