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

Abstract Motivation Molecular dynamics is a key technique for exploring biomolecular systems at the atomic level. The rapid growth in accessible system sizes and timescales has intensified the need for efficient post-processing 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. Results By actively exploiting k-d trees, InterMap efficiently handles the massive amount of distance calculations necessary to detect IFPs, particularly when dealing with intra-molecular 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. Availability InterMap’s code and issue tracker are available at https://github.com/Delta-Research-Team/intermap.git , while documentation and tutorials can be found at https://delta-research-team.github.io/intermap/ . Contact [email protected] , [email protected] Supplementary information Supplementary data are available at Nucleic Acid Research online.