ArticleJournal of cheminformatics2020
Structure-activity relationship-based chemical classification of highly imbalanced Tox21 datasets.
Article in Journal of cheminformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- MSF-HierGNN: a multi-source substructure-fusion hierarchical GNN method and web server to predict molecular property for drug design.Briefings in bioinformatics · 2026Article
- Semantic knowledge improves molecular machine learning for chemical toxicity prediction.iScience · 2026Article
- Adjusted imbalance ratio leads to effective AI-based drug discovery against infectious disease.Scientific reports · 2025Article
- Interpretable machine learning models for predicting the antitumor effects of metal and metal oxide nanomaterials.RSC advances · 2025Article
- Developing muscarinic receptor M1 classification models utilizing transfer learning and generative AI techniques.Scientific reports · 2025Article
- Predicting the Toxicity of Drug Molecules with Selecting Effective Descriptors Using a Binary Ant Colony Optimization (BACO) Feature Selection Approach.Molecules (Basel, Switzerland) · 2025Article
- Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Hybrid Class Balancing Approach for Chemical Compound Toxicity Prediction.Current computer-aided drug design · 2025Article
- A refined set of RxNorm drug names for enhancing unstructured data analysis in drug safety surveillance.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals.Journal of environmental science and health. Part C, Toxicology and carcinogenesis · 2025Review
- Article
- Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.Biomolecules · 2024Review
- Development of a comprehensive open access "molecules with androgenic activity resource (MAAR)" to facilitate risk assessment of chemicals.Experimental biology and medicine (Maywood, N.J.) · 2024Article
- BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.Frontiers in public health · 2024Article
- A quantum-based oversampling method for classification of highly imbalanced and overlapped data.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- Machine learning for predicting intrahospital mortality in ST-elevation myocardial infarction patients with type 2 diabetes mellitus.BMC cardiovascular disorders · 2023Article
- A chemical structure and machine learning approach to assess the potential bioactivity of endogenous metabolites and their association with early-childhood hs-CRP levels.bioRxiv : the preprint server for biology · 2023Article
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Federated Learning in Computational Toxicology: An Industrial Perspective on the Effiris Hackathon.Chemical research in toxicology · 2023Article
- Prediction of pathogenic single amino acid substitutions using molecular fragment descriptors.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The specificity of toxicant-target biomolecule interactions lends to the very imbalanced nature of many toxicity datasets, causing poor performance in Structure-Activity Relationship (SAR)-based chemical classification. Undersampling and oversampling are representative techniques for handling such an imbalance challenge. However, removing inactive chemical compound instances from the majority class using an undersampling technique can result in information loss, whereas increasing active toxicant instances in the minority class by interpolation tends to introduce artificial minority instances that often cross into the majority class space, giving rise to class overlapping and a higher false prediction rate. In this study, in order to improve the prediction accuracy of imbalanced learning, we employed SMOTEENN, a combination of Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms, to oversample the minority class by creating synthetic samples, followed by cleaning the mislabeled instances. We chose the highly imbalanced Tox21 dataset, which consisted of 12 in vitro bioassays for > 10,000 chemicals that were distributed unevenly between binary classes. With Random Forest (RF) as the base classifier and bagging as the ensemble strategy, we applied four hybrid learning methods, i.e., RF without imbalance handling (RF), RF with Random Undersampling (RUS), RF with SMOTE (SMO), and RF with SMOTEENN (SMN). The performance of the four learning methods was compared using nine evaluation metrics, among which F
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