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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QSAR Modeling of α-Campholenic Derivatives with Sandalwood Odor
Kovatcheva, A., Buchbauer, G., Golbraikh, A., & Wolschann, P. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci020296n · Cited by 40
Self-citation
Fast and reliable prediction of domain–peptide binding affinity using coarse-grained structure models
Tian, F., Tan, R., Guo, T., Zhou, P., & Li, Y. (2013) · Biosystems · doi:10.1016/j.biosystems.2013.04.004 · Cited by 39
“For example, in a highly ited work Golbraikh and Tropsha (2002) argued that the high prole of internal cross-validation appears to be the necessary but not he sufficient condition for the model to have a high predictive ower, and they further emphasized that the external validation s the only way to establish a reliab…”
Background
Combined 3D-QSAR, Molecular Docking, and Molecular Dynamics Study on Piperazinyl-Glutamate-Pyridines/Pyrimidines as Potent P2Y12 Antagonists for Inhibition of Platelet Aggregation
Hao, M., Li, Y., Wang, Y., Yan, Y., & Zhang, S. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci2002878 · Cited by 40
Novel anti-HIV cyclotriazadisulfonamide derivatives as modeled by ligand- and receptor-based approaches
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In silico approaches to predicting cancer potency for risk assessment of genotoxic impurities in drug substances
Bercu, J. P., Morton, S., Deahl, J. T., Gombar, V. K., Callis, C. M., & LIER, R. B. L. V. (2010) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2010.03.010 · Cited by 39
Quantitative structure–activity relationships (QSARs) using the novel marine algal toxicity data of phenols
Ertürk, M. D., Saçan, M. T., Novič, M., & Minovski, N. (2012) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2012.06.002 · Cited by 39
Quantitative structure–property relationship analysis for the retention index of fragrance-like compounds on a polar stationary phase
Rojas, C., Duchowicz, P. R., Tripaldi, P., & Diez, R. P. (2015) · Journal of Chromatography A · doi:10.1016/j.chroma.2015.10.028 · Cited by 39
Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors
Kumar, A., & Chauhan, S. (2017) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2017.1293729 · Cited by 39
“15 [52]; r(2) m(test) and r m(test) should be >0.”
BackgroundMethodInfluential
In Silico Prediction of O6-Methylguanine-DNA Methyltransferase Inhibitory Potency of Base Analogs with QSAR and Machine Learning Methods
Sun, G., Fan, T., Sun, X., Hao, Y., Cui, X., Zhao, L., Ren, T., Zhou, Y., Zhong, R., & Peng, Y. (2018) · Molecules · doi:10.3390/molecules23112892 · Cited by 39
“For acceptable QSAR predictive models, the following conditions are satisfied [20,21]: (i) Q2Loo > 0.”
BackgroundMethod
The Application of the Combination of Monte Carlo Optimization Method based QSAR Modeling and Molecular Docking in Drug Design and Development
Živković, M., Zlatanović, M., Zlatanović, N., Golubović, M., & Veselinović, A. M. (2020) · Mini-Reviews in Medicinal Chemistry · doi:10.2174/1389557520666200212111428 · Cited by 39
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Yan, W., Lin, G., Zhang, R., Liang, Z. Y., Wu, L., & Wu, W. (2020) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2020.112702 · Cited by 39
New Approach Combining Molecular Fingerprints and Machine Learning to Estimate Relative Ionization Efficiency in Electrospray Ionization
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An evolutionary-based predictive soft computing model for the prediction of electricity consumption using multi expression programming
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Combined 3D-QSAR and Molecular Docking Analysis of Thienopyrimidine Derivatives as Staphylococcus aureus Inhibitors
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3D-QSAR, molecular docking, DFT and ADMET studies on quinazoline derivatives to explore novel DHFR inhibitors
Hadni, H., Bakhouch, M., & Elhallaoui, M. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.2004233 · Cited by 37
“According to Tropsha and Golbraikh (Golbraikh & Tropsha, 2002), internal validation is necessary but not sufficient.”
Synthesis, antimicrobial evaluation, ot-QSAR and mt-QSAR studies of 2-amino benzoic acid derivatives
Mahiwal, K., Kumar, P., & Narasimhan, B. (2010) · Medicinal Chemistry Research · doi:10.1007/s00044-010-9537-5 · Cited by 39
“4) is a valid one (Golbraikh and Tropsha, 2002).”
Quantitative structure–activity relationships for organophosphates binding to acetylcholinesterase
Ruark, C. D., Hack, C. E., Robinson, P. J., Anderson, P. E., & Gearhart, J. M. (2012) · Archives of Toxicology · doi:10.1007/s00204-012-0934-z · Cited by 38
“They typically take the form Yi = Fi(X1, X2,…,Xn) where Yi are biological activities of molecules represented by some function Fi of experimental or calculated structural properties or descriptors (X1, X2,…,Xn) (Golbraikh and Tropsha 2002).”
