ArticleChemical research in toxicology2023
Transparency in Modeling through Careful Application of OECD's QSAR/QSPR Principles via a Curated Water Solubility Data Set.
Article in Chemical research in toxicology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Computational Identification of Potential Novel Allosteric IHF Inhibitors Using QSAR Modeling to Inhibit Plasmid-Mediated Antibiotic Resistance.International journal of molecular sciences · 2026Article
- Data curation in cheminformatics: importance and implementation.Journal of cheminformatics · 2026Review
- SPARFlow: a KNIME workflow for integrated structure-activity or structure-property relationship analysis.Journal of cheminformatics · 2026Article
- ADFC-ATP: Attention-Guided Dual-View Fusion and Contrastive Pretraining for Robust Aquatic Toxicity Prediction.Journal of cellular and molecular medicine · 2026Article
- CombinedJournal of chemical information and modeling · 2025Article
- A unified approach to inferring chemical compounds with the desired aqueous solubility.Journal of cheminformatics · 2025Article
- Article
- Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure-activity relationship (q-RASAR) with the application of machine learning.Critical reviews in toxicology · 2024Review
- Improving predictions of compound amenability for liquid chromatography-mass spectrometry to enhance non-targeted analysis.Analytical and bioanalytical chemistry · 2024Article
- Will we ever be able to accurately predict solubility?Scientific data · 2024Article
- Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling.Journal of cheminformatics · 2024Article
- The pursuit of accurate predictive models of the bioactivity of small molecules.Chemical science · 2024Review
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Authors and funding
6 authors.
Funding
Abstract
The need for careful assembly, training, and validation of quantitative structure-activity/property models (QSAR/QSPR) is more significant than ever as data sets become larger and sophisticated machine learning tools become increasingly ubiquitous and accessible to the scientific community. Regulatory agencies such as the United States Environmental Protection Agency must carefully scrutinize each aspect of a resulting QSAR/QSPR model to determine its potential use in environmental exposure and hazard assessment. Herein, we revisit the goals of the Organisation for Economic Cooperation and Development (OECD) in our application and discuss the validation principles for structure-activity models. We apply these principles to a model for predicting water solubility of organic compounds derived using random forest regression, a common machine learning approach in the QSA/PR literature. Using public sources, we carefully assembled and curated a data set consisting of 10,200 unique chemical structures with associated water solubility measurements. This data set was then used as a focal narrative to methodically consider the OECD's QSA/PR principles and how they can be applied to random forests. Despite some expert, mechanistically informed supervision of descriptor selection to enhance model interpretability, we achieved a model of water solubility with comparable performance to previously published models (5-fold cross validated performance 0.81
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