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Structural feature-driven pattern analysis for multitarget modulator landscapes
Author(s) -
Vigneshwaran Namasivayam,
Katja Stefan,
Katja Silbermann,
Jens Pahnke,
Michael Wiese,
Sven Marcel Stefan
Publication year - 2021
Publication title -
bioinformatics
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab832
Subject(s) - drug discovery , virtual screening , computer science , transporter , feature (linguistics) , computational biology , selection (genetic algorithm) , feature selection , process (computing) , atp binding cassette transporter , bioinformatics , biology , artificial intelligence , gene , biochemistry , linguistics , philosophy , operating system
Multitargeting features of small molecules have been of increasing interest in recent years. Polypharmacological drugs that address several therapeutic targets may provide greater therapeutic benefits for patients. Furthermore, multitarget compounds can be used to address proteins of the same (or similar) protein families for their exploration as potential pharmacological targets. In addition, the knowledge of multitargeting features is of major importance in the drug selection process; particularly in ultra-large virtual screening procedures to gain high-quality compound collections. However, large-scale multitarget modulator landscapes are almost non-existent.

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