Interpretable Deep Learning for Single-Molecule Nanopore Fingerprinting Using Physics-Guided Preprocessing

Rapid and robust molecular fingerprinting is critical in biomanufacturing, diagnostics, and environmental monitoring. Nanopore sensing provides single-molecule readouts as transient ionic current pulses; however, conventional analyses depend on handcrafted features that miss informative structural information. We present an interpretable machine learning framework that operates directly on raw pulses, pairing a physics-guided time-frequency transform with a compact neural classifier and feature-attribution maps. We also include conventional feature-based SVMs and a 1D classifier trained on raw pulses as baselines. On two self-assembled DNA nanostructures of similar size but distinct geometry, for which standard pulse features overlap, the method achieves high accuracy and yields physically consistent attributions that highlight discriminative signal motifs. A matched control without the time-frequency transform clarifies when learned filters suffice versus when physics-guided preprocessing improves reliability, leading to a practical "custom-filter" design principle. The workflow is modular, lightweight, and applicable to pulse-based sensing platforms, including virus and exosome analysis, electrochemical monitoring, and industrial fault detection. By combining accuracy with transparency, it lays the groundwork for deployable sensing platforms in regulated, mission-critical settings.