Transferring Knowledge from MM to QM: A Graph Neural Network-Based Implicit Solvent Model for Small Organic Molecules.

IF 5.5 1区 化学 Q2 CHEMISTRY, PHYSICAL
Paul Katzberger,Felix Pultar,Sereina Riniker
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引用次数: 0

Abstract

The conformational ensemble of a molecule is strongly influenced by the surrounding environment. Correctly modeling the effect of any given environment is, hence, of pivotal importance in computational studies. Machine learning (ML) has been shown to be able to model these interactions probabilistically, with successful applications demonstrated for classical molecular dynamics. While first instances of ML implicit solvents for quantum-mechanical (QM) calculations exist, the high computational cost of QM reference calculations hinders the development of a generally applicable ML implicit solvent model for QM calculations. Here, we present a novel way of developing such a general machine-learned QM implicit solvent model by transferring knowledge obtained from classical interactions to QM, emulating a QM/MM setup with electrostatic embedding and a nonpolarizable MM solvent. This has the profound advantages that neither QM/MM reference calculations nor experimental data are required for training and that the obtained graph neural network (GNN)-based implicit solvent model (termed QM-GNNIS) is compatible with any functional and basis set. QM-GNNIS is currently applicable to small organic molecules and describes 39 different organic solvents. The performance of QM-GNNIS is validated on NMR and IR experiments, demonstrating that the approach can reproduce experimentally observed trends unattainable by state-of-the-art implicit-solvent models paired with static QM calculations.
从MM到QM的知识传递:基于图神经网络的有机小分子隐式溶剂模型。
分子的构象集合受周围环境的强烈影响。因此,正确地模拟任何给定环境的影响在计算研究中是至关重要的。机器学习(ML)已被证明能够对这些相互作用进行概率建模,并成功应用于经典分子动力学。虽然存在用于量子力学(QM)计算的ML隐式溶剂的第一个实例,但QM参考计算的高计算成本阻碍了普遍适用于QM计算的ML隐式溶剂模型的发展。在这里,我们提出了一种新的方法,通过将从经典相互作用中获得的知识转移到QM中,模拟具有静电嵌入和非极化MM溶剂的QM/MM设置,来开发这样一个通用的机器学习QM隐式溶剂模型。这具有深刻的优势,既不需要QM/MM参考计算也不需要实验数据进行训练,并且所获得的基于图神经网络(GNN)的隐式溶剂模型(称为QM- gnnis)与任何功能集和基集兼容。QM-GNNIS目前适用于小有机分子,描述了39种不同的有机溶剂。QM- gnnis的性能在核磁共振和红外实验中得到验证,表明该方法可以再现实验观察到的趋势,这是最先进的隐式溶剂模型与静态QM计算相结合所无法实现的。
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来源期刊
Journal of Chemical Theory and Computation
Journal of Chemical Theory and Computation 化学-物理:原子、分子和化学物理
CiteScore
9.90
自引率
16.40%
发文量
568
审稿时长
1 months
期刊介绍: The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.
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