SciELO - Scientific Electronic Library Online

vol.20 issue4Determination and fractionation of barium in Brazil nutsMultivariate optimization and validation of an analytical method for the determination of cadmium in wines employing ET AAS author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand




Related links


Journal of the Brazilian Chemical Society

Print version ISSN 0103-5053On-line version ISSN 1678-4790

J. Braz. Chem. Soc. vol.20 no.4 São Paulo  2009 



Basic validation procedures for regression models in QSAR and QSPR studies: theory and application



Rudolf Kiralj; Márcia M. C. Ferreira*

Laboratory for Theoretical and Applied Chemometrics, Institute of Chemistry, State University of Campinas, P.O. Box 6154, 13083-970 Campinas-SP, Brazil




Four quantitative structure-activity relationships (QSAR) and quantitative structure-property relationship (QSPR) data sets were selected from the literature and used to build regression models with 75, 56, 50 and 15 training samples. The models were validated by leave-one-out crossvalidation, leave-N-out crossvalidation (LNO), external validation, y-randomization and bootstrapping. Validations have shown that the size of the training sets is the crucial factor in determining model performance, which deteriorates as the data set becomes smaller. Models from very small data sets suffer from the impossibility of being thoroughly validated, failure and atypical behavior in certain validations (chance correlation, lack of robustness to resampling and LNO), regardless of their good performance in leave-one-out crossvalidation, fitting and even in external validation. A simple determination of the critical Nin LNO has been introduced by using the limit of 0.1 for oscillations in Q2, quantified as the variation range in single LNO and two standard deviations in multiple LNO. It has been demonstrated that it is sufficient to perform 10 -25 y-randomization and bootstrap runs for a typical model validation. The bootstrap schemes based on hierarchical cluster analysis give more reliable and reasonable results than bootstraps relying only on randomization of the complete data set. Data quality in terms of statistical significance of descriptor -yrelationships is the second important factor for model performance.Variable selection that does not eliminate insignificant descriptor - yrelationships may lead to situations in which they are not detected during model validation, especially when dealing with large data sets.

Keywords: leave-one-out crossvalidation, leave-N-out crossvalidation, y-randomization, external validation, bootstrapping


Quatro conjuntos de dados de QSAR e QSPR foram selecionados da literatura e os modelos de regressão foram construídos com 75, 56, 50 e 15 amostras no conjunto de treinamento. Estes modelos foram validados por meio de validação cruzada excluindo uma amostra de cada vez, validação cruzada excluindo Namostras de cada vez (LNO), validação externa, randomização do vetor ye validação bootstrap. Os resultados das validações mostraram que o tamanho do conjunto de treinamento é o fator principal para o bom desempenho de um modelo, uma vez que este piora para os conjuntos de dados pequenos. Modelos oriundos de conjuntos de dados muito pequenos não podem ser testados em toda a sua extensão. Além disto, eles podem falhar e apresentar comportamento atípico em alguns dos testes de validação (como, por exemplo, correlações espúrias, falta de robustez na reamostragem e na validação cruzada), mesmo tendo apresentado um bom desempenho na validação cruzada excluindo uma amostra, no ajuste e até na validação externa. Uma maneira simples de determinar o valor crítico de Nem LNO foi introduzida, usando o valor limite de 0,1 para oscilações em Q2 (faixa de variações em único LNO e dois desvios padrões em LNO múltiplo). Foi mostrado que 10 - 25 ciclos de randomização de you de bootstrappingsão suficientes para uma validação típica. O uso do método bootstrapbaseado na análise de agrupamentos por métodos hierárquicos fornece resultados mais confiáveis e razoáveis do que aqueles baseados somente na randomização do conjunto de dados completo. A qualidade de dados em termos de significância estatística das relações descritor - yé o segundo fator mais importante para o desempenho do modelo. Uma seleção de variáveis em que as relações insignificantes não foram eliminadas pode conduzir a situações nas quais elas não serão detectadas durante o processo de validação do modelo, especialmente quando o conjunto de dados for grande.




Multivariate regression models in chemistry and other sciences quantitatively relate a response (dependent) variable yto a block of predictor variables X, in the form of a mathematical equation y = f(X), where the predictors can be determined experimentallyorcomputationally.Among the best known of such quantitative-X-yrelationships (QXYR) are quantitative structure-activity relationships (QSAR)1-3 and quantitative structure-property relationships (QSPR),4,5 in which yis a biological response (QSAR) or physical or chemical property (QSPR), and any of the predictors, designated as descriptors, may account for a microscopic (i.e., determined by molecular structure) or a macroscopic property. QSAR has become important in medicinal chemistry, pharmacy, toxicology and environmental science because it deals with bioactive substances such as drugs and toxicants. QSPR has become popular in various branches of chemistry (physical, organic, medicinal, analytical etc.) and materials science. There are many other types of QXYR, some of which represent variants of or are closely related to QSAR or QSPR. It is worth mentioning quantitative structure-retention relationship (QSRR),6 adsorption-distribution-metabolism-excretion-toxicity (ADMET) relationship,7 quantitative composition-activity relationship (QCAR),8 linear free energy relationship (LFER),9 linear solvent energy relationship (LSER)10 and quantitative structure-correlations in structural science.11,12 QXYR are also found in cheminformatics,13 for example, using z-scales or scores of amino-acids or nucleotides as molecular descriptors,14,15 and in bioinformatics where the primary sequence of nucleic acids, peptides and proteins is frequently understood as the molecular structure for generation of independent variables.16-18 Other QXYR deal with relationships among various molecular features19 and parameters of intermolecular interactions20 in computational and quantum chemistry, and correlate various chemical and physical properties of chemicals in chemical technology.21,22 In this work, all types of QXYR will be termed as QSAR and QSPR rather than molecular chemometrics23 because Xcan be a block of macroscopic properties which are not calculated from molecular structure.

Continuous progress of science and technology24 is the generator for a vast diversity of QSAR and QSPR approaches vianew mathematical theories, computational algorithms and procedures, and advances in computer technology, where chemometrics is the discipline for merging all these elements. Health problems, search for new materials, and environmental and climate changes give rise to new tasks for QSAR and QSPR, as can be noted in the literature. Mathematical methodologies employed in QSAR and QSPR cover a wide range, from traditional regression methods25-28 such as multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) regression to more diverse approaches of machine learning methods such as neural networks29,30 and support vector machines.31 Modern computer programs are capable of generating hundreds and even thousands of descriptors for Xand, in specific kinds of problems even variables for y, in a very easy and fast way. Time for and costs of testing chemicals in bioassays, and several difficulties in physical and chemical experiments are the reasons for more and more variables being computed instead of being measured. Regression models y = f(X) are obtained from these descriptors with the purpose of comprehensive prediction of values of y. Finally, the statistical reliability of the models is numerically and graphically tested32-34 in various procedures called by the common name of model validation,35-38 accompanied by other relevant verifications and model interpretation.3-5,39

Even though the terms validationand to validateare frequent in chemometric articles, these words are rarely explained.40 Among detailed definitions of validationin chemometric textbooks, of special interest is that discussed by Smilde et al.,32 who pointed out that validationincludes theoretical appropriateness, computational correctness, statistical reliability and explanatory validity. According to Brereton,41 to validateis equivalent to "to answer several questions" on a model's performance, and for Massart et al.,42 to validatea model means "to verify that the model selected is the correct one", "to check the assumptions" on which the model has to be based, and "to meet defined standards of quality". Validationfor Snee43 is a set of "methods to determine the validity of regression models."

The purpose of this work is to present, discuss and give some practical variants of five validation procedures which are still not-so-commonly used44 in QSAR and QSPR works: leave-one-out crossvalidation, leave-N-out crossvalidation, y-randomization, bootstrapping (least known among the five) and external validation.23,38,40,44-46 This statistical validation is the minimum recommended as standard in QSAR and QSPR studies for ensuring reliability, quality and effectiveness of the regression models for practical purposes. Unlike instrumental data generated by a spectrometer, where a huge set of predictors of the same nature (intensities, in general of the same order of magnitude) are highly correlated among themselves and to the dependent variable (analyte concentration) viaa known physical law (Beer's law), QSAR and QSPR data are more complex and obscure. In QSAR and QSPR studies, the descriptors are of different natures and orders of magnitude and, therefore careful variable selection and rigorous model validation are crucial.


