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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Khan, M. F., Verma, G., Akhtar, W., Shaquiquzzaman, M., Akhter, M., Rizvi, M. M. A., & Alam, M. M. (2016) · Arabian Journal of Chemistry · doi:10.1016/j.arabjc.2016.11.004 · Cited by 68
The Correlation between Chemical Structures and Antioxidant, Prooxidant, and Antitrypanosomatid Properties of Flavonoids
Baldim, J. L., de Alcântara, B. G. V., Domingos, O. D. S., Soares, M. G., Caldas, I. S., Novaes, R. D., Oliveira, T. B., Lago, J. H. G., & Chagas‐Paula, D. A. (2017) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2017/3789856 · Cited by 68
QSAR and Classification Study on Prediction of Acute Oral Toxicity of N-Nitroso Compounds
Fan, T., Sun, G., Zhao, L., Cui, X., & Zhong, R. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19103015 · Cited by 68
Consensus kNN QSAR: A Versatile Method for Predicting the Estrogenic Activity of Organic Compounds In Silico. A Comparative Study with Five Estrogen Receptors and a Large, Diverse Set of Ligands
Asikainen, A. H., Ruuskanen, J., & Tuppurainen, K. A. (2004) · Environmental Science & Technology · doi:10.1021/es049665h · Cited by 68
Novel ZE-Isomerism Descriptors Derived from Molecular Topology and Their Application to QSAR Analysis
Golbraikh, A., Bonchev, D., & Tropsha, A. (2002) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0103469 · Cited by 68
Self-citation
Quantitative Studies on Structure-DPPH• Scavenging Activity Relationships of Food Phenolic Acids
Jing, P., Zhao, S.-J., Jian, W., Qian, B., Dong, Y., & Pang, J. (2012) · Molecules · doi:10.3390/molecules171112910 · Cited by 67
“Tropsha applied to the regression analysis must satisfy the following conditions [12,13]: (1) q(2) > 0.”
Method
Validation tools for variable subset regression
Baumann, K., & Stiefl, N. (2004) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-004-4071-5 · Cited by 67
Tomocomd-Cardd, a novel approach for computer-aided ? rational? drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds
Marrero‐Ponce, Y., Castillo‐Garit, J. A., Olazábal, E., Serrano, H. S., Morales, A., Casta�edo, N., Ibarra-Velarde, F., Huesca‐Guillén, A., Jorge, E., del Valle, A., et al. (2004) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-004-5171-y · Cited by 67
Subtype Selectivity of Dopamine Receptor Ligands: Insights from Structure and Ligand-Based Methods
Wang, Q., Mach, R. H., Luedtke, R. R., & Reichert, D. E. C. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci1002747 · Cited by 67
Exhaled Breath Metabolomics for the Diagnosis of Pneumonia in Intubated and Mechanically-Ventilated Intensive Care Unit (ICU)-Patients
van Oort, P. M. P., de Bruin, S., Weda, H., Knobel, H., Schultz, M. J., & Bos, L. D. J. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18020449 · Cited by 67
“Another strength is that the accuracy of the model was assessed with the AUROC as a measure of accuracy, which is proven suitable in classifying patients [38,39].”
Method
Evaluation of the antioxidant properties of curcumin derivatives by genetic function algorithm
Alisi, I. O., Uzairu, A., Abechi, S. E., & Idris, S. O. (2018) · Journal of Advanced Research · doi:10.1016/j.jare.2018.03.003 · Cited by 67
“The acceptable threshold values for the given parameters are as follows: R(2)pred > 0:5; r 2 > 0:6; r(2)m P 0:5, Delta r 2 m < 0:2; jr(2)0 r02 0 j < 0:3; ðr2 r(2)0Þ=r(2) < 0:1 and 0:85 6 k 6 1:15; or ðr2 r02 0 Þ=r2 < 0:1 and 0:85 6 k0 6 1:15 [29].”
MethodInfluential
Molecular modeling and prediction accuracy in Quantitative Structure-Retention Relationship calculations for chromatography
Amos, R. I. J., Haddad, P. R., Szücs, R., Dolan, J. W., & Pohl, C. A. (2018) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2018.05.019 · Cited by 67
Protein linear indices of the ‘macromolecular pseudograph α-carbon atom adjacency matrix’ in bioinformatics. Part 1: Prediction of protein stability effects of a complete set of alanine substitutions in Arc repressor
Marrero‐Ponce, Y., Medina, R., Castillo‐Garit, J. A., Zaldívar, V. R., Torrens, F., & Castro, E. A. (2005) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2005.01.062 · Cited by 67
QSAR models for HEPT derivates as NNRTI inhibitors based on Monte Carlo method
Toropova, A. P., Toropov, A. A., Veselinović, J. B., Miljković, F. N., & Veselinović, A. M. (2014) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2014.03.013 · Cited by 66
“Cross-validated Q2 determinates the predictive ability of the model [44].”
