ArticleJournal of computer-aided molecular design2011
Mixed learning algorithms and features ensemble in hepatotoxicity prediction.
Article in Journal of computer-aided molecular design, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Machine Learning-Based Models to Predict Drug-Induced Liver Injury (DILI) to Assist Medicinal Chemistry.Journal of medicinal chemistry · 2026Review
- A Multimodal deep learning method for predicting drug-induced liver injury using structural and sequential molecular representations.Journal of computer-aided molecular design · 2026Article
- Metabolic Profiling and Detoxification of Eupalinolide A and B in Human Liver Microsomal Systems.Toxics · 2026Article
- Drug-induced liver injury prediction based on graph convolutional networks and toxicogenomics.PLoS computational biology · 2025Article
- Role of artificial intelligence in revolutionizing drug discovery.Fundamental research · 2025Review
- Recent advances in AI-based toxicity prediction for drug discovery.Frontiers in chemistry · 2025Review
- The application of chemical similarity measures in an unconventional modeling framework c-RASAR along with dimensionality reduction techniques to a representative hepatotoxicity dataset.Scientific reports · 2024Article
- Hybrid non-animal modeling: A mechanistic approach to predict chemical hepatotoxicity.Journal of hazardous materials · 2024Article
- InterDILI: interpretable prediction of drug-induced liver injury through permutation feature importance and attention mechanism.Journal of cheminformatics · 2024Article
- Comprehensive hepatotoxicity prediction: ensemble model integrating machine learning and deep learning.Frontiers in pharmacology · 2024Article
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Article
- Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay.Journal of hazardous materials · 2022Article
- Developing an artificial intelligence method for screening hepatotoxic compounds in traditional Chinese medicine and Western medicine combination.Chinese medicine · 2022Article
- An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning.Molecules (Basel, Switzerland) · 2022Article
- Machine Learning Models for Predicting Liver Toxicity.Methods in molecular biology (Clifton, N.J.) · 2022Article
- In Silico Models for Hepatotoxicity.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Predictive Model for Drug-Induced Liver Injury Using Deep Neural Networks Based on Substructure Space.Molecules (Basel, Switzerland) · 2021Article
- Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets.International journal of molecular sciences · 2021Article
- The Promise of AI for DILI Prediction.Frontiers in artificial intelligence · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug-induced liver injury, although infrequent, is an important safety concern that can lead to fatality in patients and failure in drug developments. In this study, we have used an ensemble of mixed learning algorithms and mixed features for the development of a model to predict hepatic effects. This robust method is based on the premise that no single learning algorithm is optimum for all modelling problems. An ensemble model of 617 base classifiers was built from a diverse set of 1,087 compounds. The ensemble model was validated internally with five-fold cross-validation and 25 rounds of y-randomization. In the external validation of 120 compounds, the ensemble model had achieved an accuracy of 75.0%, sensitivity of 81.9% and specificity of 64.6%. The model was also able to identify 22 of 23 withdrawn drugs or drugs with black box warning against hepatotoxicity. Dronedarone which is associated with severe liver injuries, announced in a recent FDA drug safety communication, was predicted as hepatotoxic by the ensemble model. It was found that the ensemble model was capable of classifying positive compounds (with hepatic effects) well, but less so on negatives compounds when they were structurally similar. The ensemble model built in this study is made available for public use.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.