Generalization Aware Compression of Molecular Trajectories

Molecular Dynamics (MD) simulation is often used to study properties of various chemical interactions in domains such as drug development when executing real experimental studies are costly and/or unsafe. Studying trajectories generated from MD simulations provides detailed atomic level location data of every atom 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 formidable storage and processing challenges in the analyses and querying of associated atom level motion trajectories. We take a first step towards applying domain-specific generalization techniques for 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 efficiency improvements, 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).