Cited by 3,735Computational Drug Discovery MethodsMachine Learning in Materials ScienceAdvanced Chemical Physics Studies

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Beware of q2!

Golbraikh, Alexander, Tropsha, Alexander·2002·Journal of Molecular Graphics and Modelling

DOI 10.1016/s1093-3263(01)00123-1

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Abstract

Validation is a crucial aspect of any quantitative structure-activity relationship (QSAR) modeling. This paper examines one of the most popular validation criteria, leave-one-out cross-validated R2 (LOO q2). Often, a high value of this statistical characteristic (q2 > 0.5) is considered as a proof of the high predictive ability of the model. In this paper, we show that this assumption is generally incorrect. In the case of 3D QSAR, the lack of the correlation between the high LOO q2 and the high predictive ability of a QSAR model has been established earlier [Pharm. Acta Helv. 70 (1995) 149; J. Chemomet. 10(1996)95; J. Med. Chem. 41 (1998) 2553]. In this paper, we use two-dimensional (2D) molecular descriptors and k nearest neighbors (kNN) QSAR method for the analysis of several datasets. No correlation between the values of q2 for the training set and predictive ability for the test set was found for any of the datasets. Thus, the high value of LOO q2 appears to be the necessary but not the sufficient condition for the model to have a high predictive power. We argue that this is the general property of QSAR models developed using LOO cross-validation. We emphasize that the external validation is the only way to establish a reliable QSAR model. We formulate a set of criteria for evaluation of predictive ability of QSAR models.

