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Functional groups and moieties are chemical descriptors of biomolecules thatcan be used to interpret their properties and functions, leading to theunderstanding of chemical or biological mechanisms. These chemical buildingblocks, or sub-structures, enable the identification of common molecularsubgroups, assessing the structural similarities and critical interactionsamong a set of biological molecules with known activities, and designing novelcompounds with similar chemical properties. Here, we introduce a Python-basedtool, SPECTRe (Substructure Processing, Enumeration, and Comparison ToolResource), designed to provide all substructures in a given molecularstructure, regardless of the molecule size, employing efficient enumeration andgeneration of substructures represented in a human-readable SMILES formatthrough the use of classical graph traversal (breadth-first and depth-firstsearch) algorithms. We demonstrate the application of SPECTRe for a set of10,375 molecules in the molecular weight range 27 to 350 Da (<=26 non-hydrogenatoms), spanning a wide array of structure-based chemical functionalities andchemical classes. We found that the substructure count as a measure ofmolecular complexity depends strongly on the number of unique atom and bondtypes present, degree of branching, and presence of rings. The substructurecounts are found to be similar for a set of molecules belonging to particularchemical classes and classified based on the characteristic features of certaintopologies. We demonstrate that SPECTRe shows promise to be useful in manyapplications of cheminformatics such as virtual screening for drug discovery,property prediction, fingerprint-based molecular similarity searching, and datamining for identifying frequent substructures.

SPECTRe: Substructure Processing, Enumeration, and Comparison Tool  Resource: An efficient tool to encode all substructures of molecules  represented in SMILES