Five Basic Validation Procedures

The basic statistical parameters, such as root mean square errors (standard deviations), "squared form" correlation coefficients which are regularly used in QSAR and QSPR, and the respective Pearson correlation coefficients that can be also found in several studies, are described in detail in Table 1.

The purpose of validation is to provide a statistically reliable model with selected descriptors as a consequence of the cause-effect and not only of pure numerical relationship obtained by chance. Since statistics can never replace chemistry, non-statistical validations (chemical validations5) such as verification of the model in terms of the known mechanism of action or other chemical knowledge, are also necessary. This step becomes crucial for those cases where no mechanism of action is known and also for small and problematic data sets, when some statistical tests are not applicable but the mechanism of action of compounds is well known so that the selected descriptors may be justified a priori.

Requirements for the structure of data sets

Statistical validation of the final model should start with all samples in random order and a ready and "clean" data set, i.e., where variable selection has been already performed and outliers removed. Randomness is important since a user-defined samples' order frequently affects the validation, because regularly increasing or decreasing values of variables may be correlated to the position of samples within the set or its blocks (subsets). The structure of such data sets can be characterized by their size, data set split, statistical distribution of all descriptors and the dependent variable, and structural diversity of samples.

From the statistical point of view, small data sets, sets with a small number of samples, may suffer from various deficiencies like chance correlation, poor regression statistics and inadequacy for carrying out various statistical tests as well as unwanted behavior in performed tests. Any of those may lead to false conclusions in model interpretation and to spurious proposals for the mechanism of action of the studied compounds. Working with small data sets is delicate and even questionable, and it should be avoided whenever possible.

The number of predictor variables (descriptors) also defines the data size. It is generally accepted that there must be at least five samples per descriptor (Topliss ratio) for a simple method as MLR.38,44 However, PCR and PLS allow using more descriptors, but too many descriptors may cause difficulties in model interpretability. Besides, using several factors (principal components or latent variables) can make model interpretation tedious and lead to a problem similar to that just mentioned about MLR (using too many factors means low compression of original descriptors).

Data set split is another very important item which strongly depends on the size of the whole set and the nature and aim of the study. In an ideal case, the complete data set is split into a training (learning) set used for building the model, and an external validation set (also called test or prediction set) which is employed to test the predictive power of the model. The external validation set should be distinguished from a data set additionally created only to make predictions. This data set, which is sometimes also called prediction set, is blind with respect to eventual absence of dependent variable and has never participated in any modeling step, including variable selection and outlier detection. In cases of small data sets and for special purposes, it is necessary to build first the model with all samples and, a posteriori, construct an analogous one based on the split data. In this article, the former and latter models are denominated as the real and auxiliary models, respectively. The aim of the auxiliary model is to carry out validations that are not applicable for the real model (external validation and bootstrapping). Since the auxiliary model has fewer samples than the real, it is expected that its statistics should be improved if the validations were performed on the real model.

It is expected that variables in QSAR and QSPR models follow some defined statistical distribution, most commonly the normal distribution. Moreover, descriptors and the dependent variable should cover sufficiently wide ranges of values, the size of which strongly depends on the nature of the study. From our experience, biological activities expressed as molar concentrations should vary at least two orders of magnitude. Statistical distribution profile of dependent and independent variables can easily be observed in simple histograms which are powerful tools for detection of badly constructed data sets. Examples include histograms with too large gaps, poorly populated or even empty regions, as well as highly populated narrow intervals. Such scenarios are an indication that the studied compounds, in terms of molecular structures, were not sufficiently diverse, i.e., on the one hand, one or more groups of compounds are characterized by small structural differences and on the other hand, there are structurally specific and unique molecules. A special case of such a molecular set is a degenerate (redundant in samples) set,37 containing several enantiomers, close structural isomers and very similar molecules. These samples will probably contain descriptors that have only a few distinct numerical have very similar or equal values of molecular descriptors values (two or three), which is not always a consequence and of the dependent variable, which can contribute to of degenerate samples. These descriptors behave as poor model performance in some validation procedures. qualitative variables and should be also avoided, to reduce This is the reason why degenerate samples should be data degeneracy (variable redundancy). For this purpose, avoided whenever possible. Furthermore, a data set may two cases should be distinguished. The first is of so-called indicator variables,47,48 which, by their definition, possess only a few distinct values. The very few values of indicator variables should have approximately equal frequency; this way, model validation should always yield reasonable results. The second accounts for other types of descriptors, which, according to their meaning, should contain several distinct numerical values (integers or real numbers), but because of problem definition, computational difficulties, lack of sufficient experimental data, etc., become highly degenerate. When this occurs, one should replace these descriptors by others, or even consider redefining the problem under study.

Leave-one-out crossvalidation

Leave-one-out (LOO) crossvalidation is one of the simplest procedures and a cornerstone for model validation. It consists of excluding each sample once, constructing a new model without this sample (new factors or latent variables are defined), and predicting the value of its dependent variable, yc. Therefore, for a training set of Msamples, LOO is carried out Mtimes for one, two, three, etc. factors in the model, resulting in Mpredicted values for each number of factors. The residuals, yc-ye (differences between experimental and estimated values from the model) are used to calculate the root mean square error of crossvalidation (RMSECV) and the correlation coefficient of leave-one-out crossvalidation (Q2yrand ), as indicated in Table 1.

The prediction statistics of the final model are expressed by the root mean square error of calibration (RMSEC) and the correlation coefficient of multiple determination (R2yrand), calculated for the training set. Since LOO represents certain perturbations to the model and data size reduction, the corresponding statistics are always characterized by the relations R2yrand > Q2yrand and RMSEC < RMSECV. The minimum acceptable statistics for regression models in QSAR and QSPR include conditions Q2yrand > 0.5 and R2yrand> 0.6.44,49 A large difference between R2yrandand Q2yrand , exceeding 0.2 - 0.3, is a clear indication that the model suffers from overfitting.38,46

Leave-N-out crossvalidation

Leave-N-out (LNO) crossvalidation,45,50,51 known also as leave-many-out, is highly recommended to test the robustness of a model. The training set of Msamples is divided into consecutive blocks of Nsamples, where the first Ndefine the first block, the following Nsamples is the second block, and so on. This way, the number of blocks is the integer of the ratio M/Nif Mis a multiple of N; otherwise the left out samples usually make the last block. This test is based on the same basic principles as LOO: each block is excluded once, a new model is built without it, and the values of the dependent variable are predicted for the block in question. LNO is performed for N= 2, 3, etc., and the leave-N-out crossvalidated correlation coefficients Q2LNO are calculated in the same way as for LOO (Table 1). LNO can be performed in two modes: keeping the same number of factors for each value of N(determined by LOO for the real model) or with the optimum number of factors determined by each model.

Contrary to LOO, LNO is sensitive to the order of samples in the data set. For example, leave-two-out crossvalidation for even Mmeans that M/2 models are obtained, but this is only a small fraction (0.5·(M- 1 )-1) of all possible combinations of two samples M!/(M- 2)! = M(M- 1). To avoid any systematic variation of descriptors through a data set or some subset what would affect LNO, the samples should be randomly ordered (in Xand ysimultaneously).

It is recommended that Nrepresents a significant fraction of samples (like leave-20 to 30% - out for smaller data sets40). It has been shown recently52 that repeating the LNO test for scrambled data and using average of Q2LNO with its standard deviation for each N, is statistically more reliable than LNO being performed only once. This multiple LNO test can be also performed in the two modes, with fixed or optimum number of factors. The critical Nis the maximum value of Nat which Q2LNO is still stable and high. It is primarily determined by the size of a data set and somewhat less by its quality. For a good model, Q2LNO should stay close to Q2 from LOO, with small variations at all values for Nup to the critical N. For single LNO, these variations can be quantified in the following way.Variations for single LNO are expressed as the range of Q2LNO values, which shows how much Q2LNO oscillates around its average value. By our experience, this range for single LNO should not exceed 0.1. In case of multiple LNO, a more rigorous criterion should be used, where two standard deviations should not be greater than 0.1 for N= 2, 3, etc., including the critical value of N.


