Accurate modeling of the solvent environment for biological molecules is crucial for computational biology and drug design. A popular approach to achieve long simulation time scales for large system sizes is to incorporate the effect of the solvent in a mean-field fashion with implicit solvent models. However, a challenge with existing implicit solvent models is that they often lack accuracy or certain physical properties compared to explicit solvent models, as the many-body effects of the neglected solvent molecules is difficult to model as a mean field. Here, we leverage machine learning (ML) and multi-scale coarse graining (CG) in order to learn implicit solvent models that can approximate the energetic and thermodynamic properties of a given explicit solvent model with arbitrary accuracy, given enough training data. Following the previous ML--CG models CGnet and CGSchnet, we introduce ISSNet, a graph neural network, to model the implicit solvent potential of mean force. ISSNet can learn from explicit solvent simulation data and be readily applied to MD simulations. We compare the solute conformational distributions under different solvation treatments for two peptide systems. The results indicate that ISSNet models can outperform widely-used generalized Born and surface area models in reproducing the thermodynamics of small protein systems with respect to explicit solvent. The success of this novel method demonstrates the potential benefit of applying machine learning methods in accurate modeling of solvent effects for in silico research and biomedical applications.
翻译:对生物分子的溶剂环境进行精确的建模对于计算生物学和药物设计至关重要。对于大规模系统规模而言,实现长模拟时间尺度的长期模拟系统规模的流行方法,是以隐含溶剂模型将溶剂的作用以平均方式纳入隐含溶剂模型;然而,现有隐含溶剂模型的难题是,与显性溶剂模型相比,它们往往缺乏准确性或某些物理特性,因为被忽视溶剂分子的多种体效应很难作为一个中位领域进行建模。在这里,我们利用机器学习(ML)和多尺度粗皮质颗粒(CG),以便学习能够任意精确地接近某一明确溶剂模型的能量和热力特性的隐含溶剂模型。根据以前的ML-CG模型CGnet和CGSchnet,我们采用隐性溶剂模型,我们引入了SISNet,即一个图形神经网络,以模拟中被忽略的溶剂潜力。国际空间站网可以从明确的溶剂模拟数据中学习,并很容易应用于MDMDM模拟。我们将两种硬质溶剂系统在不同的溶剂溶剂处理中的溶剂和热质溶剂应用的溶剂模型的分解分解分解分解分解分解模型,并广泛地将这一模型在生产中,在生产地面系统上,从而展示地研研磨制的研磨制的研磨制的研磨制的研磨制,从而将这种研磨法的研磨制的研磨法的研磨法的研磨法的研磨。