Complex molecular simulation methods are typically required to calculate the thermodynamic properties of biochemical systems. One example thereof is the thermodynamic profiling of (de)solvation of proteins, which is an essential driving force for protein-ligand and protein-protein binding. The thermodynamic state of water molecules depends on its enthalpic and entropic components; the latter is governed by dynamic properties of the molecule. Here, we developed, to the best of our knowledge, two novel machine learning methods based on deep neural networks that are able to generate the converged thermodynamic state of dynamic water molecules in the heterogeneous protein environment based solely on the information of the static protein structure. The applicability of our machine learning methods to predict the hydration information is demonstrated in two different studies, the qualitative analysis and quantitative prediction of structure-activity relationships, and the prediction of protein-ligand binding modes.

通常需要复杂的分子模拟方法来计算生化系统的热力学性质。其中一个例子是蛋白质（去）溶剂化的热力学分析，这是蛋白质-配体和蛋白质-蛋白质结合的重要驱动力。水分子的热力学状态取决于其焓和熵分量；后者由分子的动态特性控制。在这里，据我们所知，我们开发了两种基于深度神经网络的新型机器学习方法，这些方法能够仅基于静态蛋白质结构的信息在异质蛋白质环境中生成动态水分子的收敛热力学状态。我们的机器学习方法预测水合信息的适用性在两项不同的研究中得到了证明：结构-活性关系的定性分析和定量预测，以及蛋白质-配体结合模式的预测。