Journal Article

Site of metabolism prediction for six biotransformations mediated by cytochromes P450

Mingyue Zheng, Xiaomin Luo, Qiancheng Shen, Yong Wang, Yun Du, Weiliang Zhu and Hualiang Jiang

in Bioinformatics

Volume 25, issue 10, pages 1251-1258
Published in print May 2009 | ISSN: 1367-4803
Published online March 2009 | e-ISSN: 1460-2059 | DOI:
Site of metabolism prediction for six biotransformations mediated by cytochromes P450

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Motivation: One goal of metabolomics is to define and monitor the entire metabolite complement of a cell, while it is still far from reach since systematic and rapid approaches for determining the biotransformations of newly discovered metabolites are lacking. For drug development, such metabolic biotransformation of a new chemical entity (NCE) is of more interest because it may profoundly affect its bioavailability, activity and toxicity profile. The use of in silico methods to predict the site of metabolism (SOM) in phase I cytochromes P450-mediated reactions is usually a starting point of metabolic pathway studies, which may also assist in the process of drug/lead optimization.

Results: This article reports the Cytochromes P450 (CYP450)-mediated SOM prediction for the six most important metabolic reactions by incorporating the use of machine learning and semi-empirical quantum chemical calculations. Non-local models were developed on the basis of a large dataset comprising 1858 metabolic reactions extracted from 1034 heterogeneous chemicals. For validation, the overall accuracies of all six reaction types are higher than 0.81, four of which exceed 0.90. In further receiver operating characteristic (ROC) analyses, each of the SOM model gave a significant area under curve (AUC) value over 0.86, indicating a good predicting power. An external test was made on a previously published dataset, of which 80% of the experimentally observed SOMs can be correctly identified by applying the full set of our SOM models.

Availability: The program package SOME_v1.0 (Site Of Metabolism Estimator) developed based on our models is available at


Journal Article.  6095 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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