↓ Skip to main content

Prediction of reacting atoms for the major biotransformation reactions of organic xenobiotics

Overview of attention for article published in Journal of Cheminformatics, November 2016
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
24 Dimensions

Readers on

mendeley
39 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Prediction of reacting atoms for the major biotransformation reactions of organic xenobiotics
Published in
Journal of Cheminformatics, November 2016
DOI 10.1186/s13321-016-0183-x
Pubmed ID
Authors

Anastasia V. Rudik, Alexander V. Dmitriev, Alexey A. Lagunin, Dmitry A. Filimonov, Vladimir V. Poroikov

Abstract

The knowledge of drug metabolite structures is essential at the early stage of drug discovery to understand the potential liabilities and risks connected with biotransformation. The determination of the site of a molecule at which a particular metabolic reaction occurs could be used as a starting point for metabolite identification. The prediction of the site of metabolism does not always correspond to the particular atom that is modified by the enzyme but rather is often associated with a group of atoms. To overcome this problem, we propose to operate with the term "reacting atom", corresponding to a single atom in the substrate that is modified during the biotransformation reaction. The prediction of the reacting atom(s) in a molecule for the major classes of biotransformation reactions is necessary to generate drug metabolites. Substrates of the major human cytochromes P450 and UDP-glucuronosyltransferases from the Biovia Metabolite database were divided into nine groups according to their reaction classes, which are aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation. Each training set consists of positive and negative examples of structures with one labelled atom. In the positive examples, the labelled atom is the reacting atom of a particular reaction that changed adjacency. Negative examples represent non-reacting atoms of a particular reaction. We used Labelled Multilevel Neighbourhoods of Atoms descriptors for the designation of reacting atoms. A Bayesian-like algorithm was applied to estimate the structure-activity relationships. The average invariant accuracy of prediction obtained in leave-one-out and 20-fold cross-validation procedures for five human isoforms of cytochrome P450 and all isoforms of UDP-glucuronosyltransferase varies from 0.86 to 0.99 (0.96 on average). We report that reacting atoms may be predicted with reasonable accuracy for the major classes of metabolic reactions-aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation. The proposed method is implemented as a freely available web service at http://www.way2drug.com/RA and may be used for the prediction of the most probable biotransformation reaction(s) and the appropriate reacting atoms in drug-like compounds.Graphical abstract.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 28%
Student > Master 8 21%
Other 5 13%
Student > Ph. D. Student 5 13%
Student > Doctoral Student 2 5%
Other 2 5%
Unknown 6 15%
Readers by discipline Count As %
Chemistry 9 23%
Biochemistry, Genetics and Molecular Biology 6 15%
Computer Science 4 10%
Chemical Engineering 2 5%
Agricultural and Biological Sciences 2 5%
Other 8 21%
Unknown 8 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 November 2016.
All research outputs
#12,168,083
of 20,781,426 outputs
Outputs from Journal of Cheminformatics
#601
of 767 outputs
Outputs of similar age
#201,900
of 418,620 outputs
Outputs of similar age from Journal of Cheminformatics
#48
of 68 outputs
Altmetric has tracked 20,781,426 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 767 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.8. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 418,620 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 68 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.