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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Alnuwaiser, M. A., Javed, M. F., Khan, M. I., Ahmed, M. W., & Galal, A. M. (2022) · Journal of the Indian Chemical Society · doi:10.1016/j.jics.2022.100538 · Cited by 36
An ensemble tree-based prediction of Marshall mix design parameters and resilient modulus in stabilized base materials
Khan, A. M., Huyan, J., Zhang, R., Zhu, Y., Zhang, W., Gao, Y. L., Ahmad, K. N., & Shah, S. K. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.132833 · Cited by 35
3D-QSAR – Applications, Recent Advances, and Limitations
Sippl, W. (2009) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-1-4020-9783-6_4 · Cited by 36
QSPR study on melting point of carbocyclic nitroaromatic compounds by multiple linear regression and artificial neural network
Wang, D., Yuan, Y., Duan, S., Liu, R., Gu, S., Zhao, S., Liu, L., & Xu, J. (2015) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2015.02.009 · Cited by 35
An approach to design potent anti-Alzheimer’s agents by 3D-QSAR studies on fused 5,6-bicyclic heterocycles as γ-secretase modulators using kNN–MFA methodology
Bhadoriya, K. S., Sharma, M. C., Sharma, S., Jain, S. V., & Avchar, M. H. (2013) · Arabian Journal of Chemistry · doi:10.1016/j.arabjc.2013.02.002 · Cited by 35
Synthesis, antimicrobial evaluation and QSAR studies of gallic acid derivatives
Khatkar, A., Nanda, A., Kumar, P., & Narasimhan, B. (2013) · Arabian Journal of Chemistry · doi:10.1016/j.arabjc.2013.11.014 · Cited by 35
“The value of q 2 more than 0.5 indicated that the model developed is a valid one (Golbraikh and Tropsha, 2002).”
BackgroundInfluential
QSAR models for removal rates of organic pollutants adsorbed by in situ formed manganese dioxide under acid condition
Su, P., Zhu, H., & Shen, Z. (2015) · Environmental Science and Pollution Research · doi:10.1007/s11356-015-5569-1 · Cited by 35
Correlating the Effects of the N-Substituent Sizes of Chiral 1,2-Amino Phosphinamide Ligands on Enantioselectivities in Catalytic Asymmetric Henry Reaction Using Physical Steric Parameters
Huang, H., Zong, H., Bian, G., Yue, H., & Song, L. (2014) · The Journal of Organic Chemistry · doi:10.1021/jo500982j · Cited by 35
QSAR analysis of the toxicity of phenols and thiophenols using MLR and ANN
Ghamali, M., Chtita, S., Ousaa, A., Elidrissi, B., Bouachrıne, M., & Lakhlifi, T. (2016) · Journal of Taibah University for Science · doi:10.1016/j.jtusci.2016.03.002 · Cited by 35
Predicting chloroform production from organic precursors
Bond, T., & Graham, N. J. D. (2017) · Water Research · doi:10.1016/j.watres.2017.07.063 · Cited by 35
“Mean statistical parameters for the fivefold leave-many-out cross-validation show the five individual equations have high predictive power, since r2 > 0.6; q2 > 0.5; 0.85 < k or k0 < 1.15 and r2-r02/r2 < 0.1 (Table S8) (Golbraikh and Tropsha, 2002).”
BackgroundInfluential
HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors
Qureshi, A., Rajput, A., Kaur, G., & Kumar, M. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0266-y · Cited by 35
“However, we used Tropsha’s validation test to statistically validate the prediction ability of developed model [31, 32].”
Method
Perspectives and recent advances in quantitative structure-retention relationships for high performance liquid chromatography. How far are we?
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QSAR analysis for quinoxaline-2-carboxylate 1,4-di-N-oxides as anti-mycobacterial agents
Vicente, E. F., Duchowicz, P. R., Castro, E. A., & Monge, A. (2009) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2009.03.004 · Cited by 35
Exploring predictive QSAR models for hepatocyte toxicity of phenols using QTMS descriptors
Roy, K., & Popelier, P. L. A. (2008) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2008.03.035 · Cited by 35
Dual inhibitors of Janus kinase 2 and 3 (JAK2/3): designing by pharmacophore- and docking-based virtual screening approach
Jasuja, H., Chadha, N., Kaur, M., & Silakari, O. (2014) · Molecular Diversity · doi:10.1007/s11030-013-9497-z · Cited by 35
Optimization of biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists using QSAR modeling, classification techniques and virtual screening
Melagraki, G., Afantitis, A., Sarimveis, H., Koutentis, P. A., Markopoulos, J., & Igglessi‐Markopoulou, O. (2007) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-007-9112-4 · Cited by 35
The inhibitory activity of aldose reductase of flavonoid compounds: Combining DFT and QSAR calculations
Ghamali, M., Chtita, S., Hmamouchi, R., Adad, A., Bouachrıne, M., & Lakhlifi, T. (2015) · Journal of Taibah University for Science · doi:10.1016/j.jtusci.2015.09.006 · Cited by 35
In silico insights into the design of novel NR2B-selective NMDA receptor antagonists: QSAR modeling, ADME-toxicity predictions, molecular docking, and molecular dynamics investigations
fadili, M. E., Er-rajy, M., Mujwar, S., Ajala, A., Bouzammit, R., Kara, M., Abuelizz, H. A., Er-Rahmani, S., & Elhallaoui, M. (2024) · BMC Chemistry · doi:10.1186/s13065-024-01248-6 · Cited by 34
Predictive QSAR modeling of HIV reverse transcriptase inhibitor TIBO derivatives
Mandal, A. S., & Roy, K. (2008) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2008.07.020 · Cited by 34
QSAR modeling of taxane analogues against colon cancer
Verma, R. P., & Hansch, C. H. (2010) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2009.12.054 · Cited by 34
SMILES‐Based QSAR Models for the Calcium Channel‐Antagonistic Effect of 1,4‐Dihydropyridines
Veselinović, A. M., Milosavljević, J. B., Toropov, A. A., & Nikolić, G. M. (2012) · Archiv der Pharmazie · doi:10.1002/ardp.201200373 · Cited by 34
A data mining approach to compressive strength of CFRP-confined concrete cylinders
Mousavi, S. M., Alavi, A. H., Gandomi, A. H., Esmaeili, M., & Gandomi, M. (2010) · STRUCTURAL ENGINEERING AND MECHANICS · doi:10.12989/sem.2010.36.6.759 · Cited by 34
“Furthermore, new criteria recommended by Golbraikh and Tropsha (2002) were checked for the external validation of the GP/ ff′ fco′ fco′ SA and MEP models on the testing data sets.”
