Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies.
OBJECTIVE
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 Computational classification methods can distinguish approved drugs from withdrawn ones. We focused on 6 data sets including maximum 110 approved and 110 withdrawn drugs for all and nervous system diseases to distinguish approved drugs from withdrawn ones.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vector machines (SVMs) and ensemble methods (EMs) such as boosted and bagged trees to classify drugs into approved and withdrawn categories.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 In addition, we studied nervous system withdrawn drugs to determine the key fragments with The ParMol package including gSpan algorithm.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 A set of discriminative fragments in nervous system withdrawn drug (NSWD) data sets was obtained.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.


CONCLUSIONS
This paper covers the development of computational classification methods to distinguish approved drugs from withdrawn ones.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.
Department of Computer Engineering, TOBB University of Economics & Technology, 06560, Ankara, Turkey.
Aytun
A
Onay
Department of Environmental Engineering, Computational & Experimental Biochemistry Lab, Yuzuncu Yil University, 65080, Van, Turkey. Electronic address: melihonay@yyu.edu.tr.
Melih
M
Onay
Department of Computer Engineering, TOBB University of Economics & Technology, 06560, Ankara, Turkey.
Osman
O
Abul
D000465:Algorithms; D019540:Area Under Curve; D001554:Benzene / Q000633:toxicity; D002244:Carbon; D003198:Computer Simulation; D057225:Data Mining; D017277:Drug Approval; D016903:Drug Monitoring; D005021:Ethylamines / Q000633:toxicity; D005189:False Positive Reactions; D006801:Humans; D016014:Linear Models; D009420:Nervous System / Q000187:drug effects*; D061214:Patient Safety; D010363:Pattern Recognition, Automated / Q000379:methods; D004364:Pharmaceutical Preparations / Q000737:chemistry; D011358:Product Surveillance, Postmarketing / Q000592:standards; D012306:Risk; D056737:Safety-Based Drug Withdrawals / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D014050:Toluene / Q000633:toxicity
D005021:Ethylamines; D004364:Pharmaceutical Preparations; D014050:Toluene; D002244:Carbon; C047305:N,N-dimethylethylamine; D001554:Benzene
Approved & withdrawn drug; Drug discovery; Machine learning; Support vector machine; ToxPrint chemotypes