QNA-Based Prediction of Sites of Metabolism
AbstractMetabolism 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
- Supplementary File 1:
Supplementary (ZIP, 2461 KB)
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Tarasova, O.; Rudik, A.; Dmitriev, A.; Lagunin, A.; Filimonov, D.; Poroikov, V. QNA-Based Prediction of Sites of Metabolism. Molecules 2017, 22, 2123.
Tarasova O, Rudik A, Dmitriev A, Lagunin A, Filimonov D, Poroikov V. QNA-Based Prediction of Sites of Metabolism. Molecules. 2017; 22(12):2123.Chicago/Turabian Style
Tarasova, Olga; Rudik, Anastassia; Dmitriev, Alexander; Lagunin, Alexey; Filimonov, Dmitry; Poroikov, Vladimir. 2017. "QNA-Based Prediction of Sites of Metabolism." Molecules 22, no. 12: 2123.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.