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.

该研究提出了一种新颖的方法，结合子结构计数，k-mers 和类似 Daylight 的指纹，以扩展 SMILES 字符串中化学结构的表示。该集成方法生成全面的分子嵌入，增强了辨别能力和信息内容。实验评估表明，与传统的 Morgan 指纹，MACCS 和单独的 Daylight 指纹相比，该方法优势明显，改善了药物分类等化学信息学任务。该方法提供了化学结构的更丰富表示，推进了分子相似性分析，并促进了在分子设计和药物发现中的应用。它为分子结构分析和设计提供了有前景的研究方向，并具有重要的实际实施潜力。