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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Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches
Khan, M. A., Farooq, F., Javed, M. F., Zafar, A., Ostrowski, K. A., Aslam, F., Malazdrewicz, S., & Maślak, M. (2021) · Materials · doi:10.3390/ma15010058 · Cited by 85
“[86] recommended that for a good model the ρ can be less than 0.”
Background
Classifiers and their Metrics Quantified
Brown, J. B. (2018) · Molecular Informatics · doi:10.1002/minf.201700127 · Cited by 82
QSAR Studies of CYP2D6 Inhibitor Aryloxypropanolamines Using 2D and 3D Descriptors
Roy, P. P., & Roy, K. (2009) · Chemical Biology & Drug Design · doi:10.1111/j.1747-0285.2009.00791.x · Cited by 84
“A list of values of different parameters for different models as recommended by Golbraikh and Tropsha (59) is given in Table 4.”
BackgroundMethodInfluential
The “double cross-validation” software tool for MLR QSAR model development
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Synthesis, docking, QSAR, ADMET and antimicrobial evaluation of new quinoline-3-carbonitrile derivatives as potential DNA-gyrase inhibitors
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Accurate Stabilities of Laccase Mutants Predicted with a Modified FoldX Protocol
Christensen, N. J., & Kepp, K. P. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300398z · Cited by 81
“A LOOCV q 2 value higher than 0.5 is often regarded as a necessary (but insu ﬃ cient) criterion for predictive power, 58,59 but robustness can only be ascertained by external validation.”
BackgroundInfluential
Estimation of ADME Properties in Drug Discovery: Predicting Caco-2 Cell Permeability Using Atom-Based Stochastic and Non-stochastic Linear Indices
Castillo‐Garit, J. A., Marrero‐Ponce, Y., Torrens, F., & García‐Domenech, R. (2007) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.21122 · Cited by 84
Isonicotinic acid hydrazide derivatives: synthesis, antimicrobial activity, and QSAR studies
Judge, V., Narasimhan, B., Ahuja, M., Sriram, D., Yogeeswari, P., Clercq, E. D., Pannecouque, C. C., & Balzarini, J. M. (2011) · Medicinal Chemistry Research · doi:10.1007/s00044-011-9662-9 · Cited by 84
“5 is the basic requirement for qualifying a QSAR model to be valid one (Golbraikh and Tropsha, 2002).”
Influential
3D-Chiral quadratic indices of the ‘molecular pseudograph’s atom adjacency matrix’ and their application to central chirality codification: classification of ACE inhibitors and prediction of σ-receptor antagonist activities
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Machine learning modeling integrating experimental analysis for predicting the properties of sugarcane bagasse ash concrete
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Guha, R., & Drie, J. H. V. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci8001414 · Cited by 85
A comparison of different electrostatic potentials on prediction accuracy in CoMFA and CoMSIA studies
Tsai, K., Chen, Y., Hsiao, N., Wang, C.-L., Lin, C., Lee, Y., Li, M., & Wang, B. (2010) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2009.12.063 · Cited by 83
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Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes
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Quantitative Structure-Activity Relationship (QSAR) Studies on the Toxic Effects of Nitroaromatic Compounds (NACs): A Systematic Review
Huang, T., Sun, G., Zhao, L., Zhang, N., Zhong, R., & Peng, Y. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22168557 · Cited by 82
Assessment of bromide-based ionic liquid toxicity toward aquatic organisms and QSAR analysis
Wang, C., Wei, Z., Wang, L., Sun, P., & Wang, Z. (2015) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2015.02.012 · Cited by 82
Quantitative Structure–Activity Relationship Models of Clinical Pharmacokinetics: Clearance and Volume of Distribution
Gombar, V. K., & Hall, S. D. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400001u · Cited by 82
Pseudoreceptor models in drug design: bridging ligand- and receptor-based virtual screening
Tanrikulu, Y., & Schneider, G. (2008) · Nature Reviews Drug Discovery · doi:10.1038/nrd2615 · Cited by 82
An all atom energy based computational protocol for predicting binding affinities of protein–ligand complexes
Jain, T., & Jayaram, B. (2005) · FEBS Letters · doi:10.1016/j.febslet.2005.10.031 · Cited by 82