Background
Classification-Based Regression Models for Prediction of the Mechanical Properties of Roller-Compacted Concrete Pavement
Ashrafian, A., Amiri, M. J. T., Masoumi, P., Asadi-shiadeh, M., Yaghoubi-chenari, M., Mosavi, A. H., & Nabipour, N. (2020) · Applied Sciences · doi:10.3390/app10113707 · Cited by 67
Chemometric QSAR modeling of acute oral toxicity of Polycyclic Aromatic Hydrocarbons (PAHs) to rat using simple 2D descriptors and interspecies toxicity modeling with mouse
Sun, G., Zhang, Y., Pei, L., Lou, Y., Mu, Y., Yun, J., Li, F., Wang, Y., Hao, Z., Xi, S., et al. (2021) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2021.112525 · Cited by 66
Computational identification of 2,4-disubstituted amino-pyrimidines as L858R/T790M-EGFR double mutant inhibitors using pharmacophore mapping, molecular docking, binding free energy calculation, DFT study and molecular dynamic simulation
Pawara, R. H., Ahmad, I., Surana, S. J., & Patel, H. (2021) · In Silico Pharmacology · doi:10.1007/s40203-021-00113-x · Cited by 69
“The preparation of the protein was carried out with the Protein Preparation Wizard in Maestro (Bhadoriya et al.”
MethodInfluential
Towards sustainable construction: Machine learning based predictive models for strength and durability characteristics of blended cement concrete
Khan, M., & Javed, M. F. (2023) · Materials Today Communications · doi:10.1016/j.mtcomm.2023.107428 · Cited by 68
QSAR study of flavonoids and biflavonoids as influenza H1N1 virus neuraminidase inhibitors
Mercader, A. G., & Pomilio, A. B. (2010) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2010.01.005 · Cited by 66
Leak location of pipelines based on transient model and PSO-SVM
Ni, L., Jiang, J., & Pan, Y. L. (2013) · Journal of Loss Prevention in the Process Industries · doi:10.1016/j.jlp.2013.04.004 · Cited by 66
Synthesis, antimicrobial evaluation and QSAR studies of p-coumaric acid derivatives
Khatkar, A., Nanda, A., Kumar, P., & Narasimhan, B. (2014) · Arabian Journal of Chemistry · doi:10.1016/j.arabjc.2014.05.018 · Cited by 66
“5 is the basic requirement for qualifying a QSAR model to be valid one (Golbraikh and Tropsha, 2002).”
Background
New Formulation of Compressive Strength of Preformed-Foam Cellular Concrete: An Evolutionary Approach
Kiani, B., Gandomi, A. H., Sajedi, S., & Liang, R. Y. K. (2016) · Journal of Materials in Civil Engineering · doi:10.1061/(asce)mt.1943-5533.0001602 · Cited by 66
Prediction of Catenary Action Capacity of RC Beam-Column Substructures under a Missing Column Scenario Using Evolutionary Algorithm
Azim, I., Yang, J., Iqbal, M. F., Mahmood, Z., Javed, M. F., Wang, F., & Liu, Q. (2021) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-021-0431-0 · Cited by 66
“In order to evaluate the true predictive ability of the proposed GEP model, Golbraikh and Tropsha (2002) and Tropsha et al. (2003) suggested external validation criteria on the testing set.”
Method
Genetic programming based symbolic regression for shear capacity prediction of SFRC beams
Chaabene, W. B., & Nehdi, M. L. (2021) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2021.122523 · Cited by 67
CORAL: Building up the model for bioconcentration factor and defining it’s applicability domain
Toropov, A. A., Toropova, A. P., Lombardo, A., Roncaglioni, A., Benfenati, E., & Gini, G. C. (2011) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2011.01.018 · Cited by 65
QSPR Studies on Aqueous Solubilities of Drug-Like Compounds
Duchowicz, P. R., & Castro, E. A. (2009) · International Journal of Molecular Sciences · doi:10.3390/ijms10062558 · Cited by 65
“50, according to the specialized literature [86].”
QSPR studies on refractive indices of structurally heterogeneous polymers
Duchowicz, P. R., Fioressi, S. E., Bacelo, D. E., Saavedra, L. M., Toropova, A. P., & Toropov, A. A. (2014) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2014.11.008 · Cited by 65
Prediction of Minimum Ignition Energy from Molecular Structure Using Quantitative Structure–Property Relationship (QSPR) Models
Wang, B., Zhou, L., Xu, K., & Wang, Q. (2016) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.6b04347 · Cited by 65
Parameterization and Analysis of Peptide-Based Catalysts for the Atroposelective Bromination of 3-Arylquinazolin-4(3H)-ones
Crawford, J. M., Stone, E. A., Metrano, A. J., Miller, S. J., & Sigman, M. S. (2018) · Journal of the American Chemical Society · doi:10.1021/jacs.7b11303 · Cited by 65
Nano-QSAR modeling for ecosafe design of heterogeneous TiO 2 -based nano-photocatalysts
Mikołajczyk, A., Gajewicz, A., Mulkiewicz, E., Rasulev, B., Marchelek, M., Diak, M., Hirano, S., Zaleska‐Medynska, A., & Puzyn, T. (2018) · Environmental Science Nano · doi:10.1039/c8en00085a · Cited by 65
Developing Quantitative Structure–Property Relationship Models To Predict the Upper Flammability Limit Using Machine Learning
Yuan, S., Jiao, Z., Quddus, N., Kwon, J. S., & Mashuga, C. V. (2019) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.8b05938 · Cited by 65
Per Aspera Ad Astra : Application of Simplex Qsar Approach in Antiviral Research
Muratov, E. N., Artemenko, A. G., Varlamova, E. V., Polischuk, P. G., Lozitsky, V. P., Fedchuk, A. S., Lozitska, R. L., Gridina, T. L., Koroleva, L. S., Sil’nikov, V. N., et al. (2010) · Future Medicinal Chemistry · doi:10.4155/fmc.10.194 · Cited by 64

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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