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Small Molecule Subgraph Detector (SMSD) toolkit.

Syed Asad Rahman¹, Matthew Bashton, Gemma L Holliday

¹EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. asad@ebi.ac.uk.

Journal of Cheminformatics

|March 20, 2010

Summary

This summary is machine-generated.

A new Maximum Common Subgraph (MCS) tool, Small Molecule Subgraph Detector (SMSD), efficiently identifies molecular similarities for complex searches. This computational chemistry tool surpasses existing methods in finding exact atom-bond equivalences for drug discovery and bioinformatics.

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Area of Science:

Computational chemistry
Bioinformatics
Chemo-informatics

Background:

Heuristic approaches for small molecule similarity searches often fail to identify exact atom-bond equivalences.
This limitation restricts their application in complex searches like metabolic pathways.

Purpose of the Study:

Introduce a novel Maximum Common Subgraph (MCS) tool, Small Molecule Subgraph Detector (SMSD).
Overcome limitations of heuristic methods in computational chemistry similarity searches.
Enable exact atom-bond equivalence identification for complex molecular structures.

Main Methods:

SMSD implements MCS search incorporating chemical knowledge (atom type, bond sensitivity).
Solutions are ranked using chemical filters (stereochemistry, bond energy).
Benchmarked via 50,000 pairwise comparisons between KEGG ligands and PDB HET Group atoms.

Main Results:

SMSD demonstrates superior efficiency compared to the Chemistry Development Kit (CDK) MCS module.
Successfully generated MCS solutions in benchmark tests.
Validated effectiveness in handling complex chemical similarity searches.

Conclusions:

SMSD is a robust tool applicable to bioinformatics and chemo-informatics.
Facilitates analysis of metabolic networks and metabolome data.
Supports screening of drug-like compounds and discovery of new drug molecules.