  This study introduces a novel approach, combining substruct counting,
$k$-mers, and Daylight-like fingerprints, to expand the representation of
chemical structures in SMILES strings. The integrated method generates
comprehensive molecular embeddings that enhance discriminative power and
information content. Experimental evaluations demonstrate its superiority over
traditional Morgan fingerprinting, MACCS, and Daylight fingerprint alone,
improving chemoinformatics tasks such as drug classification. The proposed
method offers a more informative representation of chemical structures,
advancing molecular similarity analysis and facilitating applications in
molecular design and drug discovery. It presents a promising avenue for
molecular structure analysis and design, with significant potential for
practical implementation.
