机器学习潜力的基本稳定性测试用于计算药物发现中的分子模拟
Kavindri Ranasinghe1,2, Adam L Baskerville2, Geoffrey P F Wood1,2
1Recursion, Schrödinger Building, Oxford Science Park, Oxford OX4 4GE, U.K.
Journal of chemical information and modeling
|August 27, 2025
概括
神经网络潜能提供快速的分子模拟, 但需要仔细的测试. 这项研究评估了各种模型,发现稳定性和准确性的显著差异,强调了在应用中需要严格的选择.
科学领域:
- 计算化学
- 材料科学
- 机器学习
背景情况:
- 在量子力学数据上训练的神经网络潜能 (NNP) 提供了高效的分子相互作用计算.
- 然而,NNP可以表现出不稳定性,非物理行为或不充分的准确性,限制它们在分子模拟中的使用.
研究的目的:
- 为分子模拟系统地评估各种神经网络潜力的可靠性和性能.
- 使用一致的测试框架评估模型架构对NNP性能的影响.
主要方法:
- 在八个内部NNP (ANI-2x和MACE架构) 和四个公共NNP (ANI-2x,ANI-1ccx,MACE-OFF23,AIMNet2) 上进行了气体和凝结相稳定性测试.
- 对基准分子和分子动力学模拟进行正常模式分析,以确定不稳定性和非物理行为.
- 评估了蛋白质 - 连接体相互作用能量,并将结果与实验结合亲和度和其他计算方法进行了比较.
主要成果:
- 观察到模型性能的显著变化;一些MACE模型在模拟和硬体碰撞期间显示不稳定性.
- 发表的ANI-2x和一个内部MACE模型无法准确地表示液态水,形成固态.
- ANI-1ccx在凝结水中表现出非物理能量最小值,导致相变.
- 一个内部NNP与标准模型相比,与实验水结构有更好的一致性.
- 大多数NNP与实验结合亲缘关系的相关性比对接得分更好,ANI-2x接近DFT的准确性.
结论:
- 没有一个NNP是普遍适用的;模型架构和训练数据显著影响性能.
- 在不同阶段和条件下进行严格的测试对于选择可靠的NNP至关重要.
- 在NNP培训和选择过程中仔细考虑是成功的现实应用的关键.
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