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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Graph theory
Harary, F. (1969) · Cited by 10,739
Structural Determination of Paraffin Boiling Points
Wiener, H. (1947) · Journal of the American Chemical Society · doi:10.1021/ja01193a005 · Cited by 4,184
Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins
Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988) · Journal of the American Chemical Society · doi:10.1021/ja00226a005 · Cited by 3,931
Characterization of molecular branching
Randić, M. (1975) · Journal of the American Chemical Society · doi:10.1021/ja00856a001 · Cited by 3,664
Substituent constants for correlation analysis in chemistry and biology
Hansch, C. H., & Leo, A. J. (1979) · Medical Entomology and Zoology · Cited by 2,910
Molecular Connectivity in Chemistry and Drug Research
(1976) · Medicinal chemistry · doi:10.1016/b978-0-12-406560-4.x5001-6 · Cited by 1,628
Applied Statistics
Sachs, L. (1984) · Springer series in statistics · doi:10.1007/978-1-4612-5246-7 · Cited by 1,016
Graph theory and molecular orbitals. XII. Acyclic polyenes
Gutman, İ., ić, B. R., Trinajstić, N., & WILCOX, C. F. J. (1975) · The Journal of Chemical Physics · doi:10.1063/1.430994 · Cited by 829
Molecular Structure Description: The Electrotopological State
Kier, L. B., & Hall, L. H. (1999) · Medical Entomology and Zoology · Cited by 467
Novel Variable Selection Quantitative Structure−Property Relationship Approach Based on the k -Nearest-Neighbor Principle
Zheng, W., & Tropsha, A. (1999) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci980033m · Cited by 450
Self-citation

Show all 39 references

Research works printed but not linked: 6

S. Clementi, S. Wold, How to choose the proper statistical method, in: H. van de Waterbeemd (Ed.), Chemometrics Methods in Molecular Design, VCH, Weinheim, 1995, pp. 319–338.
S. Wold, PLS for multivariate linear modeling, in: H. van de Waterbeemd (Ed.), Chemometrics Methods in Molecular Design, VCH, Weinheim, 1995, pp. 195–218.
L.B. Kier, L.H. Hall, Molecular Connectivity in Structure–Activity Analysis, Wiley, New York, 1986.
D. Bonchev, Overall connectivity and molecular complexity, in: J. Devillers, A.T. Balaban (Eds.), Topological Indices and Related Descriptors, Gordon and Breach, Reading, UK, 1999, pp. 361–401.
R.D. Clark, D.G. Sprous, J.M. Leonard, Validating models based on large dataset, in: H.-D. Höltje, W. Sippl (Eds.), Rational Approaches to Drug Design. Proceedings of the 13th European Symposium on Quantitative Structure–Activity Relationships, Prous Sci., 2001, 475–485.
A. Golbraikh, D. Bonchev, Y.-D. Xiao, A. Tropsha, Novel chiral topological descriptors and their applications to QSAR, in: H.-D. Höltje, W. Sippl (Eds.), Rational Approaches to Drug Design. Proceedings of the 13th European Symposium on Quantitative Structure–Activity relationships, Aug. 27–Sept. 1, 2000, Duesseldorf, Germany, Prous Science, 2001, 219–223.