The purpose of the y-randomization test45,46,50,53,54 is to detect and quantify chance correlations between the dependent variable and descriptors. In this context, the term chance correlation means that the real model may contain descriptors which are statistically well correlated to ybut in reality there is no cause-effect relationship encoded in the respective correlations with ybecause they are not related to the mechanism of action. The y-randomization test consists of several runs for which the original descriptors matrix Xis kept fixed, and only the vector yis randomized (scrambled). The models obtained under such conditions should be of poor quality and without real meaning. One should be aware that the number of factors is kept the same as for the real model, since y-randomization is not based on any parameter optimization. The basic LOO statistics of the randomized models (Q2yrand and R2yrand) should be poor, otherwise, each resulting model may be based on pure numerical effects.

Two main questions can be raised regarding y-randomization: how to analyze the results from each randomization run and how many runs should be carred out? There are various approaches to judge whether the real model is characterized by chance correlation. The simple approach of Eriksson and Wold53 can be summarized as a set of decision inequalities based on the values of Q2yrand and R2yrand and their relationship R2yrand > Q2yrand :

Q2yrand < 0.2 and R2yrand< 0.2 → no chance correlation;

any Q2yrand and 0.2 < R2yrand< 0.3 → negligible chance correlation;

any Q2yrand and 0.3 <R2yrand< 0.4 → tolerable chance correlation;

any Q2yrand and R2yrand > 0.4 → recognized chance correlation.

Therefore, the correlation's frequency is counted as the number of randomizations which resulted in models with spurious correlations (falsely good), which is easily visible in a Q2yrand against R2yrand plot that also includes Q2 and R2 values for the real model.

In another approach,54 the smallest distance between the real model and all randomized models in units of Q2 or R2 is identified. This minimum distance is then expressed relative to the respective standard deviation for the randomization runs. The distinction of the real model from randomized models is judged in terms of an adequate confidence level for the normal distribution. A simple procedure proposed in the present work, is to count randomized models which are statistically not distinguished from the real model (confidence levels are greater than 0.0001).

There is another approach to quantify chance correlation in the literature,46 based on the absolute value of the Pearson correlation coefficient, r, between the original vector yand randomized vectors y. Two yrandomization plots r -Q2yrand and r- R2yrandare drawn for randomized and real models, and the linear regression lines are obtained:

The real model is characterized as free of chance correlation when the intercepts are aQ < 0.05 and aR < 0.3. These intercepts are measures for the background chance correlation, i.e., intrinsic chance correlation encoded in X, which is visible when statistical effects of randomizing the yvector are eliminated, i.e., the correlation between original and randomized y vectors is equal to zero (r = 0).

The number of randomized models encoding chance correlation depends primarily on two statistical factors. It strongly increases with the decrease of the number of samples in the training set, and is increased moderately for large number of randomization runs.54 Chemical factors, such as the nature of the samples and their structural similarity, data quality, distribution profile of each variable and variable intercorrelations, modify to a certain extent these statistical dependences. The approach of Wold and Eriksson53 consists of ten randomization runs for any data set size. This is a sufficiently sensitive test because models based on chance correlation easily fail in one or more (i.e., at least 10%) randomization runs. Several authors propose hundreds or thousands of randomizations independent of the data set size, while others argue that the number of randomizations should depend on the data size. The authors of this work have shown recently55 that 10 and 1000 randomization runs provide the same qualitative information and, moreover, that the statistics for these two approaches are not clearly distinguished when the linear relationships (1) and (2) and that one between Q2yrand and R2yrandare inspected.Accordingly, it is expected that poor models will show unwanted performance in y-randomization, while good models will be free from chance correlation even for a small number of randomizations, as will be shown by the examples in this work.


Bootstrapping56,57 is a kind of validation in which the complete data set is randomly split several times into training and test sets, the respective models are built and their basic LOO statistics (Q2bstr and R2bstr) are calculated and compared to that of the real model. Unlike validations (LOO and LNO) where each sample is excluded only once, in bootstraping a sample may be excluded once, or several times, as well as never. Since in each bootstrap run a new model is built, it is expected that the values of Q2bstr and R2bstr satisfy the minimum acceptable LOO statistics in all bootstrap runs, and that they oscillate around the real Q2and R2 (the LOO statistics of the real model) within reasonable ranges. The aim of bootstrapping is to perturb the training set, whilst statistics of the test set are not considered.

There are two issues to be considered when performing this validation. One is the procedure for making the bootstrappings i.e., data splits or resamplings, and the other is their number. Different number of splits have been proposed in the literature, ranging from low (ten) to high (hundreds). By its basic conception, bootstrapping does not require that the data split is based on high structural similarity between the training and test sets. The literature proposes random selection of samples for the training set by means of algorithms frequently coupled to various statistical procedures and also a rational split based on data subsets (clusters) in hierarchical cluster analysis (HCA).25,28 The size of the complete data set is the main factor that influences bootstrap procedures. In general, a small data set is difficult to split and exclusion of significant portion of its samples may seriously harm the model's performance. Exclusion of about 30% of samples from the complete set is a reasonable quantity for smaller sets40 consisting of a few clusters of samples, some of which are poorly populated. Therefore, purely random procedures that do not take into account the structure and population of the clusters may produce unrealistically good or poor models in particular bootstrap runs. Random sampling within each HCA cluster, or within other types of clusters as, for example, obtained from ydistribution (low, moderate and highly active compounds), better reflects the chemical structure of the complete data set. In large data sets, highly populated clusters will be always well represented in any random split, making clear why such sets are practically insensitive to exclusion of a significant portion of samples (more than 50%), independent of the type of random split employed.

External validation

Unlike bootstrapping, the external validation test requires only one split of the complete data set into structurally similar training and external validation sets. The purpose of this validation is to test the predictive power of the model. Basic statistical parameters that are used to judge the external validation performance (Table 1) are the root mean square error of prediction (RMSEP), the correlation coefficient of external validation (Q2ext) and the Pearson correlation coefficient of prediction (Rext). Q2ext quantifies the validation and is analogous to Q2 from LOO, with exception of the term w(see Table 1), which is the average value of the dependent variable yfor the training set and not the external validation set. Rext is a measure of fitting for the external validation set and can be compared to R for the training set.

When performing external validation, two issues have to be dealt with. One is the number of samples in the external validation set and the other is the procedure for selecting them. It is recommended to use 30% of samples for the external validation of smaller data sets40 and to keep the same percentage of external samples in bootstrapping and external validation.43 There are various procedures for selecting external samples. All of them have to provide chemical analogy between the training and external samples, structural proximity between the two data sets (similar variable ranges and variable distributions as a consequence of similar molecular diversities), and to provide external predictions as interpolation and not extrapolation. A reasonable approach for splitting the complete data set, which takes all these items into account is to use HCA combined with principal component analysis (PCA)25,28 scores, ydistribution (e.g., low, moderate and high biological activity) and other sample classification.



Data sets

Four data sets of different dimensions were selected from the literature.5,39,53-55 Basic information about them, including splits adopted and validations performed in this work, are presented in Table 2. The complete data sets are in Supplementary Information (Tables T1-T4). The new splits performed in this work were based on exploratory analysis of autoscaled complete data sets, always using clusters from HCA with complete linkage method,25 combined with PCA, ydistribution and some sample classification known a priori. The regression models, MLR and PLS, were built using data previously randomized and autoscaled.

The QSAR data set 1 comprises five molecular descriptors and toxicity, -log[IGC50/(mol L-1)], against a ciliate T. pyriformisfor 153 polar narcotics (phenols). This data set was originally defined by Aptula et al.,58 and it was used for the first time to build a MLR model by Yao et al.59 who also made a modest data split (14% samples out for the external validation). In this work, the real MLR model is based on a rather radical split (51% out) in order to show that even data sets of moderate size can enable good splitting and model performance in all validations.

