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

TL;DR

SPECTRe is a Python tool that efficiently enumerates all substructures of molecules in SMILES format, aiding cheminformatics applications like virtual screening, similarity searching, and data mining.

Contribution

The paper introduces SPECTRe, a novel, efficient Python-based tool for comprehensive substructure enumeration in molecules represented as SMILES, supporting various cheminformatics tasks.

Findings

Substructure count correlates with molecular complexity factors.

Substructure counts vary across chemical classes and topologies.

SPECTRe demonstrates potential in drug discovery and data mining applications.

Abstract

Functional groups and moieties are chemical descriptors of biomolecules that can be used to interpret their properties and functions, leading to the understanding of chemical or biological mechanisms. These chemical building blocks, or sub-structures, enable the identification of common molecular subgroups, assessing the structural similarities and critical interactions among a set of biological molecules with known activities, and designing novel compounds with similar chemical properties. Here, we introduce a Python-based tool, SPECTRe (Substructure Processing, Enumeration, and Comparison Tool Resource), designed to provide all substructures in a given molecular structure, regardless of the molecule size, employing efficient enumeration and generation of substructures represented in a human-readable SMILES format through the use of classical graph traversal (breadth-first and depth-first search) algorithms. We demonstrate the application of SPECTRe for a set of 10,375 molecules in the molecular weight range 27 to 350 Da (<=26 non-hydrogen atoms), spanning a wide array of structure-based chemical functionalities and chemical classes. We found that the substructure count as a measure of molecular complexity depends strongly on the number of unique atom and bond types present, degree of branching, and presence of rings. The substructure counts are found to be similar for a set of molecules belonging to particular chemical classes and classified based on the characteristic features of certain topologies. We demonstrate that SPECTRe shows promise to be useful in many applications of cheminformatics such as virtual screening for drug discovery, property prediction, fingerprint-based molecular similarity searching, and data mining for identifying frequent substructures.