The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
This paper emphasizes the importance of rigorous validation as a crucial, integral component of Quantitative Structure Property Relationship (QSPR) model development. We consider some examples of published QSPR models, which in spite of their high fitted accuracy for the training sets and apparent m...
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| Published in | QSAR & combinatorial science Vol. 22; no. 1; pp. 69 - 77 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Weinheim
WILEY-VCH Verlag
01.04.2003
WILEY‐VCH Verlag |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1611-020X 1611-0218 |
| DOI | 10.1002/qsar.200390007 |
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| Abstract | This paper emphasizes the importance of rigorous validation as a crucial, integral component of Quantitative Structure Property Relationship (QSPR) model development. We consider some examples of published QSPR models, which in spite of their high fitted accuracy for the training sets and apparent mechanistic appeal, fail rigorous validation tests, and, thus, may lack practical utility as reliable screening tools. We present a set of simple guidelines for developing validated and predictive QSPR models. To this end, we discuss several validation strategies including (1) randomization of the modelled property, also called Y‐scrambling, (2) multiple leave‐many‐out cross‐validations, and (3) external validation using rational division of a dataset into training and test sets. We also highlight the need to establish the domain of model applicability in the chemical space to flag molecules for which predictions may be unreliable, and discuss some algorithms that can be used for this purpose. We advocate the broad use of these guidelines in the development of predictive QSPR models. |
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| AbstractList | This paper emphasizes the importance of rigorous validation as a crucial, integral component of Quantitative Structure Property Relationship (QSPR) model development. We consider some examples of published QSPR models, which in spite of their high fitted accuracy for the training sets and apparent mechanistic appeal, fail rigorous validation tests, and, thus, may lack practical utility as reliable screening tools. We present a set of simple guidelines for developing validated and predictive QSPR models. To this end, we discuss several validation strategies including (1) randomization of the modelled property, also called Y‐scrambling, (2) multiple leave‐many‐out cross‐validations, and (3) external validation using rational division of a dataset into training and test sets. We also highlight the need to establish the domain of model applicability in the chemical space to flag molecules for which predictions may be unreliable, and discuss some algorithms that can be used for this purpose. We advocate the broad use of these guidelines in the development of predictive QSPR models. |
| Author | Tropsha, Alexander Gombar, Vijay K. Gramatica, Paola |
| Author_xml | – sequence: 1 givenname: Alexander surname: Tropsha fullname: Tropsha, Alexander email: alex_tropsha@unc.edu organization: Laboratory for Molecular Modeling, School of Pharmacy, CB# 7360 Beard Hall, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, U.S.A – sequence: 2 givenname: Paola surname: Gramatica fullname: Gramatica, Paola email: paola.gramatica@uninsubria.it organization: QSAR and Environmental Chemistry Research Unit, Department of Structural and Functional Biology, University of Insubria, Via J. H. Dunant 3 - 21100 Varese, Italy – sequence: 3 givenname: Vijay K. surname: Gombar fullname: Gombar, Vijay K. email: vijay.k.gombar@gsk.com. organization: GlaxoSmithKline, Metabolic and Viral Diseases' Center of Excellence for Drug Discovery (MV CEDD), Department of Drug Metabolism and Pharmacokinetics (DMPK), 3030 Cornwallis Road, Research Triangle Park, NC 27709, U.S.A |
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| References_xml | – reference: Lindberg, W., Persson, J.-A., and Wold, S., Partial Least-Squares Method for Spectrofluorimetric Analysis of Mixtures of Humic Acid and Ligninsulfonate, Anal. Chem. 55, 643-648 (1983). – reference: Zheng, W., and Tropsha, A., Novel Variable Selection Quantitative Structure-Property Relationship Approach Based on the k-Nearest-Neighbor Principle, J. Chem. Inform. Comput. Sci. 40, 185-194 (2000). – reference: Mandel, J., The Regression Analysis of Collinear Data, J. Res. Nat. Bur. Stand. 90, 465-476 (1985). – reference: Yasri, A., and Hartsough, D., Toward an Optimal Procedure for Variable Selection and QSAR Model Building, J. Chem. Inf. Comput. Sci. 41, 1218-1227 (2001). – reference: Mitchell, T. J., An Algorithm for the Construction of "D-optimal" Experimental Designs, Technometrics 16, 203-210 (1974). – reference: Cramer III, R. D., Patterson, D. E., and Bunce, J. D., Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins, J. Amer. Chem. Soc. 110, 5959-5967 (1988). – reference: Cho, S. J., Zheng, W., and Tropsha, A., Rational Combinatorial Library Design. 2. Rational Design of Targeted Combinatorial Peptide Libraries Using Chemical Similarity Probe and the Inverse QSAR Approaches, J. Chem. Inf. Comput. Sci. 38, 259-268 (1998). – reference: Novellino, E., Fattorusso, C., and Greco, G., Use of Comparative Molecular Field Analysis and Cluster Analysis in Series Design, Pharm. Acta Helv. 70, 149-154 (1995). – reference: Suzuki, T., Ide, K., Ishida, M., and Shapiro, S., Classification of Environmental Estrogens by Physicochemical Properties Using Principal Component Analysis and Hierarchical Cluster Analysis, J. Chem. Inf. Comput. 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| SubjectTerms | applicability domain model validation QSAR Structure-property relationship modeling |
| Title | The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models |
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