ArticleMolecular informatics2022
The Relevance of Goodness-of-fit, Robustness and Prediction Validation Categories of OECD-QSAR Principles with Respect to Sample Size and Model Type.
Article in Molecular informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Pharmacological stratification of public bioactivity databases: a reusable, OECD-anchored curation and benchmarking framework demonstrated for opioid receptors.Digital discovery · 2026Article
- Interpretable Quantitative Structure-Activity Relationship (QSAR) for identification of potent antifungal activity agents towards Candida albicans ATCC 2091.Molecular diversity · 2026Article
- Benchmarking ML in ADMET predictions: the practical impact of feature representations in ligand-based models.Journal of cheminformatics · 2025Article
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- Molecular and Descriptor Spaces for Predicting Initial Rate of Catalytic Homogeneous Quinoline Hydrogenation with Ru, Rh, Os, and Ir Catalysts.ACS omega · 2025Article
- Validation of the reduced version of the Brazilian Scale of Moral Distress in Nurses.Revista latino-americana de enfermagem · 2025Article
- Design and screening of novel 1,2,4-Triazole-3-thione derivatives as DCN1 inhibitors for anticardiac fibrosis based on 3D-QSAR modeling and molecular dynamics.Frontiers in pharmacology · 2025Article
- Article
- Patch seriation to visualize data and model parameters.Journal of cheminformatics · 2023Article
- Computational modeling of PET imaging agents for vesicular acetylcholine transporter (VAChT) protein binding affinity: application of 2D-QSAR modeling and molecular docking techniques.In silico pharmacology · 2023Article
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Authors and funding
5 authors.
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Abstract
We investigated the relevance of the validation principles on the Quantitative Structure Activity Relationship models issued by Organization for Economic and Co-operation and Development. We checked the goodness-of-fit, robustness and predictivity categories in linear and nonlinear models using benchmark datasets. Most of our conclusions are drawn using the sample size dependence of the different validation parameters. We found that the goodness-of-fit parameters misleadingly overestimate the models on small samples. In the case of neural network and support vector models, the feasibility of the goodness-of-fit parameters often might be questioned. We propose to use the simplest y-scrambling method to estimate chance correlation. We found that the leave-one-out and leave-many-out cross-validation parameters can be rescaled to each other in all models and the computationally feasible method should be chosen depending on the model type. We assessed the interdependence of the validation parameters by calculating their rank correlations. Goodness of fit and robustness correlate quite well over a sample size for linear models and one of the approaches might be redundant. In the rank correlation between internal and external validation parameters, we found that the assignment of good and bad modellable data to the training or the test causes negative correlations.
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