ArticleJournal of computer-aided molecular design2016
Predicting drug-induced liver injury in human with Naïve Bayes classifier approach.
Article in Journal of computer-aided molecular design, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Pharmaco-EEG-Based Classification of Psychotropic Activity of a Novel Chromone-Containing Allylmorpholine in Rats.Advanced pharmaceutical bulletin · 2025Article
- HepatoToxicity Portal (HTP): an integrated database of drug-induced hepatotoxicity knowledgebase and graph neural network-based prediction model.Journal of cheminformatics · 2025Article
- Deep Learning Prediction of Drug-Induced Liver Toxicity by Manifold Embedding of Quantum Information of Drug Molecules.Pharmaceutical research · 2025Article
- Prospects of Electrocorticography in Neuropharmacological Studies in Small Laboratory Animals.Brain sciences · 2024Review
- Machine Learning to Predict Drug-Induced Liver Injury and Its Validation on Failed Drug Candidates in Development.Toxics · 2024Article
- InterDILI: interpretable prediction of drug-induced liver injury through permutation feature importance and attention mechanism.Journal of cheminformatics · 2024Article
- Chemistry-Based Modeling on Phenotype-Based Drug-Induced Liver Injury Annotation: From Public to Proprietary Data.Chemical research in toxicology · 2023Article
- Computational models for predicting liver toxicity in the deep learning era.Frontiers in toxicology · 2023Review
- Review
- Machine Learning Models for Predicting Liver Toxicity.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets.International journal of molecular sciences · 2021Article
- Predicting rifampicin resistance mutations in bacterial RNA polymerase subunit beta based on majority consensus.BMC bioinformatics · 2021Article
- Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure.Biology direct · 2021Article
- An ensemble learning approach for modeling the systems biology of drug-induced injury.Biology direct · 2021Article
- Review
- Predicting Drug-Induced Liver Injury Using Convolutional Neural Network and Molecular Fingerprint-Embedded Features.ACS omega · 2020Article
- In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method.International journal of molecular sciences · 2019Article
- Herb-Induced Liver Injury: Phylogenetic Relationship, Structure-Toxicity Relationship, and Herb-Ingredient Network Analysis.International journal of molecular sciences · 2019Review
- Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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