ArticleMolecular informatics2019
Intercorrelation Limits in Molecular Descriptor Preselection for QSAR/QSPR.
Article in Molecular informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Condition-Specific LC Retention Time Prediction: Feature-Selected QSRR Versus Pretrained Graph Isomorphism Network Transfer Learning.International journal of molecular sciences · 2026Article
- Investigation of Adverse Events Associated with Predicted GnRHR Agonists Using the FAERS Database.International journal of molecular sciences · 2026Article
- Interpretable Quantitative Structure-Activity Relationship (QSAR) for identification of potent antifungal activity agents towards Candida albicans ATCC 2091.Molecular diversity · 2026Article
- 3d electron cloud descriptors for enhanced QSAR modeling of anti-colorectal cancer compounds.Journal of computer-aided molecular design · 2025Article
- Exploring Adverse Event Associations of Predicted PXR Agonists Using the FAERS Database.International journal of molecular sciences · 2025Article
- FlavorMiner: a machine learning platform for extracting molecular flavor profiles from structural data.Journal of cheminformatics · 2024Article
- Alternative weighting schemes for fine-tuned extended similarity indices.Journal of chemometrics · 2024Article
- Unsupervised manifold embedding to encode molecular quantum information for supervised learning of chemical data.Communications chemistry · 2024Article
- Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling.Current drug research reviews · 2024Review
- Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches.Current topics in medicinal chemistry · 2024Review
- Modelling of chromatographic and electrophoretic behaviour of imidazoline and alpha adrenergic receptors ligands under different acid-base conditions.ADMET & DMPK · 2024Article
- Extended continuous similarity indices: theory and application for QSAR descriptor selection.Journal of computer-aided molecular design · 2022Article
- Comparison of Descriptor- and Fingerprint Sets in Machine Learning Models for ADME-Tox Targets.Frontiers in chemistry · 2022Article
- Article
- Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules.Pharmaceutics · 2021Article
- Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification.Molecules (Basel, Switzerland) · 2021Article
- Large-scale evaluation of cytochrome P450 2C9 mediated drug interaction potential with machine learning-based consensus modeling.Journal of computer-aided molecular design · 2020Article
- Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics.Molecules (Basel, Switzerland) · 2019Article
- Intercorrelation Limits in Molecular Descriptor Preselection for QSAR/QSPR.Molecular informatics · 2019Article
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3 authors.
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Abstract
QSAR/QSPR (quantitative structure-activity/property relationship) modeling has been a prevalent approach in various, overlapping sub-fields of computational, medicinal and environmental chemistry for decades. The generation and selection of molecular descriptors is an essential part of this process. In typical QSAR workflows, the starting pool of molecular descriptors is rationalized based on filtering out descriptors which are (i) constant throughout the whole dataset, or (ii) very strongly correlated to another descriptor. While the former is fairly straightforward, the latter involves a level of subjectivity when deciding what exactly is considered to be a strong correlation. Despite that, most QSAR modeling studies do not report on this step. In this study, we examine in detail the effect of various possible descriptor intercorrelation limits on the resulting QSAR models. Statistical comparisons are carried out based on four case studies from contemporary QSAR literature, using a combined methodology based on sum of ranking differences (SRD) and analysis of variance (ANOVA).
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