The data set 2 is from a quantitative genome/structureactivity relationship (QGSAR) study,39 a hybrid of QSAR and bioinformatics, in which the resistance of 24 strains of the phytopathogenic fungus P. digitatumagainst four demethylation inhibitors was investigated by PLS models. This data set consists of toxicity values -log[EC50/(mol L-1)] for 86 samples, described by eight descriptors, from which three are fungal genome descriptors and five are products of some molecular and genome features. The reader may look at the original work39 for the definition of the matrix X. A new data split was applied in this work (35% out), which is more demanding than in the original publication. The purpose of this example is to show that even modest data sets can be successfully validated without using an auxiliary regression model.

The data set 3 is from a QSPR study on the carbonyl oxygen chemical shift (18O) in 50 substituted benzaldehydes,5 comprising eight molecular descriptors and the shifts δ/ppm. Two literature PLS models, the real and the auxiliary model (20% out), are inspected in more details with respect to the validations carried out, especially bootstrapping.

The smallest data set 4 is from a series of QSAR studies based on MLR models,60 and it was used to predict mouse cyclooxigenase-2 inhibition by 2-CF3-4-(4-SO2Mephenyl)-5-(X-phenyl)-imidazoles. It consists of three molecular descriptors and the anti-inflammatory activity -log[IC50/molL-1] for 15 compounds. Only a very modest split (13% out) could be applied in this example, to show that very small data sets cannot provide reliable statistics in all the applied validations.

All chemometric analyses were carried out by using the software Pirouette® 61 and MATLAB®.62


Samples in all data sets were randomized prior to any validation. All single and multiple (10 times) leave-N-out (LNO) cross-validations were carried out by determining the optimum number of factors for each Nwhen using PLS. For each data set, 10 and 25 randomizations were tested, to show the effect of the number of runs on chance correlation statistics. The same data split was used in external validation and bootstrapping, as suggested in the literature,43 to allow comparison between the respective statistics.At least two different bootstrap split schemes were applied for each data set, where the randomized selection of the training samples was made from the complete set, from subsets (clusters) in HCA, and other types of subsets (PCA clusters, ydistribution, or some other sample classification). To demonstrate the effect of the number of resamplings on bootstrap statistics, 10 and 25 runs were carried out for each split scheme.

Inspection of data quality versus data set size

Data set size is the primary but not the sole factor that affects model performance. To evaluate how much the data quality, i.e., descriptors and their correlations with y, affect the model performance, the following investigations were carried out. First, data sets 1, 2 and 3 were reduced to the size of data set 4 (15 samples) according to the following principles: a) all descriptors possessed at least three distinct values; b) samples were selected throughout the whole range of y; c) very influential samples were avoided; and d) one or more samples were selected from each HCA cluster already defined in bootstrapping, proportionally to cluster size. Eventually formed subsets were subject to all validations in the very same way as data set 4. Second, the relationships between descriptors and ywere inspected for all data sets and subsets in the form of the linear regression equation y = a + bx, and the following statistical parameters were calculated by using MATLAB® and QuickCalcs63 software: statistical errors for a (σa) and b (σb), respective t-test parameters (ta and tb), Pearson correlation coefficient between a descriptor and y (R), explained fitted variance (R2yrand), F-ratio (F), and normal confidence levels for parameters ta, tb and F. This way, the interplay between data set size and quality could be rationalized and the importance of variable selection discussed.


Results and Discussion

Data set 1: QSAR modeling of phenol toxicity to ciliate T. pyriformis

Yao et al.59 have explored the complete data set of 153 phenol toxicants by building a MLR model (reported: R= 0.911 and RMSECV = 0.352; calculated in the present work: Q2= 0.805, R2= 0.830, RMSEC = 0.335 and Q= 0.897), and also another MLR model with 131 training samples (reported: R= 0.924 and RMSEC = 0.309; calculated in this work: Q2= 0.827, R2= 0.854, RMSECV = 0.328, Q= 0.910, Q2ext = 0.702, R2ext= 0.696, RMSEP = 0.459 and Rext = 0.835). This set is larger than those commonly used in QSAR studies and, therefore, various statistical tests could be performed. This was the reason to make a rather radical split into 75 and 78 compounds for the training and external validation sets (51% out), based on HCA analysis (dendrogram not shown). The LOO (Q2= 0.773, R2= 0.830, RMSECV = 0.403, RMSEC = 0.363, Q= 0.880 and R= 0.911) and external validation statistics (Q2ext = 0.824, R2ext= 0.824, RMSEP = 0.313 and Rext = 0.911) obtained were satisfactory. To test the self-consistency of the data split, the training and external validation sets were exchanged and a second model with 78 training samples was obtained. Its LOO (Q2= 0.780, R2=0.838, RMSECV =0.349, RMSEC =0.313,Q=0.884and R= 0.915) and external statistics (Q2ext = 0.817, R2ext= 0.817, RMSEP = 0.362 and Rext = 0.908), were comparable to that of the first model. Results from other validations of the real MLR model are shown in Table 3, Figures 1-3 and Supplementary Information (Tables T5-T9 and Figures F1 and F2).









Among the validations performed for the real model (Table 2), the single LNO statistics shows an extraordinary behavior, with critical N= 37 (49% out), because the values of Q2LNO stay high (Figure 1) and do not oscillate significantly around the average value (Table T5) even at high N. Multiple LNO shows slow but continuous decrease of average Q2LNO and irregular increase of the respective standard deviations along N, so that up to N= 17 (23% out) two standard deviations (±σ) are not greater than 0.1. (Table T5). In other words, the training set with 75 training toxicants is rather stable, robust to exclusion of large blocks (between 17 and 37 inhibitors), and the data split applied is effective.

Three bootstrap schemes for 10 and 25 runs were applied (Tables T6 and T7) to form training sets: by random selection of 75 toxicants from the complete data set, from HCA clusters (10 clusters at the similarity index of 0.60), and from PCA groups (plot not shown). In fact, three PCA groups were detected in the scores plot, based on three distinct values of descriptor Nhdon (see Table T1). Classes of ywere not applied since no gaps in the statistical distribution of yhad been noticed. The graphical presentation of the results (Figure 2) shows that the data points are well concentrated along a R2-Q2 diagonal, with negligible dispersion in the region defined by R2< 0.85 and Q2< 0.80. The real model (the black point) is placed in the middle of the bootstrap points. Careful comparison of the three bootstrap schemes indicates that HCA-based bootstrapping has certain advantages over the other two schemes. It is less dispersed and more symmetrically distributed around the real model. This would be expected, since each bootstrap training set originating from the HCA contains toxicants that represent well the complete data set in terms of molecular structure and descriptors.

The real MLR model shows excellent performance in y-randomization with 10 and 25 runs (Tables T8 and T9). There are no randomized models in the proximity of the real model in the Q2-R2 plot (Figure 3) since they are all concentrated at Q2< 0 and R2< 0.2. A significantly larger number of randomization runs should be applied to get some randomized models approaching the real model. This example illustrates how many randomized runs are necessary to detect a model free of chance correlation: the choice of 10 or even 25 runs seems reasonable, which agrees with the method of Eriksson and Wold.53 When the Q2-rand R2-rplots are analyzed (Figures F1 and F2), it can be seen that the randomized models are placed around small values of rso that the intercepts of the linear regressions (1) and (2) are very small (aQ < -0.15 and aR < 0.003, Table 3).

All the validations for the real MLR model confirm the self-consistency, robustness and good prediction power of the model, its stability to resamplings and the absence of chance correlation. The primary reason for this is the number of compounds. One out of five descriptors (the number of hydrogen bond donors, Nhdon, Table T1) shows degeneracy, i.e., it has only three distinct integer values, but it did not affect the model's performance noticeably in this case.

Data set 2: QGSAR modeling of fungal resistance

(P. digitatum) to demethylation inhibitors

Kiralj and Ferreira39 have used five latent variables to model the complete data set of 86 samples using PLS (96.8% variance, Q2= 0.851, R2= 0.874, RMSECV = 0.286, RMSEC = 0.271, Q= 0.922 and R= 0.935), and also for the data split with 56 training samples when building the auxiliary PLS model (97.1% variance, Q2= 0.841, R2= 0.881, RMSECV = 0.305, RMSEC = 0.279, Q= 0.917, R= 0.939, Q2ext = 0.844, R2ext= 0.843, RMSEP = 0.272 and Rext = 0.935). The split (35% out) was done based on six HCA clusters at a similarity index of 0.65. In this work, the model for 56 training samples was considered as the real model and it was further validated by bootstrapping. Results from validations for data set 2 are shown in Table 4 and in the Supplementary Information (Tables T10-T15 and Figures F3-F7).

