ArticleInternational journal of molecular sciences2021
Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Development and validation of a machine learning stratified prediction model for early warning of anti-tuberculosis drug-induced liver injury risk based on real-world data: a retrospective cohort study.BMC medical informatics and decision making · 2026Article
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- Enhancing HCV NS3 Inhibitor Classification with Optimized Molecular Fingerprints Using Random Forest.International journal of molecular sciences · 2025Article
- Deep Learning Prediction of Drug-Induced Liver Toxicity by Manifold Embedding of Quantum Information of Drug Molecules.Pharmaceutical research · 2025Article
- Gap-Δenergy, a New Metric of the Bond Energy State, Assisting to Predict Molecular Toxicity.ACS omega · 2024Article
- Predicting non-chemotherapy drug-induced agranulocytosis toxicity through ensemble machine learning approaches.Frontiers in pharmacology · 2024Article
- Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach.Healthcare informatics research · 2023Article
- Predicting the Severity of Adverse Events on Osteoporosis Drugs Using Attribute Weighted Logistic Regression.International journal of environmental research and public health · 2023Article
- ACP-ADA: A Boosting Method with Data Augmentation for Improved Prediction of Anticancer Peptides.International journal of molecular sciences · 2022Article
- Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning.Pharmaceutics · 2022Article
- An Explainable Supervised Machine Learning Model for Predicting Respiratory Toxicity of Chemicals Using Optimal Molecular Descriptors.Pharmaceutics · 2022Article
- Rapid Detection of Direct Compound Toxicity and Trailing Detection of Indirect Cell Metabolite Toxicity in a 96-Well Fluidic Culture Device for Cell-Based Screening Environments: Tactics in Six Sigma Quality Control Charts.Applied sciences (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Drug-induced liver toxicity is one of the significant safety challenges for the patient's health and the pharmaceutical industry. It causes termination of drug candidates in clinical trials and also the retractions of approved drugs from the market. Thus, it is essential to identify hepatotoxic compounds in the initial stages of drug development process. The purpose of this study is to construct quantitative structure activity relationship models using machine learning algorithms and systematical feature selection methods for molecular descriptor sets. The models were built from a large and diverse set of 1253 drug compounds and were validated internally with 10-fold cross-validation. In this study, we applied a variety of feature selection techniques to extract the optimal subset of descriptors as modeling features to improve the prediction performance. Experimental results suggested that the support vector machine-based classifier had achieved a better classification accuracy with reduced molecular descriptors. The final optimal model provides an accuracy of 0.811, a sensitivity of 0.840, a specificity of 0.783 and Mathew's correlation coefficient of 0.623 with an internal validation set. Furthermore, this model outperformed the prior studies while evaluated in both the internal and external test sets. The utilization of distinct optimal molecular descriptors as modeling features produce an in silico model with a superior performance.
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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 Socratic graph.