Predicting drug-induced liver injury in human with Naïve Bayes classifier approach
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- 作者：Hui Zhang ; Lan Ding ; Yi Zou ; Shui-Qing Hu…
- 关键词：Drug ; induced liver injury ; In silico prediction ; Naïve Bayes classifier ; Molecular descriptors ; Extended connectivity fingerprints (ECFP_6)
- 刊名：Journal of Computer-Aided Molecular Design
- 出版年：2016
- 出版时间：October 2016
- 年：2016
- 卷：30
- 期：10
- 页码：889-898
- 全文大小：794 KB
- 刊物类别：Chemistry and Materials Science
- 刊物主题：Chemistry
Physical Chemistry
Computer Applications in Chemistry
Animal Anatomy, Morphology and Histology - 出版者：Springer Netherlands
- ISSN：1573-4951
- 卷排序：30
文摘
Drug-induced liver injury (DILI) is one of the major safety concerns in drug development. Although various toxicological studies assessing DILI risk have been developed, these methods were not sufficient in predicting DILI in humans. Thus, developing new tools and approaches to better predict DILI risk in humans has become an important and urgent task. In this study, we aimed to develop a computational model for assessment of the DILI risk with using a larger scale human dataset and Naïve Bayes classifier. The established Naïve Bayes prediction model was evaluated by 5-fold cross validation and an external test set. For the training set, the overall prediction accuracy of the 5-fold cross validation was 94.0 %. The sensitivity, specificity, positive predictive value and negative predictive value were 97.1, 89.2, 93.5 and 95.1 %, respectively. The test set with the concordance of 72.6 %, sensitivity of 72.5 %, specificity of 72.7 %, positive predictive value of 80.4 %, negative predictive value of 63.2 %. Furthermore, some important molecular descriptors related to DILI risk and some toxic/non-toxic fragments were identified. Thus, we hope the prediction model established here would be employed for the assessment of human DILI risk, and the obtained molecular descriptors and substructures should be taken into consideration in the design of new candidate compounds to help medicinal chemists rationally select the chemicals with the best prospects to be effective and safe.