ArticleMolecules (Basel, Switzerland)2021
Predictive Model for Drug-Induced Liver Injury Using Deep Neural Networks Based on Substructure Space.
Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 24 citations in OpenAlex.
- A Multimodal deep learning method for predicting drug-induced liver injury using structural and sequential molecular representations.Journal of computer-aided molecular design · 2026Article
- 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
- 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
- Computational models for predicting liver toxicity in the deep learning era.Frontiers in toxicology · 2023Review
- Artificial Intelligence-Assisted Image Analysis of Acetaminophen-Induced Acute Hepatic Injury in Sprague-Dawley Rats.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Drug-induced liver injury (DILI) is a major concern for drug developers, regulators, and clinicians. However, there is no adequate model system to assess drug-associated DILI risk in humans. In the big data era, computational models are expected to play a revolutionary role in this field. This study aimed to develop a deep neural network (DNN)-based model using extended connectivity fingerprints of diameter 4 (ECFP4) to predict DILI risk. Each data set for the predictive model was retrieved and curated from DILIrank, LiverTox, and other literature. The best model was constructed through ten iterations of stratified 10-fold cross-validation, and the applicability domain was defined based on integer ECFP4 bits of the training set which represented substructures. For the robustness test, we employed the concept of the endurance level. The best model showed an accuracy of 0.731, a sensitivity of 0.714, and a specificity of 0.750 on the validation data set in the complete applicability domain. The model was further evaluated with four external data sets and attained an accuracy of 0.867 on 15 drugs with DILI cases reported since 2019. Overall, the results suggested that the ECFP4-based DNN model represents a new tool to identify DILI risk for the evaluation of drug safety.
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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.