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Substructure search in chemical compound databases is a fundamental task incheminformatics with critical implications for fields such as drug discovery,materials science, and toxicology. However, the increasing size and complexityof chemical databases have rendered traditional search algorithms ineffective,exacerbating the need for scalable solutions. We introduce a novel approach toenhance the efficiency of substructure search, moving beyond the traditionalfull-enumeration methods. Our strategy employs a unique index structure: a treethat segments the molecular data set into clusters based on the presence orabsence of certain features. This innovative indexing mechanism is inspired bythe binary Ball-Tree concept and demonstrates superior performance overexhaustive search methods, leading to significant acceleration in the initialfiltering process. Comparative analysis with the existing Bingo algorithmreveals the efficiency and versatility of our approach. Although the currentimplementation does not affect the verification stage, it has the potential toreduce false positive rates. Our method offers a promising avenue for futureresearch, meeting the growing demand for fast and accurate substructure searchin increasingly large chemical databases.

Optimizing substructure search: a novel approach for efficient querying in large chemical databases