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Correction published on 26 January 2010, see Molecules 2010, 15(1), 604-605.

Open AccessArticle
Molecules 2009, 14(5), 1660-1701;

On Two Novel Parameters for Validation of Predictive QSAR Models

Drug Theoretics and Cheminformatics Lab, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India
Author to whom correspondence should be addressed.
Received: 16 April 2009 / Accepted: 28 April 2009 / Published: 29 April 2009
(This article belongs to the Special Issue Molecular Diversity Feature Papers)
PDF [235 KB, uploaded 18 June 2014]


Validation is a crucial aspect of quantitative structure–activity relationship (QSAR) modeling. The present paper shows that traditionally used validation parameters (leave-one-out Q2 for internal validation and predictive R2 for external validation) may be supplemented with two novel parameters rm2 and Rp2 for a stricter test of validation. The parameter rm2(overall) penalizes a model for large differences between observed and predicted values of the compounds of the whole set (considering both training and test sets) while the parameter Rp2 penalizes model R2 for large differences between determination coefficient of nonrandom model and square of mean correlation coefficient of random models in case of a randomization test. Two other variants of rm2 parameter, rm2(LOO) and rm2(test), penalize a model more strictly than Q2 and R2pred respectively. Three different data sets of moderate to large size have been used to develop multiple models in order to indicate the suitability of the novel parameters in QSAR studies. The results show that in many cases the developed models could satisfy the requirements of conventional parameters (Q2 and R2pred) but fail to achieve the required values for the novel parameters rm2 and Rp2. Moreover, these parameters also help in identifying the best models from among a set of comparable models. Thus, a test for these two parameters is suggested to be a more stringent requirement than the traditional validation parameters to decide acceptability of a predictive QSAR model, especially when a regulatory decision is involved. View Full-Text
Keywords: QSAR; Validation; Internal validation; External validation; Randomization QSAR; Validation; Internal validation; External validation; Randomization

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Pratim Roy, P.; Paul, S.; Mitra, I.; Roy, K. On Two Novel Parameters for Validation of Predictive QSAR Models. Molecules 2009, 14, 1660-1701.

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