溶液中相对自由能计算的可重复性与ANI-2x和MACE-OFF23的解决方案
Sara Tkaczyk1,2, Thierry Langer1, Marcus Wieder3
1Department of Pharmaceutical Sciences, Pharmaceutical Chemistry Division, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria.
Journal of chemical theory and computation
|December 17, 2025
概括
神经网络潜力 (NNP) 显示出对炼金术自由能计算的前景. 然而,在冷凝相模拟中仔细评估NNP性能对于可靠的结果至关重要,因为ANI-2x和MACE-OFF23之间的性能差异很小.
科学领域:
- 计算化学的计算化学
- 分子建模分子建模
- 物理化学 物理化学
背景情况:
- 炼金术自由能计算对于预测分子性质至关重要.
- 神经网络潜力 (NNP) 为传统的力场提供了一个有希望的,可转移的替代方案.
- 评估NNP对复杂模拟的适用性,特别是在凝缩阶段,仍然是一个活跃的研究领域.
研究的目的:
- 调查使用NNP用于化学自由能量计算的可行性和挑战.
- 为了评估不同的NNP (ANI-2x和MACE-OFF23 ((小)) 在预测分体状态方面的表现.
- 开发和测试一种方法,以使用能量混合和假原子在状态之间进行平滑的插值.
主要方法:
- 采用单坐标双拓方法进行化学自由能计算.
- 通过选择性掩盖涉及模拟原子的相互作用来利用能量混合,以进行状态插值.
- 用ANI-2x和MACE-OFF23 ((小) NNPs测试了框架,以测试水溶液中 tautomer 自由能量差异.
主要成果:
- MACE-OFF23 ((小) 产生了趋同的自由能源结果.
- 使用ANI-2x进行的模拟显示出显著的变化和收问题.
- ANI-2x的性能受到缓慢的水动力学和溶液过度稳定性的阻碍,导致采样困难.
结论:
- 可转移的NNP具有优势,但需要在缩相环境中严格验证.
- 选择NNP显著影响免费能源计算的可靠性.
- 在在自由能量模拟中应用之前,仔细评估NNP动态和潜在的转稳态是必不可少的.
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