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Molecules 2017, 22(12), 2123; doi:10.3390/molecules22122123

QNA-Based Prediction of Sites of Metabolism

1
Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
2
Pirogov Russian National Research Medical University, 1 Ostrovityanova Str., 117997 Moscow, Russia
*
Author to whom correspondence should be addressed.
Received: 9 November 2017 / Revised: 30 November 2017 / Accepted: 28 November 2017 / Published: 1 December 2017
(This article belongs to the Special Issue Frontiers in Computational Chemistry for Drug Discovery)
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Abstract

Metabolism of xenobiotics (Greek xenos: exogenous substances) plays an essential role in the prediction of biological activity and testing for the subsequent research and development of new drug candidates. Integration of various methods and techniques using different computational and experimental approaches is one of the keys to a successful metabolism prediction. While multiple structure-based and ligand-based approaches to metabolism prediction exist, the most important problem arises at the first stage of metabolism prediction: detection of the sites of metabolism (SOMs). In this paper, we describe the application of Quantitative Neighborhoods of Atoms (QNA) descriptors for prediction of the SOMs using potential function method, as well as several different machine learning techniques: naïve Bayes, random forest classifier, multilayer perceptron with back propagation and convolutional neural networks, and deep neural networks. View Full-Text
Keywords: sites of metabolism; SOM; cytochromes; quantitative neighborhoods of atoms; QNA; computational prediction sites of metabolism; SOM; cytochromes; quantitative neighborhoods of atoms; QNA; computational prediction
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Tarasova, O.; Rudik, A.; Dmitriev, A.; Lagunin, A.; Filimonov, D.; Poroikov, V. QNA-Based Prediction of Sites of Metabolism. Molecules 2017, 22, 2123.

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