Due to the limitations of existing experimental methods for capturing stereochemical molecular data, there usually is an inherent level of uncertainty present in models describing the conformation of macromolecules. This uncertainty can originate from various sources and can have a significant effect on algorithms and decisions based upon such models. Incorporating uncertainty in state-of-the-art visualization approaches for molecular data is an important issue to ensure that scientists analyzing the data are aware of the inherent uncertainty present in the representation of the molecular data. In this work, we introduce a framework that allows biochemists to explore molecular data in a familiar environment while including uncertainty information within the visualizations. Our framework is based on an anisotropic description of proteins that can be propagated along with required computations, providing multiple views that extend prominent visualization approaches to visually encode uncertainty of atom positions, allowing interactive exploration. We show the effectiveness of our approach by applying it to multiple real-world datasets and gathering user feedback.

由于用于捕获立体化学分子数据的现有实验方法的局限性，在描述大分子构象的模型中通常存在固有的不确定性水平。这种不确定性可能来自各种来源，并对基于此类模型的算法和决策产生重大影响。在分子数据的最先进可视化方法中加入不确定性是一个重要问题，以确保分析数据的科学家意识到分子数据表示中存在的固有不确定性。在这项工作中，我们引入了一个框架，允许生物化学家在熟悉的环境中探索分子数据，同时在可视化中包含不确定性信息。我们的框架基于蛋白质的各向异性描述，可以与所需的计算一起传播，提供多个视图，扩展突出的可视化方法，以可视化编码原子位置的不确定性，允许交互式探索。我们通过将其应用于多个真实世界的数据集并收集用户反馈来展示我们方法的有效性。