In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method
1
Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
2
School of Computer Science and Technology, Anhui University, Hefei 230601, China
3
School of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan 243032, China
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(17), 4106; https://doi.org/10.3390/ijms20174106
Received: 25 May 2019
/
Revised: 20 August 2019
/
Accepted: 20 August 2019
/
Published: 22 August 2019
(This article belongs to the Special Issue Special Protein or RNA Molecules Computational Identification 2019)
Drug-induced liver injury (DILI) is a major factor in the development of drugs and the safety of drugs. If the DILI cannot be effectively predicted during the development of the drug, it will cause the drug to be withdrawn from markets. Therefore, DILI is crucial at the early stages of drug research. This work presents a 2-class ensemble classifier model for predicting DILI, with 2D molecular descriptors and fingerprints on a dataset of 450 compounds. The purpose of our study is to investigate which are the key molecular fingerprints that may cause DILI risk, and then to obtain a reliable ensemble model to predict DILI risk with these key factors. Experimental results suggested that 8 molecular fingerprints are very critical for predicting DILI, and also obtained the best ratio of molecular fingerprints to molecular descriptors. The result of the 5-fold cross-validation of the ensemble vote classifier method obtain an accuracy of 77.25%, and the accuracy of the test set was 81.67%. This model could be used for drug-induced liver injury prediction.
View Full-Text
Keywords:
drug-induced liver injury; quantitative structure–activity relationship (QSAR); molecular fingerprints; ensemble classifier
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
- Supplementary File 1:
ZIP-Document (ZIP, 936 KiB)
MDPI and ACS Style
Wang, Y.; Xiao, Q.; Chen, P.; Wang, B. In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method. Int. J. Mol. Sci. 2019, 20, 4106. https://doi.org/10.3390/ijms20174106
AMA Style
Wang Y, Xiao Q, Chen P, Wang B. In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method. International Journal of Molecular Sciences. 2019; 20(17):4106. https://doi.org/10.3390/ijms20174106
Chicago/Turabian StyleWang, Yangyang, Qingxin Xiao, Peng Chen, and Bing Wang. 2019. "In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method" International Journal of Molecular Sciences 20, no. 17: 4106. https://doi.org/10.3390/ijms20174106
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
