ArticleInternational journal of molecular sciences2020
Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 38 citations in OpenAlex.
- Context-Aware Multilevel Classification of Semantic Relations in Drug-Adverse Drug Reaction (ADR) Networks-Predicting Drug-Induced Liver Injury (DILI) as a Case Study.Chemical research in toxicology · 2026Article
- Drug-induced liver injury prediction based on graph convolutional networks and toxicogenomics.PLoS computational biology · 2025Article
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- Deep Learning Prediction of Drug-Induced Liver Toxicity by Manifold Embedding of Quantum Information of Drug Molecules.Pharmaceutical research · 2025Article
- Leveraging machine learning models in evaluating ADMET properties for drug discovery and development.ADMET & DMPK · 2025Review
- A Comparison of Machine Learning Approaches for predicting Hepatotoxicity potential using Chemical Structure and Targeted Transcriptomic Data.Computational toxicology (Amsterdam, Netherlands) · 2024Article
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Article
- ChemBioSim: Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities.Journal of chemical information and modeling · 2021Article
- Deep Graph Learning with Property Augmentation for Predicting Drug-Induced Liver Injury.Chemical research in toxicology · 2021Article
- Fast Identification of Adverse Drug Reactions (ADRs) of Digestive and Nervous Systems of Organic Drugs by In Silico Models.Molecules (Basel, Switzerland) · 2021Article
- Identification of average molecular weight (AMW) as a useful chemical descriptor to discriminate liver injury-inducing drugs.PloS one · 2021Article
- Host Blood Gene Signatures Can Detect the Progression to Severe and Cerebral Malaria.Frontiers in cellular and infection microbiology · 2021Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting "the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans" (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.
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