Motivation: The ability to accurately measure structural similarities among small molecules is important for many analysis routines in drug discovery and chemical genomics. Algorithms used for this purpose include fragment-based fingerprint and graph-based maximum common substructure (MCS) methods. MCS approaches provide one of the most accurate similarity measures. However, their rigid matching policies limit them to the identification of perfect MCSs. To eliminate this restriction, we introduce a new mismatch tolerant search method for identifying flexible MCSs (FMCSs) containing a user-definable number of atom and/or bond mismatches.
Results: The fmcsR package provides an R interface, with the time-consuming steps of the FMCS algorithm implemented in C++. It includes utilities for pairwise compound comparisons, structure similarity searching, clustering and visualization of MCSs. In comparison with an existing MCS tool, fmcsR shows better time performance over a wide range of compound sizes. When mismatching of atoms or bonds is turned on, the compute times increase as expected, and the resulting FMCSs are often substantially larger than their strict MCS counterparts. Based on extensive virtual screening (VS) tests, the flexible matching feature enhances the enrichment of active structures at the top of MCS-based similarity search results. With respect to overall and early enrichment performance, FMCS outperforms most of the seven other VS methods considered in these tests.
Availability: fmcsR is freely available for all common operating systems from the Bioconductor site (http://www.bioconductor.org/packages/devel/bioc/html/fmcsR.html).
Contact: thomas.girke@ucr.edu.
Supplementary information: Supplementary data are available at Bioinformatics online.