The cytotoxicity of ortho alkyl substituted 4-X-phenols: A QSAR based on theoretical bond lengths and electron densities
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A DFT-based QSARs study of protoporphyrinogen oxidase inhibitors: phenyl triazolinones
Zhang, L., Wan, J., & Yang, G. (2004) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2004.08.046 · Cited by 39
CADASTER QSPR Models for Predictions of Melting and Boiling Points of Perfluorinated Chemicals
Bhhatarai, B., Teetz, W., Liu, T., Öberg, T., Jeliazkova, N., Kochev, N. T., Pukalov, O., Tetko, I. V., Kovarich, S., Papa, E., et al. (2011) · Molecular Informatics · doi:10.1002/minf.201000133 · Cited by 39
QSAR Analysis of 2-Amino or 2-Methyl-1-Substituted Benzimidazoles Against Pseudomonas aeruginosa
Podunavac-Kuzmanović, S. O., Cvetković, D., & Barna, D. J. (2009) · International Journal of Molecular Sciences · doi:10.3390/ijms10041670 · Cited by 40
“According to the reference [40], without the validation of the”
MethodResult
Synthesis, molecular docking and QSAR studies of 2, 4-disubstituted thiazoles as antimicrobial agents
Arora, P., Narang, R., Bhatia, S., Nayak, S. K., Singh, S. K., & Narasimhan, B. (2015) · Journal of Applied Pharmaceutical Science · doi:10.7324/japs.2015.50206 · Cited by 39
Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules
Paricharak, S., Cortés-Ciriano, I., IJzerman, A. P., Malliavin, T. E., & Bender, A. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0063-9 · Cited by 38
“[39,40] and calculated using the Validation function of the R package camb [34].”
Method
QSPR for predicting the hydrophile-lipophile balance (HLB) of non-ionic surfactants
Wu, J., Yan, F., Jia, Q., & Wang, Q. (2020) · Colloids and Surfaces A Physicochemical and Engineering Aspects · doi:10.1016/j.colsurfa.2020.125812 · Cited by 38
Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs
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“The q2 value is obtained by calculating R2 using leave-one-out cross validation [27].”
BackgroundMethodResultInfluential
Soft computing-based prediction models for compressive strength of concrete
Kumar, M., Biswas, R., Kumar, D. R., Samui, P., Kaloop, M. R., & Eldessouki, M. (2023) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2023.e02321 · Cited by 38
Machine learning-driven QSAR models for predicting the cytotoxicity of five common microplastics
Liu, C., Zong, C., Chen, S., Chu, J., Yang, Y., Pan, Y. L., Yuan, B., & Zhang, H. (2024) · Toxicology · doi:10.1016/j.tox.2024.153918 · Cited by 41
Using measured daily meteorological parameters to predict daily solar radiation
Mousavi, S. M., Mostafavi, E. S., Jaafari, A., Jaafari, A., & Hosseinpour, F. (2015) · Measurement · doi:10.1016/j.measurement.2015.08.004 · Cited by 38
Predictivity of Simulated ADME AutoQSAR Models over Time
Rodgers, S. L., Davis, A. M., Tomkinson, N. P., & Waterbeemd, H. V. D. (2011) · Molecular Informatics · doi:10.1002/minf.201000160 · Cited by 39
QSAR studies in the discovery of novel type-II diabetic therapies
Abuhammad, A., & Taha, M. O. (2015) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2016.1118046 · Cited by 38
Investigation of Antileishmanial Activities of Acridines Derivatives against Promastigotes and Amastigotes Form of Parasites Using Quantitative Structure Activity Relationship Analysis
Chtita, S., Ghamali, M., Hmamouchi, R., Elidrissi, B., Bourass, M., Larif, M., Bouachrıne, M., & Lakhlifi, T. (2016) · Advances in Physical Chemistry · doi:10.1155/2016/5137289 · Cited by 38
“00; if ρ < 1, the network simply memorizes the data, whereas if ρ > 3, the network is not able to generalize [41].”
Background
Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology
Puzyn, T., Jeliazkova, N., Sarimveis, H., Robinson, R. M., Lobaskin, V., Ralló, R., Richarz, A.-N., Gajewicz, A., Papadopulos, M. G., Hastings, J., et al. (2017) · Food and Chemical Toxicology · doi:10.1016/j.fct.2017.09.037 · Cited by 41
“The resampling techniques discussed in section 2.4.1 are commonly described as internal validation techniques that cannot directly estimate the true predictive ability of a model on unseen data (Chirico and Gramatica, 2011, 2012; Golbraikh and Tropsha, 2002; Gramatica, 2007, 2013).”
BackgroundMethodInfluential

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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