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Shahlaei, M. (2013) · Chemical Reviews · doi:10.1021/cr3004339 · Cited by 229
Advances in computational methods to predict the biological activity of compounds
Nantasenamat, C., Isarankura‐Na‐Ayudhya, C., & Prachayasittikul, V. (2010) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2010.492827 · Cited by 219
“Such parameters are essential for the successful application and interpretation of the QSAR models [132].”
Method
Applications of Gene Expression Programming for Estimating Compressive Strength of High‐Strength Concrete
Aslam, F., Farooq, F., Amin, M. N., Khan, K., Waheed, A., Akbar, A., Javed, M. F., Alyousef, R., & Alabduljabbar, H. (2020) · Advances in Civil Engineering · doi:10.1155/2020/8850535 · Cited by 218
“Golbraikh and Tropsha [58] proposed a generalized relationship that the slope of line regression (k′ or k) in the model should approach to 1.”
Background
A new set of amino acid descriptors and its application in peptide QSARs
Hu, M. Y., Liao, Z., Zhou, Y., & Li, S. Z. (2005) · Biopolymers · doi:10.1002/bip.20296 · Cited by 214
Gaussian Processes: A Method for Automatic QSAR Modeling of ADME Properties
Obrezanova, O., Cśanyi, G., Gola, J. M. R., & Segall, M. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci7000633 · Cited by 214
Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future
Zahrt, A. F., Athavale, S. V., & Denmark, S. E. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00425 · Cited by 212
“NanoBRIDGES” software: Open access tools to perform QSAR and nano-QSAR modeling
Ambure, P. S., Aher, R. B., Gajewicz, A., Puzyn, T., & Roy, K. (2015) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2015.07.007 · Cited by 211
Validated QSAR Prediction of OH Tropospheric Degradation of VOCs: Splitting into Training−Test Sets and Consensus Modeling
Gramatica, P., Pilutti, P., & Papa, E. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci049923u · Cited by 208
Formulation of flow number of asphalt mixes using a hybrid computational method
Alavi, A. H., Ameri, M., Gandomi, A. H., & Mirzahosseini, M. R. (2010) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2010.09.010 · Cited by 204
“New criteria recommended by Golbraikh and Tropsha [60] were also checked for the external validation of the GP/SA model on the testing data sets.”
Method
MedusaScore: An Accurate Force Field-Based Scoring Function for Virtual Drug Screening
Yin, S., Biedermannová, L., Vondrášek, J., & Dokholyan, N. V. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci8001167 · Cited by 212
QSAR Modeling of the Blood–Brain Barrier Permeability for Diverse Organic Compounds
Zhang, L., Zhu, H., Oprea, T. I., Golbraikh, A., & Tropsha, A. (2008) · Pharmaceutical Research · doi:10.1007/s11095-008-9609-0 · Cited by 201
Self-citation
ADME Evaluation in Drug Discovery. 5. Correlation of Caco-2 Permeation with Simple Molecular Properties
Hou, T., Zhang, W., Xia, K., Qiao, X. B., & Xu, X. J. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci049884m · Cited by 204
Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming
Salgotra, R., Gandomi, M., & Gandomi, A. H. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109945 · Cited by 198
A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems
Gandomi, A. H., & Alavi, A. H. (2011) · Neural Computing and Applications · doi:10.1007/s00521-011-0735-y · Cited by 194
“Furthermore, new criteria recommended by Golbraikh and Tropsha [43] are checked for external validation of the MGGP prediction models (Problems I, II, III) on the validation data sets.”
Method
Characterization of structure–antioxidant activity relationship of peptides in free radical systems using QSAR models: Key sequence positions and their amino acid properties
Li, Y., & Li, B. (2012) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2012.10.029 · Cited by 193
The History and Development of Quantitative Structure-Activity Relationships (QSARs)
Dearden, J. C. (2016) · International Journal of Quantitative Structure-Property Relationships · doi:10.4018/ijqspr.2016010101 · Cited by 192
Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model
Danial Mohammadzadeh, S., Kazemi, S.-F., Mosavi, A. H., Nasseralshariati, E., & Tah, J. H. M. (2019) · Infrastructures · doi:10.3390/infrastructures4020026 · Cited by 189
“h t h k     Golbraikh and Tropsha [43] 0.”
Structure activity relationships and quantitative structure activity relationships for the flavonoid-mediated inhibition of breast cancer resistance protein
Zhang, S., Yang, X., Coburn, R. A., & Morris, M. E. (2005) · Biochemical Pharmacology · doi:10.1016/j.bcp.2005.05.017 · Cited by 190
Critical review and advices on spectral-based normalization methods for LIBS quantitative analysis
Guezenoc, J., Gallet‐Budynek, A., & Bousquet, B. (2019) · Spectrochimica Acta Part B Atomic Spectroscopy · doi:10.1016/j.sab.2019.105688 · Cited by 195
Toxicity of 58 substituted anilines and phenols to algae Pseudokirchneriella subcapitata and bacteria Vibrio fischeri: Comparison with published data and QSARs
Aruoja, V., Sihtmäe, M., Dubourguier, H., & Kahru, A. (2011) · Chemosphere · doi:10.1016/j.chemosphere.2011.05.023 · Cited by 185
Evaluation guidelines for machine learning tools in the chemical sciences
Bender, A., Schneider, N., Segler, M., Walters, W. P., Engkvist, O., & Rodrigues, T. (2022) · Nature Reviews Chemistry · doi:10.1038/s41570-022-00391-9 · Cited by 192
The Trouble with QSAR (or How I Learned To Stop Worrying and Embrace Fallacy)
Johnson, S. R. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci700332k · Cited by 185

Authors: Golbraikh, A., & Tropsha, A.
Affiliations: University of North Carolina at Chapel Hill (US)
Corresponding author: Tropsha, Alexander
ORCID: Golbraikh, Alexander
Tropsha, Alexander
Venue: Journal of Molecular Graphics and Modelling
Publisher: Elsevier BV
Published: 2002-01
Volume: 20
Issue: 4
Pages: 269-276
ISSN: 1093-3263 (ISSN-L)
DOI: 10.1016/s1093-3263(01)00123-1
OpenAlex: W2087661061
Cited by: 3,735
References: 39
Citations by year: 2026: 133 · 2025: 164 · 2024: 179 · 2023: 174 · 2022: 194 · 2021: 197 · 2020: 215 · 2019: 189 · 2018: 200 · 2017: 174 · 2016: 181 · 2015: 175 · 2014: 197 · 2013: 203 · 2012: 202
FWCI: 52.71
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Journal of Molecular Graphics and Modelling (publishedVersion)
PubMed (publishedVersion)
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: leave-one-out cross-validation, QSAR model validation, predictive power, external validation, 2D molecular descriptors, overfitting, model evaluation metrics
MeSH terms: Data Interpretation, Statistical, Ligands, Mathematical Computing, Models, Molecular, Receptors, Histamine H1, Steroids, Transcortin, Databases, Factual, Quantitative Structure-Activity Relationship, Ecdysteroids

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