ArticleMolecules (Basel, Switzerland)2018
Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes.
Article in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- An Explainable Machine Learning-Based QSAR Framework for Predicting Thrombin Inhibitory Activity.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Clinical Variable-Based Machine Learning for Predicting Early mCRPC Using Exclusively Clinical Variables: Development and Multicenter External Validation.The Prostate · 2026Article
- Integrating AI in Medicinal Chemistry for Accelerated Drug Discovery: A Comprehensive SAR (CSAR) Optimization Strategy and Discovery of Potent ALDH3A1 Inhibitors.Journal of medicinal chemistry · 2026Article
- Interpretable machine learning-driven QSAR modeling for coagulation factor X inhibitors: from molecular descriptors to predictive potency.Journal of computer-aided molecular design · 2026Article
- Evaluating the generalizability of graph neural networks for predicting collision cross section.Journal of cheminformatics · 2024Article
- First report on exploration of structural features of natural compounds (NPACT database) for anti-breast cancer activity (MCF-7): QSAR-based virtual screening, molecular docking, ADMET, MD simulation, and DFT studies.In silico pharmacology · 2024Article
- Homology modeling, docking, and ADMET studies of benzoheterocyclic 4-aminoquinolines analogs as inhibitors ofJournal of Taibah University Medical Sciences · 2023Article
- Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review.Archives of toxicology · 2023Review
- Design, Biological Evaluation, and Computer-Aided Analysis of Dihydrothiazepines as Selective Antichlamydial Agents.Journal of medicinal chemistry · 2023Article
- QSAR, ADMET In Silico Pharmacokinetics, Molecular Docking and Molecular Dynamics Studies of Novel Bicyclo (Aryl Methyl) Benzamides as Potent GlyT1 Inhibitors for the Treatment of Schizophrenia.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Development of Machine Learning Models for Accurately Predicting and Ranking the Activity of Lead Molecules to Inhibit PRC2 Dependent Cancer.Pharmaceuticals (Basel, Switzerland) · 2021Article
- Predictive Capability of QSAR Models Based on the CompTox Zebrafish Embryo Assays: An Imbalanced Classification Problem.Molecules (Basel, Switzerland) · 2021Article
- General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps.Journal of chemical information and modeling · 2021Article
- Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods.Journal of chemical information and modeling · 2021Article
- Article
- An In Silico Approach for Assessment of the Membrane Transporter Activities of Phenols: A Case Study Based on Computational Models of Transport Activity for the Transporter Bilitranslocase.Molecules (Basel, Switzerland) · 2019Article
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2 authors.
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Abstract
The reliability of a QSAR classification model depends on its capacity to achieve confident predictions of new compounds not considered in the building of the model. The results of this external validation process show the applicability domain (AD) of the QSAR model and, therefore, the robustness of the model to predict the property/activity of new molecules. In this paper we propose the use of the rivality and modelability indexes for the study of the characteristics of the datasets to be correctly modeled by a QSAR algorithm and to predict the reliability of the built model to prognosticate the property/activity of new molecules. The calculation of these indexes has a very low computational cost, not requiring the building of a model, thus being good tools for the analysis of the datasets in the first stages of the building of QSAR classification models. In our study, we have selected two benchmark datasets with similar number of molecules but with very different modelability and we have corroborated the capacity of the predictability of the rivality and modelability indexes regarding the classification models built using Support Vector Machine and Random Forest algorithms with 5-fold cross-validation and leave-one-out techniques. The results have shown the excellent ability of both indexes to predict outliers and the applicability domain of the QSAR classification models. In all cases, these values accurately predicted the statistic parameters of the QSAR models generated by the algorithms.
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