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Multivariate Analysis of Side Effects of Drug Molecules Based on Knowledge of Protein Bindings and ProteinProtein Interactions
Author(s) -
Hasegawa Kiyoshi,
Funatsu Kimito
Publication year - 2014
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201400064
Subject(s) - multivariate statistics , multivariate analysis , drug , chemistry , protein–protein interaction , cheminformatics , drug discovery , computational biology , computer science , data mining , data science , pharmacology , computational chemistry , biochemistry , machine learning , medicine , biology
Here, we examined the relationships between 969 side effects associated with 658 drugs and their 1368 human protein targets using our hybrid approaches. Firstly, L‐shaped PLS (LPLS) was used to construct a multivariate model of side effects and protein bindings of drug molecules. LPLS is an extension of standard PLS regression, where, in addition to the response matrix Y and the regressor matrix X , an extra data matrix Z is constructed that summarizes the background information of X. X and Y are matrices comprising drugs‐target proteins, and drugs‐side effects, respectively. The Z matrix is the proteinprotein interaction data. From the loading plot of Y , we could identify two remarkable side effects (urinary incontinence and increased salivation) From the corresponding loading plot of X , the responsible protein targets causing each side effect could be estimated (sodium channels and gamma‐aminobutyric acid (GABA) receptors). The loading plot of the Z matrix indicated that the GABA receptors interact with each other and they heavily influence the side effect of increased salivation. Secondly, Bayesian classifier methods were separately applied to the cases of the two side effects. That is, the Bayesian classifier method was used to classify drug molecules as binding or not binding to the responsible protein targets associated with each side effect. Using atom‐coloring techniques, it was possible to estimate which fragments on the drug molecule might cause the specific side effects. This information is valuable for drug design to avoid specific side effects.