## 1 Introduction  
A cavity on the surface of a protein that possesses suitable properties for binding a ligand is usually referred to as a protein binding pocket [1]. In structure-based drug design, the identification of protein binding pockets (orthosteric pockets) is critically important [2]. Different from the crystalline state, the protein structure in the organism is always moving and changing. The geometric and physicochemical properties of protein pockets may be affected by protein movement [3]. The dynamic characteristics of protein pockets include stability, continuity, and correlation. The stability and continuity of the pocket can be used to track the changes of the pocket, and a stable pocket can ensure the stability of the local structure of the protein. Drugs developed on allosteric pockets with high stability can ensure their stable function. EPOS\({}^{BP}\)[4] and Epock [5] track some pocket properties along the MD trajectory (as volume). It is difficult to show the stability and continuity of the pockets as a whole and intuitively in the long-term pocket simulation, and to compare the stability of the pockets more conveniently. The correlation of pockets is mainly used to identify potential allosteric pockets that are highly related to the movement of orthosteric pockets. However, allosteric site discovery is mainly verified by experimental random discovery [6]. In recent years, with the development of technology and the deepening of allosteric research, active identification of allosteric sites by using existing knowledge systems has become a hot topic [7]. A variety of methods based on normal mode analysis [8, 9] and MD simulation [2, 10] have been developed to analyze the correlation of protein pockets and predict protein allosteric pockets. Due to the different length and time length of the alpha spheres of two dynamic pocket sequences, it is difficult to calculate the correlation. Analyzing the physicochemical properties of the pocket is helpful for experts in the field to further judge the physicochemical properties of the potentially binding drugs of the pocket. Dynamic pocket data has high dimensional, spatiotemporal, and relevant characteristics. In this case, the way to obtain the dynamic pocket characteristics from high-dimensional protein pocket data has become a challenging task. Using visual methods has become a trend that explores the dynamic characteristics of protein pockets by analyzing and calculating the trajectory data. As far as we know, there is no visualization method by which to systematically explore the dynamic properties of the protein pocket based on any molecular dynamics (MD) simulation trajectories. We propose a visual analysis method of protein pocket dynamics to analyze the dynamic characteristics of cavities for the discovery of allosteric pockets (Figure 1). First, most of the methods only take into account the topological features or pocket volume features of protein pockets. VAPPD innovatively introduces the vectorization method in NLP into the molecular dynamic pocket feature coding. A special dynamic pocket feature encoding method is proposed to better preserve multiple features of the alpha sphere. Secondly, the common correlation calculation methods are generally unable to align three-dimensional spatiotemporal pocket vectors, so it is difficult to calculate high-dimensional molecular pocket correlation. An innovative high-dimensional pocket correlation calculation method based on P2V-DTW is proposed to solve the problems that the length of two static pocket alpha spheres is different and the correlation is misplaced, and the problems of different time lengths and time-related dislocation of two dynamic pocket sequences as well. Thirdly, we propose a progressive visual analysis method, which can gradually locate from all pockets to a single important pocket, and from global time to a more critical time.