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Khamoulı, S., Belaıdı, S., Ouassaf, M., Lanez, T., Belaaouad, S., & Chtita, S. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1824815 · Cited by 32
“To establish the robustness and validate the predictive ability of QSAR models, external validation tests, such as Golbraikh and Tropsha (2002) statistical characteristics following criteria: 1) Q2cv> 0.5 2) R2pred > 0:6 3) R2 R20ð Þ R2 0:1 or R2 R020ð Þ R2 0:1 4) 0.85 K 1.15 or 0.85 K’ 1.15 5Þ…”
MethodInfluential
Prediction of potential inhibitors of SARS-CoV-2 using 3D-QSAR, molecular docking modeling and ADMET properties
Khaldan, A., Bouamrane, S., En-Nahli, F., El-Mernissi, R., Khatabi, K. E., Hmamouchi, R., Maghat, H., Ajana, M. A., Sbai, A., Bouachrıne, M., et al. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e06603 · Cited by 36
“In this paper, the biological activities of four carboxamides sulfonamide scaffolds were predicted using the suggested 3D-QSAR model in order to evaluate the external predictive capacity of the molded model [33].”
A thorough study on the effect of red mud, granite, limestone and marble slurry powder on the strengths of steel fibres-reinforced self-consolidation concrete: Experimental and numerical prediction
Karimipour, A., Jahangir, H., & Eidgahee, D. R. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.103398 · Cited by 32
Ground motion model for Peninsular India using an artificial neural network
Meenakshi, Y., Vemula, S., Alne, A., & Raghukanth, S. T. G. (2023) · Earthquake Spectra · doi:10.1177/87552930221144330 · Cited by 32
“The Rrup or Rjb are unavailable for the PI region and Gupta et al. (2017) database records.”
BackgroundMethodInfluential
In-silico screening based on molecular simulations of 3,4-disubstituted pyrrolidine sulfonamides as selective and competitive GlyT1 inhibitors
fadili, M. E., Er-rajy, M., Eltayb, W. A., Kara, M., Assouguem, A., Saleh, A., Kamaly, O. M. A., Zarougui, S., & Elhallaoui, M. (2023) · Arabian Journal of Chemistry · doi:10.1016/j.arabjc.2023.105105 · Cited by 32
Prediction of Placental Barrier Permeability: A Model Based on Partial Least Squares Variable Selection Procedure
Zhang, Y., Xia, Z., Li, Y., & Liu, S. (2015) · Molecules · doi:10.3390/molecules20058270 · Cited by 32
“It is based on the following four criteria [43,45]: (i) q2Yrand < 0.”
BackgroundMethodInfluential
QSPR analysis for melting point of fatty acids using genetic algorithm based multiple linear regression (GA-MLR)
Liang, G., Xu, J., & Liu, L. (2013) · Fluid Phase Equilibria · doi:10.1016/j.fluid.2013.06.008 · Cited by 32
PK/PD assessment in CNS drug discovery: Prediction of CSF concentration in rodents for P-glycoprotein substrates and application to in vivo potency estimation
Caruso, A., Alvarez‐Sánchez, R., Hillebrecht, A., Poirier, A., Schuler, F., Lavé, T., Funk, C., & Belli, S. (2013) · Biochemical Pharmacology · doi:10.1016/j.bcp.2013.02.021 · Cited by 33
“It has to be noted that LOOCV might overestimate model predictive performances when applied to redundant datasets [35].”
Background
Bond-based linear indices in QSAR: computational discovery of novel anti-trichomonal compounds
Marrero‐Ponce, Y., Meneses‐Marcel, A., Rivera-Borroto, O. M., García‐Domenech, R., de Julián‐Ortiz, J. V., Montero, A., Escario, J. A., Gómez‐Barrio, A., Pereira, D. M., Nogal‐Ruiz, J. J., et al. (2008) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-008-9171-1 · Cited by 32
Docking and CoMSIA studies on steroids and non-steroidal chemicals as androgen receptor ligands
Wang, X., Li, X., Shi, W. F., Wei, S., Giesy, J. P., Yu, H., & Wang, Y. (2012) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2012.11.020 · Cited by 32
“4) (Wang et al., 2005; Golbraikh and Tropsha, 2002), thus further analysis based on these models would be appropriate.”
Background
A new quantitative structure–property relationship model to predict bioconcentration factors of polychlorinated biphenyls (PCBs) in fishes using E-state index and topological descriptors
de Melo, E. B. (2011) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2011.08.026 · Cited by 32
On the Use of 1H and 13C 1D NMR Spectra as QSPR Descriptors
Willighagen, E., Denissen, H. M. G. W., Wehrens, R., & Buydens, L. M. C. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci050282s · Cited by 32
2D Quantitative structure–activity relationship studies on a series of cholesteryl ester transfer protein inhibitors
Castilho, M. S., Guido, R. V. C., & Andricopulo, A. D. (2007) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2007.06.021 · Cited by 32
Predicting Boiling Points of Aliphatic Alcohols through Multivariate Image Analysis Applied to Quantitative Structure−Property Relationships
Goodarzi, M., & Freitas, M. P. (2008) · The Journal of Physical Chemistry A · doi:10.1021/jp8059085 · Cited by 32
Discovery of nanomolar phosphoinositide 3-kinase gamma (PI3Kγ) inhibitors using ligand-based modeling and virtual screening followed by in vitro analysis
Taha, M. O., Al-Sha’er, M. A., Khanfar, M. A., & Al-Nadaf, A. H. (2014) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2014.07.056 · Cited by 32
Quantitative structure–property relationships for the reactivity parameters of acrylate monomers
Yu, X., Yi, B., & Wang, X. (2008) · European Polymer Journal · doi:10.1016/j.eurpolymj.2008.09.028 · Cited by 32
Application of QSAR and Shape Pharmacophore Modeling Approaches for Targeted Chemical Library Design
