Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations.
Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations.
复制标题
基于机器学习的隐式溶剂模型，用于水溶液丙氨酸二肽分子动力学模拟。
作者：
受Noé及其同事最近开发基于机器学习的隐式溶剂模型用于模拟溶剂化多肽的工作的启发[Chen et al.，J.Chem.在这里，我们报告了另一项关于使用机器学习(ML)技术直接从显式溶剂分子动力学(MD)模拟“导出”隐式溶剂模型的可能性的研究。对于丙氨酸二肽，基于分子的DeepPot-SE表示的机器学习潜力(MLP)被训练以捕捉其与其平均溶剂环境配置(ASEC)的相互作用。计算得到的溶质上的作用力与参考值的RMSD值偏差仅为0.4kcal−1ä−1，基于该模型的自由能面与显式溶剂分子动力学模拟得到的结果不同，显式溶剂分子动力学模拟的RMSD值小于0.9kcal−1。我们的MLP训练方案还可以在ASEC环境下准确地再现量子力学(QM)溶质上的复合量子力学(QM/MM)力，从而为从头算-QM分子动力学模拟开发出精确的基于ML的隐式溶剂模型。这种基于ML的隐式溶剂模型用于质量管理计算，在培训阶段和推理阶段都具有成本效益，在培训阶段，使用ASEC减少了要标记的数据点的数量，在推理阶段，可以在计算溶质的质量管理的基础上，以相对较小的额外成本来评估MLP。在这里，我们研究了使用机器学习(ML)技术，根据显式溶剂分子动力学(MD)模拟得到的平均溶剂环境构型，“导出”一个隐含的溶剂模型。
Inspired by the recent work from Noé and coworkers on the development of machine learning based implicit solvent model for the simulation of solvated peptides [Chen et al., J. Chem. Phys., 2021, 155, 084101], here we report another investigation of the possibility of using machine learning (ML) techniques to “derive” an implicit solvent model directly from explicit solvent molecular dynamics (MD) simulations. For alanine dipeptide, a machine learning potential (MLP) based on the DeepPot-SE representation of the molecule was trained to capture its interactions with its average solvent environment configuration (ASEC). The predicted forces on the solute deviated only by an RMSD of 0.4 kcal mol−1 Å−1 from the reference values, and the MLP-based free energy surface differed from that obtained from explicit solvent MD simulations by an RMSD of less than 0.9 kcal mol−1. Our MLP training protocol could also accurately reproduce combined quantum mechanical molecular mechanical (QM/MM) forces on the quantum mechanical (QM) solute in ASEC environment, thus enabling the development of accurate ML-based implicit solvent models for ab initio-QM MD simulations. Such ML-based implicit solvent models for QM calculations are cost-effective in both the training stage, where the use of ASEC reduces the number of data points to be labelled, and the inference stage, where the MLP can be evaluated at a relatively small additional cost on top of the QM calculation of the solute. Here we investigated the use of machine learning (ML) techniques to “derive” an implicit solvent model based on the average solvent environment configurations from explicit solvent molecular dynamics (MD) simulations.
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影响因子：
12.9
作者：
Chew, Alex K.;Walker, Theodore W.;Van Lehn, Reid C.
通讯作者：
Van Lehn, Reid C.
影响因子：
4.4
作者：
Chandra, A;Ichiye, T
通讯作者：
Ichiye, T
影响因子：
4.4
作者：
BECKE, AD
通讯作者：
BECKE, AD
DOI：
10.1073/pnas.100127697
发表时间：
2000-05-23
影响因子：
11.1
作者：
Bolhuis, PG;Dellago, C;Chandler, D
通讯作者：
Chandler, D
DOI：
10.1063/5.0055522
发表时间：
2021-08-28
期刊：
The Journal of chemical physics
影响因子：
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作者：
通讯作者：
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