ArticleMolecular diversity2021
Modeling and insights into molecular basis of low molecular weight respiratory sensitizers.
Article in Molecular diversity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- ChronicDPipredictor: an interpretable deep learning framework for chemical chronic and subchronic toxicity assessment.Molecular diversity · 2026Article
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- Can machine learning 'transform' peptides/peptidomimetics into small molecules? A case study with ghrelin receptor ligands.Molecular diversity · 2023Article
- Chemical rules for optimization of chemical mutagenicity via matched molecular pairs analysis and machine learning methods.Journal of cheminformatics · 2023Article
- Modeling and insights into the structural characteristics of drug-induced autoimmune diseases.Frontiers in immunology · 2022Article
- SApredictor: An Expert System for Screening Chemicals Against Structural Alerts.Frontiers in chemistry · 2022Article
- In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods.Molecular diversity · 2021Article
- In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity.Frontiers in pharmacology · 2021Article
- Simplex representation of molecular structure as universal QSAR/QSPR tool.Structural chemistry · 2021Review
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Authors and funding
6 authors.
Funding
Abstract
Respiratory sensitization has been considered an important toxicological endpoint, because of the severe risk to human health. A great part of sensitization events were caused by low molecular weight (< 1000) respiratory sensitizers in the past decades. However, there is currently no widely accepted test method that can identify prospective low molecular weight respiratory sensitisers. Herein, we performed the study of modeling and insights into molecular basis of low molecular weight respiratory sensitizers with a high-quality data set containing 136 respiratory sensitizers and 518 nonsensitizers. We built a number of classification models by using OCHEM tools, and a consensus model was developed based on the ten best individual models. The consensus model showed good predictive ability with a balanced accuracy of 0.78 and 0.85 on fivefold cross-validation and external validation, respectively. The readers can predict the respiratory sensitization of organic compounds via https://ochem.eu/article/114857 . The effect of several molecular properties on respiratory sensitization was also evaluated. The results indicated that these properties differ significantly between respiratory sensitizers and nonsensitizers. Furthermore, 14 privileged substructures responsible for respiratory sensitization were identified. We hope the models and the findings could provide useful help for environmental risk assessment.
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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 OpenQuestion graph.