The single and multiple LNO statistics (Table 4, Table T10 and Figure F3) show that the critical Nis 10 (leave-18%-out) and 17 (leave-30%-out), respectively. The variations of Q2LNO in single LNO are uniform and less than 0.1, and the same is valid for two standard deviations in multiple LNO. Therefore, the real model is robust to exclusion of blocks in the range of 10 - 17 samples, which is reasonable for a data set of this size.40

Four bootstrap schemes were applied (Tables T11 and T12) to randomly select 56 training samples from the following sets: 1) the complete data set; 2) the six HCA clusters; 3) three classes of y, based on its statistical distribution (low, moderate and high fungal activity referred to intervals 4.55-5.75, 5.76-6.75, and 6.76-7.70, respectively); and 4) three MDR (multidrug resistance) classes of fungal strains with respect to pesticides (resistant, moderately resistant and susceptible).39 The graphical presentation of the bootstrap models when compared to the real model (Figure F4), is very similar to that from data set 1. A new observation can be pointed out here, that the bootstrap models become more scattered as the number of runs increases, which is statistically expected. Resamplings based on HCA and ydistribution seems to be the most adequate bootstrap schemes, because they are more compact and better distributed around the real model than those for the other two bootstrap schemes.

Results from 10 and 25 y-randomization runs (Tables T13 and T14) were analyzed numerically (Table T15) and graphically by means of the Q2-R2 plot (Figure F5), and Q2yrand -rand R2yrand-rplots (Figures F6 and F7). The results are very similar to those from data set 1, leading to the same conclusion that the explained variance by the real PLS model is not due to chance correlation. In cases like this one, the results from a huge number of randomization runs18 would concentrate mainly in the region of these 10 or 25 randomizations, confirming the conclusions that the real PLS model is statistically reliable.

Data set 3: QSPR modeling of carbonyl oxygen chemical shift in substituted benzaldehydes

Kiralj and Ferreira5 have used two latent variables to build the real PLS model for the complete data set of 50 benzaldehydes (92.3% variance, Q2= 0.895, R2= 0.915, RMSECV = 9.10 ppm, RMSEC = 8.43 ppm, Q= 0.946 and R= 0.957), and also for the data split with 40 training samples to construct an auxiliary PLS model (92.6% variance, Q2= 0.842, R2= 0.911, RMSECV = 9.59 ppm, RMSEC = 8.83 ppm, Q= 0.942, R= 0.954, Q2ext = 0.937, R2ext= 0.936, RMSEP = 6.79 ppm and Rext = 0.970). This split (20% out) was done based on five HCA clusters with a similarity index of 0.70. In this work, these real and auxiliary models were further validated and the validation results were analyzed in detail.

The LNO statistics5 (Table 4, Table T16 and Figure F8) show that Q2 stays high and stable up to the value of N= 10 (leave-20%-out) in single LNO and N= 19 (leave38%-out) in multiple LNO, after which it starts to decrease and oscillate significantly. This is a very satisfactory result for a modest data set of fifty samples.

Three resampling schemes were applied in bootstrap validation (Tables T16 and T17) to exclude10 from 50 samples randomly from the following sets: 1) the complete data set; 2) the five HCA clusters; and 3) three classes of y, based on statistical distribution of y(low, moderate and high chemical shifts).5 Figure 4 shows the Q2-R2plot taking into account all the resampling schemes for 10 and 25 runs. Unlike the analogues for data sets 1 and 2, a different type of dispersion of the bootstrap models is observed in the plot. In fact, the data points are not well distributed along a diagonal direction but are substantially scatteredinthe orthogonal direction, along the whole range of values of Q2and R2. The auxiliary model (the green point) is not in the centre of all bootstrap models, whilst the real model (the black point) is out of the main trend due to the different size of the training set. On the other hand, the plot still shows small variations in Q2and R2, and no qualitative changes in this scenario are expected when increasing the number of bootstrap runs. Differences with respect to the analogue plots from data sets 1 and 2 may be a cumulative effect of diverse factors, as for example, smaller training set size, different ranges and statistical distribution profile of y, and the nature of y(chemical shifts against negative logarithm of molar concentrations). The common point in the three data sets is the performance of HCA-bootstrapping over the other schemes.

Results from 10 and 25 y-randomization runs (Tables T18 and T19) were analyzed numerically (Table T20) and graphically (Figures F9 - F11). The Q2-R2plot (Figure F9) shows no chance correlation. It is likely that the results from a larger number of randomization runs would be concentrated in the region already defined. The Q2-rand R2-rplots (Figures F10 and F11) also show the absence of chance correlation, which is reconfirmed by numerical approaches in Table T20.

The real model has somewhat weaker statistics than the previous one, but its statistics is still acceptable and in accordance to the data set size and recommendations for the five validation procedures.

Data set 4: QSAR modeling of mouse cyclooxigenase-2 inhibition by imidazoles

Hansch et al.55 have built a MLR model for this small data set containing 15 inhibitors, with acceptable LOO statistics (reported: Q2= 0.798, R2= 0.886 and RMSEC = 0.212; calculated in this work: RMSECV = 0.241, Q= 0.895 and R= 0.941). Besides the real model, an auxiliary model was constructed in this work, by considering inhibitors 4and 10(Table T4 in Supporting Information) as external validation samples. This data split, reasonable for such a small data set (13% out), was performed according to the HCA analysis which resulted in one large and one small cluster with 13 and 2 samples, respectively (dendrogram not shown). The two inhibitors selected were from the large cluster. The auxiliary model shows improved LOO statistics with respect to that of the real model (Q2= 0.821, R2 = 0.911, RMSECV = 0.239, RMSEC = 0.202, Q= 0.908 and R= 0.954). The external samples 4and 10are reasonably well predicted with calculated activities 6.52 and 6.49, respectively, compared to their experimental values 6.72 and 6.19, respectively, which means less than 5% error. The downside of this external validation is that it is not justified to calculate RMSEP, Q2ext or other statistical parameters for a set of two inhibitors.

Both single and multiple LNO statistics (Table 4, Table T21 and Figure F12) show that the model is stable for N= 1 -3 (leave-20%-out). The values of Q2LNO in this interval of Noscillate within the limit of 0.1 (see Figure F12). These results indicate that the model is reasonably robust in spite of the data size.

The bootstrap tests for the MLR model were performed by eliminating two inhibitors by random selection from the complete data set and from the large HCA cluster. Other resampling schemes were not applied in this example due to the data size and its ydistribution. The results obtained (Tables T22 and T23), when compared to that from the auxiliary model of the same data size (Figure 5), show rather unusual behavior. The data points in the R2-Q2 plot are not well distributed along some diagonal direction as in the analyses for data sets 1 and 2, but are rather dispersed in the orthogonal direction, especially at lower values of Q2around 0.6. This suggests thatQ2 would easily overcome the limit of 0.65 with increasing the number of resamplings. The auxiliary model (the green point), which should be closer to the bootstrap models than the real model, is placed too high with respect to the centroids of these models. The plot shows an unusual, "asymmetric" aspect unlike the analogue plots for data sets 1 - 3 (Figures 2 - 4), due to the pronounced differences between Q2and R2(see Tables T22 and T23).



Results from 10 and 25 y-randomization runs (Tables T24 and T25), when presented graphically (Figure 6), show a large dispersion of the data points. The points are placed along a R2-Q2 diagonal and also spread around it. Furthermore, it is evident that a slight increase in the number of y-randomization runs would result in falsely good models that would be very close to the real model, meaning that this final model is based on chance correlation, and thus, is invalid. Compared to the previous y-randomization plots for data sets 1 - 3 (Figures 3, F5 and F9), a systematic increase of the dispersion of the data points can be observed. This trend is followed by the appearance of highly negative values of Q2 for the randomized models: about -0.2, -0.3, -0.6 and -1.1 for data sets 1, 2, 3, and 4, respectively.