Method
3D-QSAR studies of azaoxoisoaporphine, oxoaporphine, and oxoisoaporphine derivatives as anti-AChE and anti-AD agents by the CoMFA method
Li, Y., Weng, X., Ning, F.-X., Ou, J., Hou, J., Luo, H., Li, D., Huang, Z., Huang, S., & Gu, L. (2013) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2013.02.003 · Cited by 34
QSAR analysis of 2-benzoxazolyl hydrazone derivatives for anticancer activity and its possible target prediction
Moorthy, N. S. H. N., Cerqueira, N. M. F. S. A., Ramos, M. J., & Fernandes, P. A. (2010) · Medicinal Chemistry Research · doi:10.1007/s00044-010-9510-3 · Cited by 34
“…cell growth IC 50 (lM) Butkitt CCRF-CEM HeLa HT-29 13H N N H 3 C CH 3 H 0.15 0.62 0.37 0.29 13I N CH 3 H 5.49 7.04 4.53 7.76 13J N CH 3 H 0.20 0.35 0.34 1.93 13K N CH 3 H 0.03 0.14 0.10 0.04 significant predictive ability (Tropsha et al., 2003; Erikkson et al., 2003; Alexander and Alexander, 2002).”
Influential
Modification of polychlorinated phenols and evaluation of their toxicity, biodegradation and bioconcentration using three-dimensional quantitative structure–activity relationship models
Lidan, T., Guo, L.-X., Lv, X., & Li, Y. (2016) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2016.10.012 · Cited by 34
Binding Space Concept: A New Approach To Enhance the Reliability of Docking Scores and Its Application to Predicting Butyrylcholinesterase Hydrolytic Activity
Vistoli, G., Mazzolari, A., Testa, B., & Pedretti, A. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00121 · Cited by 34
Evaluation of shear strength parameters of granulated waste rubber using artificial neural networks and group method of data handling
Eidgahee, D. R., Haddad, A., & Naderpour, H. (2018) · Scientia Iranica · doi:10.24200/sci.2018.5663.1408 · Cited by 34
In silico identification and screening of CYP24A1 inhibitors: 3D QSAR pharmacophore mapping and molecular dynamics analysis
Jayaraj, J. M., Gopinath, K., Lee, J.-K., & Muthusamy, K. (2018) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2018.1464958 · Cited by 34
“According to the literature, (Golbraikh & Tropsha, 2002; Kirubakaran & Karthikeyan, 2013) 3D-QSAR models are accepted if they satisfy the following conditions: [ 0:5; r2 [ 0:6; ½ðr2 r2oÞ=r2 \0:1; 0:85 k 1:15 and r2m [ 0:5 (1) The r2 value is calculated as R ¼ Pðyi yoÞð~yi…”
Background
QSPR modelling for prediction of glass transition temperature of diverse polymers
Khan, P. M., & Roy, K. (2018) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2018.1536078 · Cited by 34
Ecotoxicological Modeling, Ranking and Prioritization of Pharmaceuticals Using QSTR and i‐QSTTR Approaches: Application of 2D and Fragment Based Descriptors
Khan, K., Kar, S., Sanderson, H., Roy, K., & Leszczyński, J. (2018) · Molecular Informatics · doi:10.1002/minf.201800078 · Cited by 34
A novel evolutionary learning to prepare sustainable concrete mixtures with supplementary cementitious materials
Naseri, H., Hosseini, P., Jahanbakhsh, H., Hosseini, P., & Gandomi, A. H. (2022) · Environment Development and Sustainability · doi:10.1007/s10668-022-02283-w · Cited by 34
Predictive Modeling and Experimental Validation for Assessing the Mechanical Properties of Cementitious Composites Made with Silica Fume and Ground Granulated Blast Furnace Slag
Asif, U., Memon, S. A., Javed, M. F., & Kim, J. R. (2024) · Buildings · doi:10.3390/buildings14041091 · Cited by 34

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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