XenoSite: Accurately Predicting CYP-Mediated Sites of Metabolism with Neural Networks

Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. The cytochrome P450s (CYPs) are proteins responsible for metabolizing 90% of drugs on the market, and many c...

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Bibliographic Details
Published in:Journal of chemical information and modeling 2013-12, Vol.53 (12), p.3373-3383
Main Authors: Zaretzki, Jed, Matlock, Matthew, Swamidass, S. Joshua
Format: Article
Language:English
Subjects:
Analytical, structural and metabolic biochemistry
Applied sciences
Artificial intelligence
Biological and medical sciences
Biotransformation
Catalytic Domain
Computer science
control theory
systems
Connectionism. Neural networks
Cytochrome P-450 Enzyme System - chemistry
Cytochrome P-450 Enzyme System - metabolism
Enzymes and enzyme inhibitors
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
Humans
Isoenzymes - chemistry
Isoenzymes - metabolism
Ligands
Metabolism
Molecular Docking Simulation
Molecules
Neural networks
Neural Networks (Computer)
Oxidoreductases
Probability
Protein Binding
Proteins
Small Molecule Libraries - chemistry
Small Molecule Libraries - metabolism
Structure-Activity Relationship
Substrate Specificity
Thermodynamics
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Summary:Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. The cytochrome P450s (CYPs) are proteins responsible for metabolizing 90% of drugs on the market, and many computational methods can predict which atomic sites of a moleculesites of metabolism (SOMs)are modified during CYP-mediated metabolism. This study improves on prior methods of predicting CYP-mediated SOMs by using new descriptors and machine learning based on neural networks. The new method, XenoSite, is faster to train and more accurate by as much as 4% or 5% for some isozymes. Furthermore, some “incorrect” predictions made by XenoSite were subsequently validated as correct predictions by revaluation of the source literature. Moreover, XenoSite output is interpretable as a probability, which reflects both the confidence of the model that a particular atom is metabolized and the statistical likelihood that its prediction for that atom is correct.
ISSN:1549-9596
1549-960X
DOI:10.1021/ci400518g