Predicting the Metabolic Sites by Flavin-Containing Monooxygenase on Drug Molecules Using SVM Classification on Computed Quantum Mechanics and Circular Fingerprints Molecular Descriptors Article Swipe
Fu Chien-wei
,
Thy‐Hou Lin
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0169910
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0169910
As an important enzyme in Phase I drug metabolism, the flavin-containing monooxygenase (FMO) also metabolizes some xenobiotics with soft nucleophiles. The site of metabolism (SOM) on a molecule is the site where the metabolic reaction is exerted by an enzyme. Accurate prediction of SOMs on drug molecules will assist the search for drug leads during the optimization process. Here, some quantum mechanics features such as the condensed Fukui function and attributes from circular fingerprints (called Molprint2D) are computed and classified using the support vector machine (SVM) for predicting some potential SOMs on a series of drugs that can be metabolized by FMO enzymes. The condensed Fukui function fA- representing the nucleophilicity of central atom A and the attributes from circular fingerprints accounting the influence of neighbors on the central atom. The total number of FMO substrates and non-substrates collected in the study is 85 and they are equally divided into the training and test sets with each carrying roughly the same number of potential SOMs. However, only N-oxidation and S-oxidation features were considered in the prediction since the available C-oxidation data was scarce. In the training process, the LibSVM package of WEKA package and the option of 10-fold cross validation are employed. The prediction performance on the test set evaluated by accuracy, Matthews correlation coefficient and area under ROC curve computed are 0.829, 0.659, and 0.877 respectively. This work reveals that the SVM model built can accurately predict the potential SOMs for drug molecules that are metabolizable by the FMO enzymes.
Related Topics
Concepts
Flavin-containing monooxygenase
Molecular mechanics
Monooxygenase
Support vector machine
Computational biology
Flavin group
Chemistry
Molecular dynamics
Physics
Computational chemistry
Biology
Artificial intelligence
Computer science
Biochemistry
Enzyme
Cytochrome P450
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0169910
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0169910&type=printable
- OA Status
- gold
- Cited By
- 11
- References
- 108
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2569194618
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2569194618Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0169910Digital Object Identifier
- Title
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Predicting the Metabolic Sites by Flavin-Containing Monooxygenase on Drug Molecules Using SVM Classification on Computed Quantum Mechanics and Circular Fingerprints Molecular DescriptorsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
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2017-01-10Full publication date if available
- Authors
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Fu Chien-wei, Thy‐Hou LinList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pone.0169910Publisher landing page
- PDF URL
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0169910&type=printableDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0169910&type=printableDirect OA link when available
- Concepts
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Flavin-containing monooxygenase, Molecular mechanics, Monooxygenase, Support vector machine, Computational biology, Flavin group, Chemistry, Molecular dynamics, Physics, Computational chemistry, Biology, Artificial intelligence, Computer science, Biochemistry, Enzyme, Cytochrome P450Top concepts (fields/topics) attached by OpenAlex
- Cited by
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11Total citation count in OpenAlex
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2024: 1, 2023: 1, 2022: 2, 2020: 2, 2018: 3Per-year citation counts (last 5 years)
- References (count)
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108Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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