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RD-Metabolizer: an integrated and reaction types extensive approach to predict metabolic sites and metabolites of drug-like molecules

BACKGROUND: Experimental approaches for determining the metabolic properties of the drug candidates are usually expensive, time-consuming and labor intensive. There is a great deal of interest in developing computational methods to accurately and efficiently predict the metabolic decomposition of dr...

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Autores principales: Meng, Jiajia, Li, Shiliang, Liu, Xiaofeng, Zheng, Mingyue, Li, Honglin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5515729/
https://www.ncbi.nlm.nih.gov/pubmed/29086838
http://dx.doi.org/10.1186/s13065-017-0290-4
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author Meng, Jiajia
Li, Shiliang
Liu, Xiaofeng
Zheng, Mingyue
Li, Honglin
author_facet Meng, Jiajia
Li, Shiliang
Liu, Xiaofeng
Zheng, Mingyue
Li, Honglin
author_sort Meng, Jiajia
collection PubMed
description BACKGROUND: Experimental approaches for determining the metabolic properties of the drug candidates are usually expensive, time-consuming and labor intensive. There is a great deal of interest in developing computational methods to accurately and efficiently predict the metabolic decomposition of drug-like molecules, which can provide decisive support and guidance for experimentalists. RESULTS: Here, we developed an integrated, low false positive and reaction types extensive metabolism prediction approach called RD-Metabolizer (Reaction Database-based Metabolizer). RD-Metabolizer firstly employed the detailed reaction SMARTS patterns to encode different metabolism reaction types with the aim of covering larger chemical reaction space. 2D fingerprint similarity calculation model was built to calculate the metabolic probability of each site in a molecule. RDKit was utilized to act on pre-written reaction SMARTS patterns to correct the metabolic ranking of each site in a molecule generated by the 2D fingerprint similarity calculation model as well as generate corresponding structures of metabolites, thus helping to reduce the false positive metabolites. Two test sets were adopted to evaluate the performance of RD-Metabolizer in predicting SOMs and structures of metabolites. The results indicated that RD-Metabolizer was better than or at least as good as several widely used SOMs prediction methods. Besides, the number of false positive metabolites was obviously reduced compared with MetaPrint2D-React. CONCLUSIONS: The accuracy and efficiency of RD-Metabolizer was further illustrated by a metabolism prediction case of AZD9291, which is a mutant-selective EGFR inhibitor. RD-Metabolizer will serve as a useful toolkit for the early metabolic properties assessment of drug-like molecules at the preclinical stage of drug discovery. [Figure: see text]
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spelling pubmed-55157292017-08-02 RD-Metabolizer: an integrated and reaction types extensive approach to predict metabolic sites and metabolites of drug-like molecules Meng, Jiajia Li, Shiliang Liu, Xiaofeng Zheng, Mingyue Li, Honglin Chem Cent J Methodology BACKGROUND: Experimental approaches for determining the metabolic properties of the drug candidates are usually expensive, time-consuming and labor intensive. There is a great deal of interest in developing computational methods to accurately and efficiently predict the metabolic decomposition of drug-like molecules, which can provide decisive support and guidance for experimentalists. RESULTS: Here, we developed an integrated, low false positive and reaction types extensive metabolism prediction approach called RD-Metabolizer (Reaction Database-based Metabolizer). RD-Metabolizer firstly employed the detailed reaction SMARTS patterns to encode different metabolism reaction types with the aim of covering larger chemical reaction space. 2D fingerprint similarity calculation model was built to calculate the metabolic probability of each site in a molecule. RDKit was utilized to act on pre-written reaction SMARTS patterns to correct the metabolic ranking of each site in a molecule generated by the 2D fingerprint similarity calculation model as well as generate corresponding structures of metabolites, thus helping to reduce the false positive metabolites. Two test sets were adopted to evaluate the performance of RD-Metabolizer in predicting SOMs and structures of metabolites. The results indicated that RD-Metabolizer was better than or at least as good as several widely used SOMs prediction methods. Besides, the number of false positive metabolites was obviously reduced compared with MetaPrint2D-React. CONCLUSIONS: The accuracy and efficiency of RD-Metabolizer was further illustrated by a metabolism prediction case of AZD9291, which is a mutant-selective EGFR inhibitor. RD-Metabolizer will serve as a useful toolkit for the early metabolic properties assessment of drug-like molecules at the preclinical stage of drug discovery. [Figure: see text] Springer International Publishing 2017-07-18 /pmc/articles/PMC5515729/ /pubmed/29086838 http://dx.doi.org/10.1186/s13065-017-0290-4 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology
Meng, Jiajia
Li, Shiliang
Liu, Xiaofeng
Zheng, Mingyue
Li, Honglin
RD-Metabolizer: an integrated and reaction types extensive approach to predict metabolic sites and metabolites of drug-like molecules
title RD-Metabolizer: an integrated and reaction types extensive approach to predict metabolic sites and metabolites of drug-like molecules
title_full RD-Metabolizer: an integrated and reaction types extensive approach to predict metabolic sites and metabolites of drug-like molecules
title_fullStr RD-Metabolizer: an integrated and reaction types extensive approach to predict metabolic sites and metabolites of drug-like molecules
title_full_unstemmed RD-Metabolizer: an integrated and reaction types extensive approach to predict metabolic sites and metabolites of drug-like molecules
title_short RD-Metabolizer: an integrated and reaction types extensive approach to predict metabolic sites and metabolites of drug-like molecules
title_sort rd-metabolizer: an integrated and reaction types extensive approach to predict metabolic sites and metabolites of drug-like molecules
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5515729/
https://www.ncbi.nlm.nih.gov/pubmed/29086838
http://dx.doi.org/10.1186/s13065-017-0290-4
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