“Recent studies [47,48] have shown that, in addition to leave-one-out (LOO) cross-validation (q(2)) procedure, validation of the model using an external test set of compounds is”
Method
Density Functional Theory and Electrochemical Studies: Structure–Efficiency Relationship on Corrosion Inhibition
Camacho-Mendoza, R. L., Gutiérrez, E., Percástegui, E. G., Aquino‐Torres, E., Cruz‐Borbolla, J., Rodríguez, J. A., Alvarado-Rodríguez, J. G., Olvera‐Neria, O., Thangarasu, P., & Medina‐Franco, J. L. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00385 · Cited by 82
Chemometrics in QSAR
Todeschini, R., Consonni, V., & Gramatica, P. (2009) · Elsevier eBooks · doi:10.1016/b978-044452701-1.00007-7 · Cited by 85
Constitutive modelling of cemented paste backfill: A data-mining approach
Qi, C., Chen, Q., Fourie, A. B., Tang, X., Zhang, Q., Dong, X., & Feng, Y. P. (2018) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2018.11.142 · Cited by 81
Ensemble Learning Regression for Estimating Unconfined Compressive Strength of Cemented Paste Backfill
Lü, X., Zhou, W., Ding, X., Shi, X., Luan, B., & Li, M. (2019) · IEEE Access · doi:10.1109/access.2019.2918177 · Cited by 81
New empirical approach for determining nominal shear capacity of steel fiber reinforced concrete beams
Ahmadi, M., Kheyroddin, A., Dalvand, A., & Kioumarsi, M. (2019) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2019.117293 · Cited by 81
Formulation of shear strength of slender RC beams using gene expression programming, part I: Without shear reinforcement
Gandomi, A. H., Alavi, A. H., Kazemi, S., & Gandomi, M. (2014) · Automation in Construction · doi:10.1016/j.autcon.2014.02.007 · Cited by 80
Effect of porosity on predicting compressive and flexural strength of cement mortar containing micro and nano-silica by multi-objective ANN modeling
Kooshkaki, A., & Eskandari‐Naddaf, H. (2019) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2019.03.243 · Cited by 83
Multivariate adaptive regression splines model for reinforced soil foundations
Raja, M. N. A., & Shukla, S. K. (2020) · Geosynthetics International · doi:10.1680/jgein.20.00049 · Cited by 81
QSAR, ADMET In Silico Pharmacokinetics, Molecular Docking and Molecular Dynamics Studies of Novel Bicyclo (Aryl Methyl) Benzamides as Potent GlyT1 Inhibitors for the Treatment of Schizophrenia
fadili, M. E., Er-rajy, M., Kara, M., Assouguem, A., Belhassan, A., Alotaibi, A., Mrabti, N. N., Fidan, H., Ullah, R., Erċışlı, S., et al. (2022) · Pharmaceuticals · doi:10.3390/ph15060670 · Cited by 81
“Additionally, the Golbreikh and Tropsha criteria and the Y-randomization test were used to verify the robustness and predictive potential of the established QSAR model [31,46].”
Method
Estimating compressive strength of concrete containing rice husk ash using interpretable machine learning-based models
Alyami, M., Nassar, R., Khan, M., Hammad, A. W. A., Alabduljabbar, H., Nawaz, R., Fawad, M., & Gamil, Y. (2024) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2024.e02901 · Cited by 80
“It is stated that the line going through the origin must have a slope (k or k ′ ) equal to 1 [162].”
Synthesis, antimycobacterial, antiviral, antimicrobial activities, and QSAR studies of nicotinic acid benzylidene hydrazide derivatives
Narang, R., Narasimhan, B., Sharma, S., Sriram, D., Yogeeswari, P., Clercq, E. D., Pannecouque, C. C., & Balzarini, J. M. (2011) · Medicinal Chemistry Research · doi:10.1007/s00044-011-9664-7 · Cited by 80
“One should not forget the recommendations of Golbraikh and Tropsha (2002) who have recently reported that the only way to estimate the true predictive power of a model is to test their ability to Table 9 Correlation of molecular descriptors with antimicrobial (antibacterial and antifungal)…”
BackgroundInfluential
Structure–activity relationship study of antioxidative peptides by QSAR modeling: the amino acid next to C‐terminus affects the activity
Li, Y., Li, B., He, J., & Qian, P. (2011) · Journal of Peptide Science · doi:10.1002/psc.1345 · Cited by 80
Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches
Zhang, L., Sedykh, A., Tripathi, A., Zhu, H., Afantitis, A., Mouchlis, V. D., Melagraki, G., Rusyn, I., & Tropsha, A. (2013) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2013.04.032 · Cited by 84
“Y-randomization was applied in duplicate to all training/test dataset divisions considered in this study as detailed in (Golbraikh and Tropsha, 2002).”
InfluentialSelf-citation

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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