Compressing generalized trajectories of molecular motion for efficient detection of chemical interactions

Molecular Dynamics (MD) simulation is often used to study properties of various chemical interactions in domains such as drug discovery and development, particularly when executing real experimental studies is costly and/or unsafe. Studying the motion of trajectories of molecules/atoms generated from MD simulations provides a detailed atomic level spatial location of every atom for every time frame in the experiment. The analysis of this data leads to an atomic and molecular level understanding of interactions among the constituents of the system of interest. However, the data is extremely large and poses storage and processing challenges in the querying and analysis of associated atom level motion trajectories. We take a first step towards applying domain-specific generalization techniques for the data representation, subsequently used for applying trajectory compression algorithms towards reducing the storage requirements and speeding up the processing of within-distance queries over MD simulation data. We demonstrate that this generalization-aware compression, when applied to the dataset used in this case study, yields significant improvements in terms of data reduction and processing time without sacrificing the effectiveness of within-distance queries for threshold-based detection of molecular events of interest, such as the formation of Hydrogen Bonds (H-Bonds).