fmcsR: mismatch tolerant maximum common substructure searching in R | Zendy

Yan Wang | Zendy; Tyler W. H. Backman | Zendy; Kevin Horan | Zendy; Thomas Girke | Zendy

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fmcsR: mismatch tolerant maximum common substructure searching in R

Author(s) -

Yan Wang,

Tyler W. H. Backman,

Kevin Horan,

Thomas Girke

Publication year - 2013

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btt475

Subject(s) - bioconductor , computer science , pairwise comparison , data mining , substructure , similarity (geometry) , identification (biology) , virtual screening , cluster analysis , matching (statistics) , visualization , feature (linguistics) , fragment (logic) , plug in , algorithm , artificial intelligence , drug discovery , bioinformatics , mathematics , biology , biochemistry , statistics , botany , linguistics , philosophy , structural engineering , gene , engineering , image (mathematics) , programming language

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.

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