## 2 Related Work
### Pocket Calculation  
Pocket detection and recognition is the basis of pocket analysis. In recent years, the pocket detection and calculation methods are mainly divided into five distinct classes: grid-based (POCKET [13], LIGSITE [14], PocketPicker [15]), probe-based (SURFNET [16], HOLLOW [17]), surface-based (MSPOcket [18]), Voronoi-based (CAVER [19], and CAST [20]). The subclasses are then formed by their combinations (KVFinder [21], CavVis [22]). The grid-based method is used to build the protein on a discrete three-dimensional grid space. The characteristic of the grid-based method is that the accuracy of protein cavity extraction will be affected by the grid size, and the improvement of grid accuracy will greatly increase the amount of computation. The PASS [23] tool developed by [PERSON] and [PERSON] combines surface-based and probe-based methods. [PERSON] et al. detect the pocket by calculating the difference between the two solvent-exclusion surfaces with different probe radii [24]. The method based on Voronoi is a computational geometry method. It is faster than the previous methods, and its results are more accurate. Therefore, in the current pocket-extraction technology, the method based on Voronoi is more commonly used. The above pocket-detection method provides a useful tool for static pocket recognition on the static snapshot provided by the protein database (PDB). They usually take a protein structure as input and return one or more candidate pockets.  
In addition to static detection and calculation, it is also very important to recognize pockets under different time slices under dynamic conditions. Dynamic pocket recognition has attracted extensive attention in the field of biochemistry. A variety of dynamic pocket analysis tools have been developed, such as MDPocket [25], CAVER [19], CavityPlus [26], D3 Pockets [2], and so on. CAVER proposes a grid-based solution for detecting cavities and  
Figure 1: VAPPD representation for the abstracted overview of protein pocket exploration over time. The navigation view (**a**) shows the volume change trend of all cavities in the protein (line chart), the hydrophilic and hydrophobic properties of pockets (pie chart), and the correlation between pockets (bar chart). The pocket comparison view (**b**) is used to select some pockets for pocket feature comparison. The pocket properties of comparison include pocket stability (river opacity) and pocket volume (river width). The magnifying glass view (**c**) shows the pocket features after partial magnification. Open the “magnifier” component and start the view. Other feature views (**d**) mainly show the changes of hydrophilicity and polarity of the pocket with time, which can be switched to the form of scatter chart or pie chart. In the pie chart form, the pocket-lining amino acids in each timestep can be viewed. The 3D visual view (**e**) describes the morphology, position, and changes of molecules and pockets. This helps users to establish the interaction between two-dimensional and three-dimensional.  
exploring protein surface and internal pathways. On this basis, [PERSON] et al. of Masarik University continued to develop CAVER Analyst 2.0 [27], which analyzes tunnels and channels in large MD simulations. In this paper, the Fpocket [28] is used to process the trajectory data of protein molecular dynamics simulation. This method uses Voronoi and alpha spheres [29] to analyze the protein surface. Pocket dynamics are analyzed by iterative pocket tracking of a set of PDB snapshots, which represent various conformational states of proteins of interest. The above work adopts the form of static three-dimensional structure in a single frame to describe the instantaneous mode of the pocket. When the static structure visually describes the dynamic process, it is easy to cause visual fragmentation, and it is difficult to form a unified and continuous visual effect in time and space, which makes it difficult for users to perceive the dynamic process of the pocket.