ArticleJournal of chemical information and modeling2018
In Silico Pharmacoepidemiologic Evaluation of Drug-Induced Cardiovascular Complications Using Combined Classifiers.
Article in Journal of chemical information and modeling, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Beyond Molecular Structures: Investigating Demographic Factors in Drug-Induced Cardiotoxicity Prediction Models.Journal of chemical information and modeling · 2026Article
- In silico prediction of drug-induced cardiotoxicity with ensemble machine learning and structural pattern recognition.Molecular diversity · 2026Article
- Multimodal fusion of EHR and ECG based on deep learning for predicting new-onset coronary heart disease in cancer patients.Frontiers in cardiovascular medicine · 2026Article
- Review
- PrevCardioOncAI: Machine Learning Algorithms for Predicting Cardiovascular Disease in Cancer Survivors.Journal of the American Heart Association · 2025Observational
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- Predicting non-chemotherapy drug-induced agranulocytosis toxicity through ensemble machine learning approaches.Frontiers in pharmacology · 2024Article
- Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline.Reviews in cardiovascular medicine · 2023Review
- Unraveling Structural Alerts in Marketed Drugs for Improving Adverse Outcome Pathway Framework of Drug-Induced QT Prolongation.International journal of molecular sciences · 2023Article
- Chemical Distance Measurement and System Pharmacology Approach Uncover the Novel Protective Effects of Biotransformed Ginsenoside C-Mc against UVB-Irradiated Photoaging.Oxidative medicine and cellular longevity · 2022Article
- Article
- ChemBioSim: Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities.Journal of chemical information and modeling · 2021Article
- Selection of safe artemisinin derivatives using a machine learning-based cardiotoxicity platform and in vitro and in vivo validation.Archives of toxicology · 2021Article
- Improving cardiotoxicity prediction in cancer treatment: integration of conventional circulating biomarkers and novel exploratory tools.Archives of toxicology · 2021Review
- Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening.Molecules (Basel, Switzerland) · 2020Article
- Machine Learning-Based Risk Assessment for Cancer Therapy-Related Cardiac Dysfunction in 4300 Longitudinal Oncology Patients.Journal of the American Heart Association · 2020Article
- Comprehensive assessment of side effects in COVID-19 drug pipeline from a network perspective.Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association · 2020Article
- Article
- A Computational Toxicology Approach to Screen the Hepatotoxic Ingredients in Traditional Chinese Medicines:Biomolecules · 2019Article
- Development of QSAR machine learning-based models to forecast the effect of substances on malignant melanoma cells.Oncology letters · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Drug-induced cardiovascular complications are the most common adverse drug events and account for the withdrawal or severe restrictions on the use of multitudinous postmarketed drugs. In this study, we developed new in silico models for systematic identification of drug-induced cardiovascular complications in drug discovery and postmarketing surveillance. Specifically, we collected drug-induced cardiovascular complications covering the five most common types of cardiovascular outcomes (hypertension, heart block, arrhythmia, cardiac failure, and myocardial infarction) from four publicly available data resources: Comparative Toxicogenomics Database, SIDER, Offsides, and MetaADEDB. Using these databases, we developed a combined classifier framework through integration of five machine-learning algorithms: logistic regression, random forest, k-nearest neighbors, support vector machine, and neural network. The totality of models included 180 single classifiers with area under receiver operating characteristic curves (AUC) ranging from 0.647 to 0.809 on 5-fold cross-validations. To develop the combined classifiers, we then utilized a neural network algorithm to integrate the best four single classifiers for each cardiovascular outcome. The combined classifiers had higher performance with an AUC range from 0.784 to 0.842 compared to single classifiers. Furthermore, we validated our predicted cardiovascular complications for 63 anticancer agents using experimental data from clinical studies, human pluripotent stem cell-derived cardiomyocyte assays, and literature. The success rate of our combined classifiers reached 87%. In conclusion, this study presents powerful in silico tools for systematic risk assessment of drug-induced cardiovascular complications. This tool is relevant not only in early stages of drug discovery but also throughout the life of a drug including clinical trials and postmarketing surveillance.
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