Electrostatic calculations are generally used in studying the thermodynamics and kinetics of biomolecules in solvent. Generally, this is performed by solving the Poisson-Boltzmann equation on a large grid system, a process known to be time consuming. In this study, we developed a deep neural network to predict the decomposed solvation free energies and forces of all atoms in a molecule. To train the network, the internal coordinates of the molecule were used as the input data, and the solvation free energies along with transformed atomic forces from the Poisson-Boltzmann equation were used as labels. Both the training and prediction tasks were accelerated on GPU. Formal tests demonstrated that our method can provide reasonable predictions for small molecules when the network is well-trained with its simulation data. This method is suitable for processing lots of snapshots of molecules in a long trajectory. Moreover, we applied this method in the molecular dynamics simulation with enhanced sampling. The calculated free energy landscape closely resembled that obtained from explicit solvent simulations.

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深度学习方法计算分子动力学模拟中的溶剂化力

静电计算通常用于研究溶剂中生物分子的热力学和动力学。通常，这是通过在大型网格系统上求解泊松-玻尔兹曼方程来执行的，这是一个众所周知的耗时过程。在这项研究中，我们开发了一个深度神经网络来预测分子中所有原子的分解溶剂化自由能和力。为了训练网络，分子的内坐标被用作输入数据，溶剂化自由能以及来自泊松-玻尔兹曼方程的变换原子力被用作标签。训练和预测任务都在 GPU 上加速。正式测试表明，当网络经过模拟数据训练良好时，我们的方法可以为小分子提供合理的预测。该方法适用于处理长轨迹中的大量分子快照。此外，我们将该方法应用于增强采样的分子动力学模拟中。计算出的自由能景观与从显式溶剂模拟中获得的自由能景观非常相似。