Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1158
Title: Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies
Authors: Onay, Aytun
Onay, Melih
Abul, Osman
31072
Keywords: Machine Learning
Support Vector Machine
Drug Discovery
Toxprint Chemotypes
Approved & Withdrawn Drug
Issue Date: Apr-2017
Publisher: Elsevier Ireland Ltd..
Source: Onay, A., Onay, M., & Abul, O. (2017). Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies. Computer methods and programs in biomedicine, 142, 9-19.
Abstract: Background and objectives: Early-phase virtual screening of candidate drug molecules plays a key role in pharmaceutical industry from data mining and machine learning to prevent adverse effects of the drugs. Computational classification methods can distinguish approved drugs from withdrawn ones. We focused on 6 data sets including maximum 110 approved and 110 withdrawn drugs for all and nervous system diseases to distinguish approved drugs from withdrawn ones. Methods: In this study, we used support vector machines (SVMs) and ensemble methods (EMs) such as boosted and bagged trees to classify drugs into approved and withdrawn categories. Also, we used CORINA Symphony program to identify Toxprint chemotypes including over 700 predefined chemotypes for determination of risk and safety assesment of candidate drug molecules. In addition, we studied nervous system withdrawn drugs to determine the key fragments with The ParMol package including gSpan algorithm. Results: According to our results, the descriptors named as the number of total chemotypes and bond CN_amine_aliphatic_generic were more significant descriptors. The developed Medium Gaussian SVM model reached 78% prediction accuracy on test set for drug data set including all disease. Here, bagged tree and linear SVM models showed 89% of accuracies for phycholeptics and psychoanaleptics drugs. A set of discriminative fragments in nervous system withdrawn drug (NSWD) data sets was obtained. These fragments responsible for the drugs removed from market were benzene, toluene, N,N-dimethylethylamine, crotylamine, 5-methyl-2,4-heptadiene, octatriene and carbonyl group. Conclusion: This paper covers the development of computational classification methods to distinguish approved drugs from withdrawn ones. In addition, the results of this study indicated the identification of discriminative fragments is of significance to design a new nervous system approved drugs with interpretation of the structures of the NSWDs. (C) 2017 Elsevier B.V. All rights reserved.
URI: https://www.sciencedirect.com/science/article/pii/S0169260716305247?via%3Dihub
https://hdl.handle.net/20.500.11851/1158
ISSN: 0169-2607
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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