Ebalunode, J. O., Zheng, W., & Tropsha, A. (2010) · Methods in molecular biology · doi:10.1007/978-1-60761-931-4_6 · Cited by 33
Self-citation
Developmental Toxicity Prediction
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Identification of a series of novel derivatives as potent HCV inhibitors by a ligand-based virtual screening optimized procedure
Melagraki, G., Afantitis, A., Sarimveis, H., Koutentis, P. A., Markopoulos, J., & Igglessi‐Markopoulou, O. (2007) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2007.08.036 · Cited by 32
Spectroscopic QSAR Methods and Self-Organizing Molecular Field Analysis for Relating Molecular Structure and Estrogenic Activity
Asikainen, A. H., Ruuskanen, J., & Tuppurainen, K. A. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034110b · Cited by 32
Three-dimensional quantitative structure–activity relationship (3 D-QSAR) and docking studies on (benzothiazole-2-yl) acetonitrile derivatives as c-Jun N-terminal kinase-3 (JNK3) inhibitors
Shaikh, A. R., Ismael, M., Carpio, C. A. D., Tsuboi, H., Koyama, M., Endou, A., Kubo, M., Brocławik, E., & Miyamoto, A. (2006) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2006.06.039 · Cited by 32
Modeling of structure–mutagenicity relationships: counter propagation neural network approach using calculated structural descriptors
Valkova, I., Vračko, M., & Basak, S. C. (2004) · Analytica Chimica Acta · doi:10.1016/j.aca.2003.12.035 · Cited by 32
Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data
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“Thus, the CVR value should always be accompanied by descriptive statistics of the training data set used to compute it, such as CVE (also denoted RMSE) [30,32].”
MethodInfluential
A Review of Machine Learning and QSAR/QSPR Predictions for Complexes of Organic Molecules with Cyclodextrins
Boczar, D., & Michalska, K. (2024) · Molecules · doi:10.3390/molecules29133159 · Cited by 39
Stacked-based hybrid gradient boosting models for estimating seepage from lined canals
Elshaarawy, M. K. (2025) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2024.106913 · Cited by 31
In silico exploration of c-KIT inhibitors by pharmaco-informatics methodology: pharmacophore modeling, 3D QSAR, docking studies, and virtual screening
Chaudhari, P. J., & Bari, S. B. (2015) · Molecular Diversity · doi:10.1007/s11030-015-9635-x · Cited by 31
Structural analysis of α-glucosidase inhibitors by validated QSAR models using topological and hydrophobicity based descriptors
Moorthy, N. S. H. N., Ramos, M. J., & Fernandes, P. A. (2011) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2011.02.010 · Cited by 32
Global Performance and Trend of QSAR/QSPR Research: A Bibliometric Analysis
Li, L., Hu, J., & Ho, Y. (2014) · Molecular Informatics · doi:10.1002/minf.201300180 · Cited by 32
“Golbraikh and Tropsha (2002)[33] 1088 QSAR modeling; LOO cross-validation; training and test sets; kNN QSAR USA/USA Molecular similarity indexes in a comparativeanalysis (CoMSIA) of drug molecules to correlate and predict their biological-activity Klebe et al. (1994)[85] 954 N/A Germany/Germany…”
Method
Exploring 2D and 3D QSARs of benzimidazole derivatives as transient receptor potential melastatin 8 (TRPM8) antagonists using MLR and kNN-MFA methodology
Bhadoriya, K. S., Kumavat, N. K., Bhavthankar, S. V., Avchar, M. H., Dhumal, D. M., Patil, S. D., & Jain, S. V. (2012) · Journal of Saudi Chemical Society · doi:10.1016/j.jscs.2012.11.001 · Cited by 31
“However, a QSAR model is considered to be predictive, if the following conditions are satisﬁed: q 2 > 0.6 and pred_ r 2 > 0.5 (Golbraikh and Tropsha, 2002).”
Influential
The use of 2D fingerprint methods to support the assessment of structural similarity in orphan drug legislation
Franco, P., Porta, N., Holliday, J. D., & Willett, P. (2014) · Journal of Cheminformatics · doi:10.1186/1758-2946-6-5 · Cited by 31
“It is generally considered better, however, to use a distinct test-set that has not been involved in the training [30,31], and this was accomplished here using data kindly provided by the CHMP that typifies their regular work-load.”
Method
Quantitative structure–activity relationship study on potent anticancer compounds against MOLT-4 and P388 leukemia cell lines
Arthur, D. E., Uzairu, A., Mamza, P. A., & Abechi, S. E. (2016) · Journal of Advanced Research · doi:10.1016/j.jare.2016.03.010 · Cited by 31
Quantitative structure-property relationship model leading to virtual screening of fullerene derivatives: Exploring structural attributes critical for photoconversion efficiency of polymer solar cell acceptors
Kar, S., Sizochenko, N., Ahmed, L., Batista, V. S., & Leszczyński, J. (2016) · Nano Energy · doi:10.1016/j.nanoen.2016.06.011 · Cited by 31
Oxindole-Based Syk and Jak3 Dual Inhibitors for Rheumatoid Arthritis: Designing, Synthesis and Biological Evaluation
Kaur, M., Singh, M., & Silakari, O. (2017) · Future Medicinal Chemistry · doi:10.4155/fmc-2017-0037 · Cited by 31
QSAR modelling: a therapeutic patent review 2010-present
Halder, A. K., Moura, A. S., & Cordeiro, M. N. D. S. (2018) · Expert Opinion on Therapeutic Patents · doi:10.1080/13543776.2018.1475560 · Cited by 31
“available to general public (vide patent’s page 28, Table 14) and the formula of r2 Pred was applied [45,90].”
Method

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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