To be more rigorous in this validation, further calculations were carried out, as shown in Table 5 and Figures F13 and F14. The smallest distance between the real model and randomized models is significant in terms of confidence levels of the normal distribution (<0.0001), both in Q2 and R2yrand. The situation is even more critical when all distances are expressed in terms of the confidence level, since more than 40% of the randomized models are not statistically distinguished from the real model in Q2 units, and much less but still noticeable in R2 units (for more than 10 runs). These tendencies seem to be more obvious when increasing the number of randomization runs. However, when the linear regression equations (1) and (2) are obtained, the intercepts do not approach the limits (aQ < 0.05 and aR < 0.3) and the validation seems apparently acceptable. It is obvious that this inspection is not sufficient by itself to detect chance correlation in the model and so, one has to investigate the spread of the data points in the region around the intercept in both Q2-rand R2-rplots. If this spread is pronounced in the way that several data points are situated above the limits for intercepts, then the chance correlation is identified, which is exactly the situation in the present plots (Figures F13 and F14). In fact, the MLR model published by Hansch et al.60 has certainly failed in y-randomization, confirming that small data sets seriously tend to incorporate chance correlation. The other possible reason, although of less impact, is data degeneration (redundancy) in columns and rows of the data matrix X(see Table T4 in Supporting Information). The cumulative results of y-randomization and the other validations show that the real MLR model is not statistically reliable.

Data quality versus data set size: data subset 3

The comparative discussion of models'performance in previous sections was based on data set size. In this section, the data quality, i.e., relationships between descriptors Xand the dependent variable y, are inspected in two ways. First, data sets 1, 2 and 3 were reduced to small subsets of 15 samples, to inspect how much the data size and its quality affect the performance of the models. Second, to rationalize these results and emphasize the importance of variable selection, correlations between Xand yfor all data sets and their subsets were inspected by calculating various statistical parameters.

The largest set, data set 1, was reduced to 15 samples (denominated as subset 1, containing the toxicants 4, 17, 20, 32, 37, 51, 53, 62, 70, 71, 113, 133, 141, 143and 149), but its poor performance in LOO (Q2 = 0.073) did not justify any further validation. All attempts to reduce data set 2 failed because certain descriptors which were not indicator variables in the original data, became degenerate (i.e., were reduced to two distinct values) due to the loss of information. Among the three data sets tested for size reduction, only data set 3 could be reduced successfully to a subset (denominated as subset 3) and validated in the same way as data set 4. All analyses for this data subset can be found in the Supplementary Information (Tables T26 - T32 and Figures F15 - F19).

The real PLS model for data subset 3 still has acceptable LOO statistics when two latent variables are used (91.8% variance, Q2 = 0.779, R2 = 0.897, RMSECV = 13.6 ppm, RMSEC = 10.4 ppm, Q= 0.892 and R= 0.947), which is somewhat inferior to that of the real model for data set 3 (the differences are more obvious in RMSEC and RMSECV than in the correlation coefficients), but is comparable to that of the real model for data set 4. The same number of latent variables is used for the auxiliary model (91.8% variance, Q2 = 0.738, R2 = 0.889, RMSECV = 15.0 ppm, RMSEC = 11.1 ppm, Q= 0.874 and R= 0.943), which is obtained when benzaldehydes 7and 37are treated as external samples. These samples were selected from an HCA histogram (not shown) and, not surprisingly, the predictions are satisfactory. The experimental chemical shifts are 570.1 and 520.0 ppm for 7and 37, respectively, and predicted shifts are 564.3 and 513.9 ppm, respectively, which amounts to less than 7% error. When analyzing the performance of the real model in LNO, bootstrapping and y-randomization, it is obvious that the model is much inferior to that from data set 3, due to the difference in the number of samples. However, when compared to that from data set 4, the model for subset 3 is somewhat better in single and multiple LNO (critical N= 4 or leave-27%-out versus N= 3), the same atypical behavior is also observed in the Q2 -R2 space for bootstrapping, and the model is also based on chance correlation. The model's failure in most of the numerical and graphical analyses for y-randomization is even more obvious than that of the model for data set 4. Even though small data sets allow the construction of models with reasonable LOO, LNO and external validation statistics (as has been shown in this section), this does not imply reasonable performance in bootstrapping and y-randomization. Concluding, small data sets of about 15 samples are not suitable for a QSAR or QSPR study.

Effects of sample randomization to leave-N-out crossvalidation andy-randomization

It has been emphasized in this work that sample scrambling is an important step prior to model validation, by which the randomness of a data set is enhanced. The effects of this randomization can be found in the Supplementary Information (Figures F3, F8, F12 and F15, and two special sections containing Tables T33 - T38 and Figures F20 - F26 with discussion), where the results from LNO and y-randomization are presented for data sets 1 - 4 and subset 3, using the original descriptor blocks, X. The reader should keep in mind that data sets with significant redundancy in samples are not of random character, and consequently, a regression model built on such data will have falsely good performance in validation procedures,50 even though sample randomization has been performed.

Data quality versus data set size: statistics ofx-yrelationships

There are 90 relationships between descriptors and dependent variables (i.e., x-yrelationships) for all data sets and subsets studied in this article, presented as linear regressions y= a+ bxand analyzed viacorrelation coefficients, t-test and F-test parameters (Figures F20 F25 and Table 33 in Supplementary Information). Careful analysis of these statistics may aid in explaining the behavior of QSAR or QSPR models in all validations performed. First, models built for various subsets of the same data set deteriorate as the number of samples decreases, which is a consequence of the fact that x-yrelationships tend to become less statistically significant when there are fewer samples. Second, some statistical parameters are, although not identical, very highly correlated to each other. This is valid for square of Rand R2; F-value (F) and the square of the t-test parameter for b (tb); and the confidence levels for tb (pb) and F-value (p). Third, minimum values of some parameters are not so problem-dependent but may be well related to the statistical significance of x-yrelationships: R> 0.3; R2 > 0.1; F> 5; tb > 2.5, and probably ta > 2.5. However, the exact limits for t-test parameters and F-value in a particular study are extracted from statistical tables, which strongly depend on the number of samples. It is recommended44,49 to use confidence level 0.05 or 0.01 for t-test and F-test. In fact, variable selection should provide statistically significant x-yrelationships, so model validation does not have to deal with poor and individual problematic x-yrelationships but with the relationship between the descriptor block X as a whole and y.

In the light of these facts, it is possible to understand the scatterplots for the data sets studied (Figures F20 - F24 and Table 33). It is rather clear that data set 1 contains only one statistically significant descriptor (LogKow), whilst another one (Nhdon) behaves as a poorly distributed degenerate variable (there are 126, 24 and 3 samples with values Nhdon = 1, 2 and 3, respectively). The other three descriptors (pKa, ELUMO and EHOMO) are characterized by very high dispersion in their scatterplots and, consequently, the x-yrelationships are not statistically significant (see bold descriptors and values in Table T33). In other words, the models built for data set 1 and its subsets are based on at least three statistically not significant x-yrelationships, meaning that the selected variables were not so significant. The large number of samples has simply masked their deficiencies so that they could not be detected by the five validation procedures and, consequently, the model for set 1 showed excellent performance in all tests. However, successful reduction of data set 1 to 15 samples was not possible. Therefore, data set 1 is an example of a large and falsely good set for building QSAR models.

Although x-yrelationships for data set 2, of moderate size, were all statistically significant, it was also not possible to reduce the data from 86 to 15 samples. It probably contains some non-linear relationships, but this is questioned by the following items: a) a few data at high values for certain descriptors are not sufficient to confirm non-linearities; b) how to interpret the non-linearities in terms of fungal genome; and c) how to form subsets since three genome descriptors have only three distinct values.

Another set of moderate size, data set 3, has the most adequate scatterograms, and is based on statistically significant x-yrelationships. When it is reduced to 15 or less samples, only one or two x-yrelationships become partially insignificant in parameters for a. Data set 4, besides being small, is characterized by three x-yrelationships from which only one is statistically significant (ClogP), another is insignificant (MgVol) and the third is not sufficiently significant (B1X.2). This set is a typical example of a small and falsely good data set, which, in spite of this fact, showed good performance in some validation tests. This is another example indicating the need to couple variable selection based on statistical tests for x -y relationships and model validation.

