Site of metabolism prediction for oxidation reactions mediated by oxidoreductases based on chemical bond | Zendy

Shuaibing He | Zendy; Manman Li | Zendy; Xiaotong Ye | Zendy; Hongyu Wang | Zendy; Wenkang Yu | Zendy; Wenjing He | Zendy; Yun Wang | Zendy; Yanjiang Qiao | Zendy

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Site of metabolism prediction for oxidation reactions mediated by oxidoreductases based on chemical bond

Author(s) -

Shuaibing He,

Manman Li,

Xiaotong Ye,

Hongyu Wang,

Wenkang Yu,

Wenjing He,

Yun Wang,

Yanjiang Qiao

Publication year - 2016

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/btw617

Subject(s) - computer science , in silico , akaike information criterion , support vector machine , applicability domain , machine learning , feature selection , artificial intelligence , quantitative structure–activity relationship , data mining , biological system , biochemical engineering , chemistry , biochemistry , biology , engineering , gene

The metabolites of exogenous and endogenous compounds play a pivotal role in the domain of metabolism research. However, they are still unclear for most chemicals in our environment. The in silico methods for predicting the site of metabolism (SOM) are considered to be efficient and low-cost in SOM discovery. However, many in silico methods are focused on metabolism processes catalyzed by several specified Cytochromes P450s, and only apply to substrates with special skeleton. A SOM prediction model always deserves more attention, which demands no special requirements to structures of substrates and applies to more metabolic enzymes.

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