TorchANI-Amber:将神经网络潜力与经典生物分子模拟相结合
Ignacio Pickering1, Jonathan A Semelak2,3, Jinze Xue1
1Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States.
The journal of physical chemistry. B
|November 10, 2025
概括
TorchANI-Amber能够使用先进的机器学习潜力进行分子动力学模拟. 这种接口将人工神经网络 (ANI) 潜力与珀软件集成,增强生物分子模拟.
科学领域:
- 计算化学是一种计算化学.
- 生物物理学的生物物理.
- 在科学领域的机器学习.
背景情况:
- 分子动力学 (MD) 模拟对于理解生物分子系统至关重要.
- 传统的力场在准确性和可转移性方面存在局限性.
- 机器学习潜力,如ANI,为准确的能源预测提供了一个有希望的替代方案.
研究的目的:
- 将TorchANI-Amber引入,这是一个将ANI机器学习潜力集成到Amber MD模拟套件中的接口.
- 以高精度使用神经网络潜力实现常规生物分子模拟.
- 为了证明各种生物分子系统的接口的可扩展性和性能.
主要方法:
- 在珀软件套件 (sander和pmemd引擎) 中集成ANI神经网络潜力.
- 实现优化的CUDA例程,以实现高效的特征向量计算.
- 扩展接口以支持其他能源预测潜力 (AIMNet2,胡桃).
- 在显式溶剂中对生物分子系统 (ubiquitin,Trp-cage) 进行MD模拟.
主要成果:
- TorchANI-Amber成功地将ANI潜力集成到Amber中,支持所有Amber功能.
- 模拟显示了生物分子系统的良好能量保存和稳定性.
- 接口使大规模模拟 (数以十万计的原子) 在近DFT准确度.
- 在增强的采样技术中成功应用,如复制交换MD.
结论:
- TorchANI-Amber提供了一个使用机器学习潜力的生物分子MD模拟的多功能和高效平台.
- 该接口可方便在大型模拟中使用高精度神经网络潜力,接近DFT精度.
- 这项工作推动了机器学习在计算生物物理学和药物发现中的应用.
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