A simple way to verify self-consistency of a data is to see if the positive or negative contribution of a descriptor to yremains the same during the data split and building regression models. This contribution can be seen from the x-yrelationship, using the signs of correlation coefficient Ror regression coefficient band the respective regression coefficient from the real model. For self-consistent data, the sign of Ror bfor a descriptor should be the same in the complete data set and all of its subsets, and also equal to the corresponding regression coefficient from the real model. In this sense, data set 1 showed being inconsistent both in data split and model building (in 3 out of 5 descriptors), data set 2 only in modeling (due to non-linearities, as seen in 2 out of 8 descriptors), and data sets 3 and 4 and subset 3 were self-consistent in all descriptors (see Table T39). This self-consistency is important from the statistical point of view and also for model interpretation and mechanism of action.



Four QSAR and QSPR data sets from the literature were used to rebuild published or build statistically related regression models. These models were validated by means of leave-one-out crossvalidation, leave-N-out crossvalidation, external validation, y-randomization and boostrappings. The five validation tests have shown that the size of the data sets, more precisely, of the training sets, is the crucial factor determining model performance. The larger the data set, the better is its validation statistics. Very small data sets suffer from several deficiencies: impossibility of making validations (data split is not possible or is not sufficient), failure and atypical behavior in validations (chance correlation, lack of robustness in resampling and crossvalidations). Obtaining satisfactory statistics in leave-one-out crossvalidation and fitting and even in external validation is not a guarantee for good model performance in other validations procedures. Validation has to be carried out carefully, with detailed graphical and numerical analyses of the results obtained. The critical Nin LNO at which Q2 is still stable and high, can be determined by applying the limit of 0.1 for oscillations in Q2, defined as the variation range in single LNO and two standard deviations in multiple LNO. It has been demonstrated that it is not necessary to perform a large number of y-randomization or bootstrap runs to distinguish acceptable from non-acceptable regression models. Comparing various bootstrap schemes, it has been noted for data sets 1 - 3 that resampling based on clusters from hierarchical cluster analysis, and perhaps on some other schemes, gives somewhat more reliable and reasonable results than that relying only on randomization of the complete data set. Data quality in terms of descriptor -yrelationships is the second important factor which influences model performance. A reliable model has to be constructed from statistically significant x-yrelationships, emphasizing the important role of variable selection. Statistically insignificant x-yrelationships in large data sets can be masked by data size, resulting in models with excellent performance in all validation procedures, but at the end the QSAR or QSPR models obtained are false.



The authors acknowledge The State of São Paulo Funding Agency (FAPESP) for financial support and Dr. Carol H. Collins for English revision.


Supplementary Information

Data sets used to build regression models, results of validations of the models, graphical and numerical analyses of these results, and additional tests for chance correlation and statistical significance of descriptors used (in total 39 tables, 32 figures, and discussion on randomization effects), are available online at



1. Ferreira, M. M. C.; J. Braz. Chem. Soc.2002, 13, 742.         [ Links ]

2. Bruni, A. T.; Ferreira, M. M. C.; J. Chemom.2002, 16, 510.         [ Links ]

3. Kiralj, R.; Ferreira, M. M. C.; J. Mol. Graphics Modell.2003, 21, 435.         [ Links ]

4. Kiralj, R.; Takahata, Y.; Struct. Chem. 2006, 17, 525.         [ Links ]

5. Kiralj, R.; Ferreira, M. M. C.; J. Phys. Chem. A2008, 112, 6134.         [ Links ]

6. Ladiswala, A.; Rege, K.; Breneman, C. M.; Cramer, S. M.; Langmuir2003, 19, 8443.         [ Links ]

7. Guntur, S. B.; Narayanan, R.; Khandewal, A.; Bioorg. Med. Chem.2006, 12, 4118.         [ Links ]

8. Wang, Y.; Wang, X. W.; Chen, Y. Y.; Chem. Biol. Drug Des.2006, 68, 166.         [ Links ]

9.Kalikova, K.; Lokajova, J.; Tesarova, E.; J. Sep. Sci.2006, 29, 1476.         [ Links ]

10. Yeatts, J. L.; Baynes, R. E.; Xia, R. E.; Riviere, J. E.; J. Chromatogr., A2008, 1188, 108.         [ Links ]

11. Kiralj R.; Ferreira, M. M. C.; J. Chem. Inf. Comput. Sci.2002, 42, 508.         [ Links ]

12. Kiralj R.; Ferreira, M. M. C.; J. Chem. Inf. Comput. Sci.2003, 43, 787.         [ Links ]

13. Faulon, J.-L.; Brown, W. M..; Martin, S.; J. Comput.-Aided Mol. Des.2005, 19, 637.         [ Links ]

14. Lee, M. J.; Jong, S.; Gade, G.; Poulos, C.; Goldsworthy, G. J.; Insect Biochem. Mol. Biol.2000, 30, 899.         [ Links ]

15. Liang, G. Z.; Li, Z. L.; J. Mol. Graph. Mod.2007, 26, 269.         [ Links ]

16. Yuan, Z.; Wang, Z. X.; Proteins2008, 70, 509.         [ Links ]

17. Yuan, Z.; Bailey, T. L.; Teasdale, R. D.; Proteins2005, 58, 905.         [ Links ]

18. Sturm, M.; Quinten, S.; Huber, C. G.; Kohlbachere, O.; Nucleic Acid Res.2007, 35, 4195.         [ Links ]

19. Sakai, M.; Toyota, K.; Takui, T.; J. Chem. Inf. Model.2006, 46, 1269.         [ Links ]

20. Sundaralingam, M.; Ponnuswamy, P. K.; Biochemistry2004, 43, 16467.         [ Links ]

21. Dubey, A.; Realff, M. J.; Lee, J. H.; Schork, F. J.; Butté, A.; Ollé, B.; AIChE J.2006, 52, 2149.         [ Links ]

22. Han, I. S.; Lee, Y. H.; Ind. Eng. Chem. Res.2006, 45, 670.         [ Links ]

23. Willighagen, E. L.; Wehrens, R.; Buydens, L. M. C.; Crit. Rev. Anal. Chem.2006, 36, 189.         [ Links ]

24. Kiralj, R.; Ferreira, M. M. C.; J. Chemom.2006, 20, 247.         [ Links ]

25. Beebe, K. R.; Pell , R.; Seasholtz, M. B.; Chemometrics: a practical guide. J. Wiley & Sons: New York, NY, 1998.         [ Links ]

26. Martens, H.; Naes, T.; Multivariate Calibration, 2nd ed. J. Wiley & Sons: New York, NY, 1989.         [ Links ]

27. Ferreira, M. M. C.; Antunes, A. M.; Melgo, M. S.; Volpe, P. L. O.; Quim. Nova1999, 22, 724.         [ Links ]

28. Ferreira, M. M. C.; J. Braz. Chem. Soc.2002, 13, 742.         [ Links ]

29. Bhadeshia, H. K. D. H.; ISIJ Int.1999, 39, 966.         [ Links ]

30. Vracko, M.; Curr. Comput.-Aided Drug Des.2005, 1, 73.         [ Links ]

31. Ivanciuc, O. In Reviews in Computational Chemistry, Vol. 23; Lipkowitz, K. B.; T.R. Cundari, T. R., eds.; J. Wiley-VCH: Weinheim, Germany, 2007, ch. 6.         [ Links ]

32. Smilde, A.; Bro, R.; Geladi, P.; Multi-way Analysis with Applications in the Chemical Sciences. J. Wiley & Sons: Chichester, UK, 2004.         [ Links ]

33. Anscombe, F. J.; Am. Stat.1973, 27, 17.         [ Links ]

34. Gray, J. B.; Statistician1989, 38, 97.         [ Links ]

35. Todeschini, R.; Consonni, V.; Mauri, A. Pavan, M.; Anal. Chim. Acta2004, 515, 199.         [ Links ]

36. Polanski, J.; Bak, A.; Gieleciak, R.; Magdziarz, T.; J. Chem. Inf. Model.2006, 46, 2310.         [ Links ]

37. Todeschini, R.; Consoni, V.; Maiocchi, A.; Chemometr. Intell. Lab. Syst.1999, 46, 13.         [ Links ]

38. Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models. OECD Environment Health and Safety Publications Series on Testing and Assessment No. 69. OECD: Paris, 2007. http://www.oecd. org/dataoecd/55/35/38130292.pdf [last access on May 8, 2008]         [ Links ]

