Acceleration of the GROMACS Free-Energy Perturbation Calculations on GPUs

Free-energy perturbation
(FEP) calculations have emerged
as a promising
tool for the accurate prediction of ligand binding affinities. However,
their widespread adoption in drug discovery pipelines has been hindered
by long computation times and complex workflow setups. Here, we introduce
an optimized graphics processing unit (GPU)-resident FEP implementation
within GROMACS. The GPU-enabled FEP calculations are validated on
a benchmark system containing eight ligand–protein pairs, including
two charged ligands, on both the Nvidia A100 and the MetaX C500 GPU
platforms. The absolute binding free energies predicted on these GPUs
show excellent agreement (around 1.0 kcal/mol) with previous CPU-computed
results. Compared to a 32-core CPU implementation, the GPU-accelerated
FEP calculations demonstrate significant speed-ups, with up to nearly
800 and 400% improvements on Nvidia A100 and MetaX C500 GPUs, respectively.
The end-to-end absolute binding free-energy calculations for the benchmark
systems are reduced from 400 h to around 48 h on the A100 GPU. These
advancements aim to provide the alchemical free-energy community with
a fast and efficient way of conducting FEP calculations, thereby paving
the way for a highly accurate and computationally efficient solution
in predicting ligand–protein binding free energies. All codes,
data, and scripts are included in our open-source project, FEP-on-GPU
workflow, freely available at https://github.com/yiqichenshallwetalk/FEP-on-GPU-Workflow.

自由能微扰（Free-energy perturbation, FEP）计算已成为精准预测配体结合亲和力的极具前景的工具。然而，其在药物发现流程中的广泛应用却因计算耗时过长、工作流程配置复杂而受到阻碍。本研究提出一种经优化的、适配图形处理器（Graphics Processing Unit, GPU）驻留模式的FEP实现方案，集成于GROMACS软件中。我们在英伟达（Nvidia）A100与美达思（MetaX）C500两款GPU平台上，基于包含8对配体-蛋白（含2个带电配体）的基准测试系统，对该GPU加速FEP计算方案进行了验证。在这两款GPU上预测得到的绝对结合自由能，与此前基于CPU计算的结果具有极佳的一致性，误差约为1.0 kcal/mol。与32核CPU的实现方案相比，该GPU加速的FEP计算展现出显著的加速效果：在英伟达A100与美达思C500 GPU上的加速比分别可达近800%与400%。在A100 GPU上，基准测试系统的端到端绝对结合自由能计算时长从400小时缩短至约48小时。本研究的这些进展旨在为炼金术式自由能计算领域的研究者提供一套高效便捷的FEP计算方案，从而为精准且高效的配体-蛋白结合自由能预测方案铺平道路。本研究的所有代码、数据与脚本均包含在开源项目FEP-on-GPU Workflow中，可通过https://github.com/yiqichenshallwetalk/FEP-on-GPU-Workflow免费获取。