InterMap: Accelerated Detection of Interaction Fingerprints on Large-Scale Molecular Ensembles

Molecular dynamics is a key technique for exploring biomolecular systems at the atomic level. The rapid growth in accessible system sizes and time scales has intensified the need for efficient postprocessing methods that extract meaningful insights from the resulting data. Interaction fingerprint (IFP) analyses are a valuable tool for elucidating key atomic interactions within molecular ensembles; yet current specialized software often struggle with extensive trajectories or complex systems. Here, we introduce InterMap, a Python package designed to accelerate IFP detection on large-scale molecular ensembles. By actively exploiting k-d trees, InterMap efficiently handles the massive amount of distance calculations necessary to detect IFPs, particularly when dealing with intramolecular interactions. The seamless integration with MDAnalysis ensures broad format compatibility and allows using SMARTS patterns for flexible interaction definitions. InterMap adopts a deeply compressed binary encoding to manage IFPs, which makes it very memory-friendly. Furthermore, convenient interactive visualizations are provided to enhance data interpretation through a locally hosted web-browser application. Benchmark results indicate that InterMap significantly outperforms existing tools for processing complex biomolecular systems, achieving up to a 99% reduction in both runtime and peak memory usage. InterMap's code and issue tracker are available at https://github.com/rglez/intermap, while documentation and tutorials can be found at https://rglez.github.io/intermap/.