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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

fmcsR: mismatch tolerant maximum common substructure searching in R.

Yan Wang¹, Tyler W H Backman, Kevin Horan

¹Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.

Bioinformatics (Oxford, England)

|August 22, 2013

Summary

This summary is machine-generated.

A new flexible maximum common substructure (FMCS) method enhances small molecule similarity searching in drug discovery. This approach improves the enrichment of active compounds in virtual screening, outperforming existing methods.

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Area of Science:

Computational chemistry
Cheminformatics
Drug discovery

Background:

Accurate structural similarity measurement is crucial for drug discovery and chemical genomics.
Existing maximum common substructure (MCS) methods are accurate but limited by rigid matching policies.
A new mismatch-tolerant search method for flexible MCSs (FMCSs) is introduced to overcome these limitations.

Purpose of the Study:

To develop and evaluate a novel FMCS algorithm for improved small molecule similarity analysis.
To enhance the accuracy and efficiency of virtual screening processes.
To provide a user-friendly R package (fmcsR) for FMCS computation.

Main Methods:

Implementation of the FMCS algorithm with time-consuming steps in C++ and an R interface (fmcsR).
Inclusion of utilities for pairwise compound comparisons, similarity searching, clustering, and visualization.
Evaluation of FMCS performance against existing MCS tools and other virtual screening methods.

Main Results:

The fmcsR package demonstrates superior time performance compared to existing MCS tools.
Enabling mismatch tolerance in FMCSs results in larger common substructures and improved enrichment of active compounds in similarity searches.
FMCS outperforms seven other virtual screening methods in both overall and early enrichment performance.

Conclusions:

The FMCS approach significantly enhances the effectiveness of similarity searching for drug discovery.
The fmcsR package offers an efficient and flexible tool for analyzing small molecule structural similarities.
Flexible matching in MCS algorithms is a valuable strategy for improving virtual screening and identifying potential drug candidates.