Molecular dynamics (MD) plays a crucial role in research across diverse fields such as biochemistry, materials science, pharmaceutical drug design, and physics. By simulating and analyzing the dynamics of molecular systems, researchers aim to bridge observable and unobservable scales by replicating experimental outcomes through computational methods. These simulations generate molecular trajectories that capture the temporal evolution of molecular systems, providing rich datasets for further study. To analyze this data, researchers rely on a combination of visualization and analysis tools to construct and study properties and structures to gain insights. However, the fragmentation of tools in typical workflows disrupts the exploration process as the tools cannot share contextual information, leading to inefficiencies.

This work introduces methods to enhance the core aspects of molecular analysis: the definition, evaluation, and visualization of molecular properties and structures. By framing MD analysis as a practical application of the visualization pipeline, we establish guiding principles aimed at improving the entire process, shaping the work presented in this thesis.

Visualization of molecular structures, composed of infinitesimal particles, relies on mapping molecular properties into geometric and color representations. Through geometric shape and color, we can emulate light interactions, crucial for perceiving depth and spatial relations in three-dimensional environments. Spatial relations play an essential role in the dynamics of molecular systems, as intermolecular interactions depend on proximity. To encode the spatial relations over time, we employ Spatial Distribution Functions (SDF), a technique which extracts reference frames inherent to structures that are used to record the occurrences of atoms. This work incorporates the SDF as an integral part of the analysis workflow and extends its capabilities to support the aggregation of structural instances.

Beyond the SDF, the work presents a set of visual analysis components tailored for MD analysis. By unifying these components within a single software tool, we streamline the workflow, enabling tighter integration, improved communication between components, and enhanced user interaction. This unified approach reduces the time required for each exploratory cycle and enhances the overall analysis experience.

The culmination of this work is VIAMD, a software for interactive visual analysis of molecular dynamics. A key challenge in developing such interactive tools is ensuring efficient hardware utilization to handle large datasets and minimize response times. Consequently, a dedicated chapter addresses practical implementation aspects for common MD operations, with the goal of ensuring scalability and performance.