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Rational selection of training and test sets for the development of validated QSAR models
Abstract
Quantitative Structure-Activity Relationship (QSAR) models are used increasingly to screen chemical databases and/or virtual chemical libraries for potentially bioactive molecules. These developments emphasize the importance of rigorous model validation to ensure that the models have acceptable predictive power. Using k nearest neighbors (kNN) variable selection QSAR method for the analysis of several datasets, we have demonstrated recently that the widely accepted leave-one-out (LOO) cross-validated R2 (q2) is an inadequate characteristic to assess the predictive ability of the models [Golbraikh, A., Tropsha, A. Beware of q2! J. Mol. Graphics Mod. 20, 269-276, (2002)]. Herein, we provide additional evidence that there exists no correlation between the values of q2 for the training set and accuracy of prediction (R2) for the test set and argue that this observation is a general property of any QSAR model developed with LOO cross-validation. We suggest that external validation using rationally selected training and test sets provides a means to establish a reliable QSAR model. We propose several approaches to the division of experimental datasets into training and test sets and apply them in QSAR studies of 48 functionalized amino acid anticonvulsants and a series of 157 epipodophyllotoxin derivatives with antitumor activity. We formulate a set of general criteria for the evaluation of predictive power of QSAR models.
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Golbraikh et al. proposed a protocol to determine if a QSAR model has predictive power.
“Golbraikh et al. 43 proposed a protocol to determine if a QSAR model has a predictive power.”
From Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks (2018) · cites this paper as [43]
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Metadata
- Authors
- Golbraikh, A., Shen, M., Xiao, Z., Xiao, Y.-D., Lee, K.-H., & Tropsha, A.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Tropsha, Alexander
- Venue
- Journal of Computer-Aided Molecular Design
- Published
- 2003-02
- Volume
- 17
- Issue
- 2-4
- Pages
- 241-253
- ISSN
- 0920-654X (ISSN-L)
- 1573-4951
- OpenAlex
- W1571439140
- Cited by
- 712
- References
- 30
- Citations by year
- 2026: 24 · 2025: 36 · 2024: 26 · 2023: 41 · 2022: 35 · 2021: 44 · 2020: 47 · 2019: 38 · 2018: 36 · 2017: 31 · 2016: 35 · 2015: 36 · 2014: 28 · 2013: 31 · 2012: 30
- FWCI
- 13.60
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Computer-Aided Molecular Design (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r03mh060328-02)
- National Institute of Mental Health (MH60328)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- QSAR model validation, leave-one-out cross-validation, external validation, epipodophyllotoxin derivatives, anticancer activity, virtual screening
- MeSH terms
- Algorithms, Anticonvulsants, Models, Chemical, Software Validation, Molecular Structure, Databases, Factual, Quantitative Structure-Activity Relationship
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