ArticleMolecular diversity2019
Development of an in silico prediction model for chemical-induced urinary tract toxicity by using naïve Bayes classifier.
Article in Molecular diversity, 2019. 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.
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- Recent advances in AI-based toxicity prediction for drug discovery.Frontiers in chemistry · 2025Review
- IntegratedCurrent medicinal chemistry · 2025Article
- Improvement of a prediction model for heart failure survival through explainable artificial intelligence.Frontiers in cardiovascular medicine · 2023Article
- The Discovery of Potential SARS-CoV-2 Natural Inhibitors among 4924 African Metabolites Targeting the Papain-like Protease: A Multi-Phase In Silico Approach.Metabolites · 2022Article
- Discovery of Potential SARS-CoV-2 Papain-like Protease Natural Inhibitors Employing a Multi-Phase In Silico Approach.Life (Basel, Switzerland) · 2022Article
- Isolation and In Silico Inhibitory Potential against SARS-CoV-2 RNA Polymerase of the Rare Kaempferol 3-Plants (Basel, Switzerland) · 2022Article
- The Computational Preventive Potential of the Rare Flavonoid, Patuletin, Isolated fromPlants (Basel, Switzerland) · 2022Article
- Multi-Step In Silico Discovery of Natural Drugs against COVID-19 Targeting Main Protease.International journal of molecular sciences · 2022Article
- Ligand and Structure-Based In Silico Determination of the Most Promising SARS-CoV-2 nsp16-nsp10 2'-Molecules (Basel, Switzerland) · 2022Article
- Isolation and In Silico SARS-CoV-2 Main Protease Inhibition Potential of Jusan Coumarin, a New Dicoumarin fromMolecules (Basel, Switzerland) · 2022Article
- Jusanin, a New Flavonoid fromMolecules (Basel, Switzerland) · 2022Article
- Isolation and In Silico Anti-SARS-CoV-2 Papain-Like Protease Potentialities of Two Rare 2-Phenoxychromone Derivatives fromMolecules (Basel, Switzerland) · 2022Article
- Prediction and Screening Model for Products Based on Fusion Regression and XGBoost Classification.Computational intelligence and neuroscience · 2022Article
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- FP-ADMET: a compendium of fingerprint-based ADMET prediction models.Journal of cheminformatics · 2021Article
- Early lung cancer diagnostic biomarker discovery by machine learning methods.Translational oncology · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The urinary tract toxicity is one of the major reasons for investigational drugs not coming into the market and even marketed drugs being restricted or withdrawn. The objective of this investigation is to develop an easily interpretable and practically applicable in silico prediction model of chemical-induced urinary tract toxicity by using naïve Bayes classifier. The genetic algorithm was used to select important molecular descriptors related to urinary tract toxicity, and the ECFP-6 fingerprint descriptors were applied to the urinary tract toxic/non-toxic fragments production. The established naïve Bayes classifier (NB-2) produced 87.3% overall accuracy of fivefold cross-validation for the training set and 84.2% for the external test set, which can be employed for the chemical-induced urinary tract toxicity assessment. Furthermore, six important molecular descriptors (e.g., number of N atoms, AlogP, molecular weight, number of H acceptors, number of H donors and molecular fractional polar surface area) and toxic and non-toxic fragments were obtained, which would help medicinal chemists interpret the mechanisms of urinary tract toxicity, and even provide theoretical guidance for hit and lead optimization.
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