Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets.

中文翻译：

---

分子动力学模拟数据相互作用指纹的系统分析、聚合和可视化

分子对接或分子动力学（MD）模拟等计算方法已被开发来模拟和探索生物分子之间的相互作用。然而，使用这些方法获得的相互作用很难分析和评估。交互指纹 (IFP) 已被提出用于从静态 3D 坐标导出交互并将其转换为 1D 位向量。最近，这一概念已被应用于从MD模拟中导出IFP，MD模拟通过添加系统的时间运动和动态而增加了一层复杂性。因此，许多 IFP 是从一次 MD 模拟中获得的，导致大量单独的 IFP 与从静态 3D 结构导出的 IFP 相比难以分析。科学贡献：我们引入了一种新方法来系统地聚合从 MD 模拟数据得出的 IFP。此外，我们提出可视化来有效地分析和比较从 MD 模拟数据导出的 IFP，以解释交互的时间演变，并比较不同 MD 模拟的 IFP。它已作为免费的 Python 库实现，因此可以轻松地被其他研究人员采用并应用于不同的 MD 模拟数据集。