A structural hierarchy matching approach for molecular similarity/substructure searching

Area of Science:

• Computational Chemistry
• Cheminformatics

Background:

• Molecular similarity and substructure searching are crucial in drug discovery and chemical research.
• Existing methods may lack efficiency or comprehensive structural analysis.

Purpose of the Study:

• To propose a new approach for molecular similarity and substructure searching based on structural hierarchy matching.
• To develop a method that categorizes molecules into acyclic and cyclic forms with further classification of cyclic structures.

Main Methods:

• Molecules were divided into acyclic and cyclic categories, with cyclic structures further classified into framework, complicated-, and mono-rings.
• Minimal cyclic and acyclic substructures were extracted from 13,911 chemicals and processed into fuzzy structural fingerprints.
• Similarity coefficients were calculated using the query's hierarchy as a reference for searching.

Main Results:

• The approach allows for both one-substructure and multi-substructure searching with sorted results.
• Fuzzy structural fingerprints derived from hierarchical matching serve as effective search indices.
• The method demonstrated its capability in retrieving relevant molecular data.

Conclusions:

• The proposed structural hierarchy matching approach offers an effective strategy for molecular similarity and substructure searching.
• This algorithm provides flexibility for users and shows potential for further development in molecular analysis.