39. Kiralj, R.; Ferreira, M. M. C.; QSAR Comb. Sci.2008, 27, 289.         [ Links ]

40. Gramatica, P.; QSAR Comb. Sci.2007, 26, 694.         [ Links ]

41. Brereton, R. G.; Chemometrics: Data Analysis for the Laboratory and Chemical Plant.J. Wiley & Sons: New York, NY, 2003.         [ Links ]

42. Massart, D. L.; Vandeginste, B. G. M.; Buydens, L. M. C.; De Jong, S.; Lewi, P. J.; Smeyers-Verbeke, J.; Handbook of Chemometrics and Qualimetrics, Part A. Data Handling in Science and Technology, vol. 20 A. Elsevier: Amsterdam, 1997.         [ Links ]

43. Snee, R. D.; Technometrics1977, 19, 415.         [ Links ]

44. Tropsha, A.; Gramatica, P.; Gombar, V. K.; QSAR Comb. Sci.2003, 22, 69.         [ Links ]

45. Baumann, K.; Stiefl, N.; J. Comput.-Aided Mol. Des.2004, 18, 549.         [ Links ]

46. Eriksson, L.; Jaworska, J.; Worth, A. P.; Cronin, M. T. D.; McDowell, R. M.; Environ. Health Perspect.2003, 111, 1361.         [ Links ]

47. Khadikar, P. V.; Phadnis, A.; Shrivastava, A.; Bioorg. Med. Chem.2002, 10, 1181.         [ Links ]

48. Leonard, J. T.; Roy, K.; QSAR Comb. Sci.2007, 26, 980.         [ Links ]

49. Golbraikh, A.; Tropsha, A.; J. Mol. Graphics Mod.2002, 20, 269.         [ Links ]

50. Clark, R. D.; Fox, P. C.; J. Comput.-Aided Mol. Des.2004, 18, 563.         [ Links ]

51. Baumann, K.; TrAC, Trends Anal. Chem.2003, 22, 395.         [ Links ]

52. Teófilo R. F.; Martins J. P. A.; Ferreira, M. M. C.; J. Chemometr.2009, 23, 32.         [ Links ]

53. Wold, S.; Eriksson, L. In Chemometric Methods in Molecular Design; van de Waterbeemd, H., ed.; VCH: Weinheim, 1995, p. 309.         [ Links ]

54. Rücker, C.; Rücker, G.; Meringer, M.; J. Chem. Inf. Model.2007, 47, 2345.         [ Links ]

55. Kiralj, R.; Ferreira, M. M. C.; Chemom. Intell. Lab. Syst., in the press.         [ Links ]

56. Wehrens, R.; van der Linden, W. E.; J. Chemom.1997, 11, 157.         [ Links ]

57. Wehrens, R.; Putter, H.; Buydens, L. M. C.; Chemometr. Intell. Lab. Syst.2000, 54, 35.         [ Links ]

58. Aptula, A. O.; Netzeva, T. I.; Valkova, I. V.; Cronin, M. T. D.; Schültz, T. W.; Kuhne, R.; Schürmann, G.; Quant. Struct.-Act. Relat.2002, 21, 12.         [ Links ]

59. Yao, X. J.; Panaye, A.; Doucet, J. P.; Zhang, R. S.; Chen, H. F.; Liu, M. C.; Hu, Z. D.; Fan, B. T.; J. Chem. Inf. Comput. Sci.2004, 44, 1257.         [ Links ]

60. Garg, R.; Kurup, A.; Mekapati, S. B.; Hansch, C.; Chem. Rev.2003, 103, 703.         [ Links ]

61. Pirouette 3.11; Infometrix, Inc., Woodinville, WA, 2003.         [ Links ]

62. Matlab 7.3.0; MathWorks, Inc., Natick, MA, 2006.         [ Links ]

63. QuickCalcs: Online Calculators for Scientists; GraphPad Software, Inc., La Jolla, CA, 2005. quickcalcs/index.cfm, accessed in February 2009.         [ Links ]



Received: November 24, 2008
Web Release Date: May 6, 2009
FAPESP helped in meeting the publication costs of this article.






Supplementary Information



Table T1 - Click to enlarge



Table T2 - Click to enlarge



Table T3 - Click to enlarge



Table T4 - Click to enlarge








Table T5 - Click to enlarge



Table T6 - Click to enlarge



Table T7 - Click to enlarge








Figure F3 - Click to enlarge



Figure F4 - Click to enlarge









Table T10 - Click to enlarge



Table T11 - Click to enlarge



Table T12 - Click to enlarge







Table T15 - Click to enlarge




Figure F8 - Click to enlarge









Table T16 - Click to enlarge



Table T16 - Click to enlarge



Table T17 - Click to enlarge







Table T20 - Click to enlarge










Table T21 - Click to enlarge



Table T22 - Click to enlarge



Table T23 - Click to enlarge


















Table T26 - Click to enlarge



Table T27 - Click to enlarge



Table T28 - Click to enlarge



Table T29 - Click to enlarge







Table T32 - Click to enlarge



The effect of sample randomization can be noticed when a model is validated by means of two LNO modes: a single LNO using the original (not reordered) data set, and a multiple LNO with the reordered (randomized) data. This is illustrated in the LNO plots for data sets 1 (Figure F20), 2 (Figure F3), 3 (Figure F8) and 4 (Figure F12), and subset 3 (Figure F15). Green curves are from single LNO for original data, compared to red curves for multiple LNO. The green curves do not show randomization effect on LNO for data set 1, probably due to relatively large number of samples and certain statistically insignificant x-yrelationships, and also for subset 3. Data sets 2, 3 and 4, on the contrary, show green curves above the red curves from N = 1 to critical N at which Q2LNO is still stable.

The randomization effect on LNO is more obvious when Pearson correlation coefficients between all variables (descriptors and y) and the row index of X (i.e., OrdNum, the ordinal number or position of a sample in a data set), are calculated for the original data, randomized data prior to LNO, new randomized data sets for single and multiple LNO (Tables T33 -T37 and Figure F21). This analysis shows significant correlations (absolute values of correlation coefficients over 0.30 are highlighted) between OrdNum and variables in two cases: a) small to moderate original data sets 2, 3, and 4 and subset 3 (15 -56 samples); and b) small to moderate randomized data sets 3 and 4 and subset 3 (15 - 50 samples). The smaller the data set, the higher is the probability for chance correlation between OrdNum and variables, what results in virtually higher Q2LNO than in reality. By randomizing the original data, and especially by performing multiple LNO with several runs, one may reduce this chance correlation even for small data sets.




Figure F21 - Click to enlarge



Table T33 - Click to enlarge



Table T34 - Click to enlarge



Table T35 - Click to enlarge



Table T36 - Click to enlarge



Table T37 - Click to enlarge



When the same correlation analysis from LNO is performed for 10 + 25 data sets from y-randomization, which were obtained first by reordering (randomizing) the samples in the whole set and a posteriori randomizing only y, similar conclusions can be drawn (Table T38). Larger data sets 1 and 2 (75 and 50 samples) do not show significant correlation coefficients, data set 3 (50 samples) possesses one critical correlation, whilst data set 4 and subset 3 (15 samples) have ten correlation coefficients above 0.30.

When using the original instead of reordered data for 10 y-randomizations, the results show that there is no effect for larger sets 1 (Figure F22) and 2 (Figure F23), but is visible for smaller set 3 (Figure F24), subset 3 (Figure F25) and set 4 (Figure F26). The effect can be noticed as a shift of Q2 and R2 towards higher values relative to the normal y-randomization procedure.










Figure F26 - Click to enlarge


Table T38 - Click to enlarge





Figure F27 - Click to enlarge



Figure F28 - Click to enlarge



Figure F29 - Click to enlarge



Figure F30 - Click to enlarge



Figure F31 - Click to enlarge



Figure F32 - Click to enlarge



Table T39 - Click to enlarge


Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License