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Comparison of ultra‐fast 2 D and 3 D ligand and target descriptors for side effect prediction and network analysis in polypharmacology
Author(s) -
CortésCabrera Alvaro,
Morris Garrett M,
Finn Paul W,
Morreale Antonio,
Gago Federico
Publication year - 2013
Publication title -
british journal of pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.432
H-Index - 211
eISSN - 1476-5381
pISSN - 0007-1188
DOI - 10.1111/bph.12294
Subject(s) - chemical space , computer science , viewpoints , drug discovery , chembl , interface (matter) , data mining , computational biology , machine learning , data science , bioinformatics , biology , physics , bubble , maximum bubble pressure method , parallel computing , acoustics
Some existing computational methods are used to infer protein targets of small molecules and can therefore be used to find new targets for existing drugs, with the goals of re-directing the molecule towards a different therapeutic purpose or explaining off-target effects due to multiple targeting. Inherent limitations, however, arise from the fact that chemical analogy is calculated on the basis of common frameworks or scaffolds and also because target information is neglected. The method we present addresses these issues by taking into account 3D information from both the ligand and the target.