ArticleScientific reports2017
CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers, 1 of them a synthesis that pooled it.
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Who cites it
87 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Toward Explainable Carcinogenicity Prediction: An Integrated Cheminformatics Approach and Consensus Framework for Possibly Carcinogenic Chemicals.Journal of chemical information and modeling · 2025Guideline
- LMF-CP: An Interpretable Multimodal Late-Fusion Framework for Compound Carcinogenicity Prediction.International journal of molecular sciences · 2026Article
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- Carcinogenicity prediction via multi-task learning of cross-organ representations with attention mechanisms.Briefings in bioinformatics · 2026Article
- Evaluating the ability of in silico identified hit compounds to bind Staphylococcus aureus LcpAMolecular diversity · 2026Article
- Artificial Intelligence and Machine Learning-Based Approaches for Genetic Damage Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- In silico prediction, molecular docking and simulation of natural flavonoid apigenin and xanthoangelol E against human metapneumovirus.In silico pharmacology · 2026Article
- Machine learning driven LDFrontiers in oncology · 2026Article
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- Irilone and Lupinisoflavone C as Potential Plant-Based Modulators of S1PR1 for Neuroimmune Modulation in Multiple Sclerosis: Insights from Molecular Docking and Dynamics.Journal of molecular neuroscience : MN · 2025Article
- Enhancing multi-task in vivo toxicity prediction via integrated knowledge transfer of chemical knowledge and in vitro toxicity information.Journal of cheminformatics · 2025Article
- Establishment of an Integrated Model for Predicting Compound Mutagenicity with a Feature Importance Analysis.Journal of chemical information and modeling · 2025Article
- Structure-guided identification of potential inhibitors of MurB from S. typhimurium LT2 strain: towards therapeutic development against multidrug resistance.Molecular diversity · 2025Article
- Application of chemical similarity and bioisosteres to find allosteric inhibitors of type 2 lipid kinase γ.Scientific reports · 2025Article
- Structure-guided screening identified bioactive phytoconstituents Hernandonine and Anolobine as potential inhibitors of dual specificity protein kinase CLK1.Scientific reports · 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
- Molecular simulation-based investigation of thiazole derivatives as potential LasR inhibitors of Pseudomonas aeruginosa.PloS one · 2025Article
- Exploring bioactive phytochemicals as ULK1 activators for enhancing cytoprotective autophagy in amyotrophic lateral sclerosis.Frontiers in pharmacology · 2025Article
- Discovering novel inhibitors of RfaH from Klebsiella pneumoniae to combat antimicrobial resistance.Archives of microbiology · 2024Article
27 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
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Abstract
Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70.1 ± 2.9%, sensitivity of 67.0 ± 5.0%, and specificity of 73.1 ± 4.4% in five-fold cross-validation and an accuracy of 70.0%, sensitivity of 65.2%, and specificity of 76.5% in external validation. In comparison with some recent methods, the ensemble models outperform some machine learning-based approaches and yield equal accuracy and higher specificity but lower sensitivity than rule-based expert systems. It is also found that the ensemble models could be further improved if more data were available. As an application, the ensemble models are employed to discover potential carcinogens in the DrugBank database. The results indicate that the proposed models are helpful in predicting the carcinogenicity of chemicals. A web server called CarcinoPred-EL has been built for these models ( http://ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/ ).
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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.