Molecular fingerprints are widely used in quantitative structure–activity relationship (QSAR) modeling, virtual screening, and similarity analysis; however, interpreting their bit-level contributions to molecular properties and biological activity remains challenging due to limited visualization tools and poor integration with explainable AI methods. We developed the Universal Molecular Fingerprint Highlighter (UMFH), a Python-based graphical application supporting over 15 fingerprint types, including ECFP, FCFP, MACCS Keys, RDKit, Atom Pair, Topological Torsion, and Avalon. UMFH enables weight-based substructure highlighting linked to QSAR and machine-learning model outputs, supports batch processing, and incorporates asynchronous multithreading for high-throughput analysis. UMFH achieved a processing throughput of 50–200 molecules per second with linear scalability up to 10,000 compounds. Case studies demonstrated accurate pharmacophore identification in quorum-sensing inhibitors (87%), complete functional group recognition using FCFP4 (100%), and high specificity in substructure attribution. UMFH provides a high-performance, interpretable, and user-friendly platform for molecular fingerprint visualization, facilitating transparent QSAR interpretation and explainable AI-driven drug discovery.
Arulsamy et al. (Sun,) studied this question.
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