a novel approach using pharmacophore ensemble/support vector machine (phe/svm) for prediction of...

A Novel Approach Using Pharmacophore Ensemble/Support VectorMachine (PhE/SVM) for Prediction of hERG Liability

Max K. Leong*

Department of Chemistry, National Dong Hwa UniVersity, Shoufeng, Hualien 97401, Taiwan

ReceiVed September 8, 2006

A novel approach by using a panel of plausible pharmacophore hypothesis candidates to constitute thepharmacophore ensemble (PhE) and subject them to regression by support vector machine (SVM) hasbeen developed for predicting the liability of human ether-a-go-go-related gene (hERG). This PhE/SVMscheme takes into account the protein conformational flexibility while interacting with structurally diverseligands, which is crucial yet often neglected by most of the analogue-based modeling methods. Thirty-nine molecules were carefully selected and cross-examined from the literature data for this study, ofwhich 26 and 13 molecules were deliberately treated as the training set and the test set to generate themodel and to validate the generated model, respectively. The final PhE/SVM model gave rise to anr2

value of 0.97 for observed vs predicted pIC50 values for the training set, aq2 value of 0.89 by the 10-foldcross-validation and anr2 value of 0.94 for the test set. Thus, this PhE/SVM model provides a fast andaccurate tool for predicting liability of hERG and can be utilized to guide medicinal chemistry to avoidmolecules with an inhibition potential of this potassium channel.

Introduction

Drugs that inhibit the human ether-a-go-go-related gene(hERG) are at great risk to lead to a prolongation of the QTinterval or Torsade de Pointes (TdP) in the worst case (1-3).It has become a crucial consideration for drug discovery anddevelopment for the past decade (4, 5). Experiments todetermine the hERG liability for the potential drug molecules,nevertheless, are expensive, time-consuming, and labor-de-mending (6). Computational approaches seem to be betteralternatives. In fact, numerous in silico models have beenproposed to predict hERG blockade activity (7-30), includingthe traditional modeling tools CoMFA (8), pharmacophore (9),quantitative structure-activity relationship (QSAR) (10, 16),HQSAR (12), CoMSiA (14), pharmacophore/QSAR (19), andstructure-based modeling (21, 23).

There are a number of excellent reviews (5, 20, 28), whichdescribe the latest development, and the brief summary can befound in Table 1 of the publication by Song and Clark (26). Inaddition to those traditional modeling methodologies mentionedabove, several new modeling tools have also recently beenintroduced, trying to address the hERG liability from differentapproaches. Cianchetta et al., for example, used pharmacophore-based GRID descriptors to construct a QSAR model (19); Tobitaet al. utilized a support vector machine (SVM) to classify hERGinhibition (22); Song and Clark developed new QSAR modelsby using fragment-based descriptors in conjunction with variousstatistical methods, including an SVM (26); Seierstad andAgrafiotis proposed another QSAR model, in which the regres-sion calculation was done by neural network ensemble (25);Sun published a model by using Bayesian classification (29),and Yoshida and Niwa employed a genetic algorithm to selectsignificant descriptors to generate QSAR models (27).

Despite their impressive performances, there is one commoncharacteristic shared by these proposed in silico models: Theyfail to take into account the protein plasticity, which is not

uncommon especially when protein interacts with structurallydiverse molecules (31). The importance of protein flexibilitycan be further demonstrated by Carlson et al. (32), in which adynamic pharmacophore was developed for HIV-1 integrasebased on the molecular dynamics calculations derived from theprotein-inhibitor cocomplex structure. In fact, Rajamani et al.constructed two homology models of the hERG channel,namely, one for the open state and the other for the partiallyopen state, to account for the protein flexibility (21).

It will be a more realistic approach to construct a proteinconformation ensemble to accommodate the fact that proteinwill adopt different conformations to interact with structurallydiverse ligands provided that the protein or protein-ligandcocomplex structure is available and different protein conforma-tions will yield different pharmacophore models. Little or nodifference among those protein conformations or pharmacophoremodels can be expected, provided that those ligands appear tobe structurally similar. On the other hand, the discrepancy amongdifferent protein conformations or pharmacophore models willbe even more pronounced especially when protein interacts withstructurally highly diverse ligands such as in the case of hERG.

The pharmacophore ensemble (PhE), which consists of apanel of plausible pharmacophore hypotheses, can be con-structed in lieu of a protein conformation ensemble in case theprotein or protein-ligand cocomplex structure is not availableor reliable enough to perform structure-based modeling. Bydoing so, the issue of protein plasticity can be taken intoconsideration without explicit protein structures. Furthermore,it is reasonable to assert that the true pharmacophore model isvery close to at least one of the model candidates and only showslittle deviation, suggesting that a true model can be thecombination of all model candidates with different weights fora given protein-ligand interaction. In other words, differentweights will be given to the corresponding pharmacophoremodel candidate and are governed by energy states.

Upon completion of PhE construction, an appropriate data-mining tool will be employed to construct the relationshipsamong all of the model candidates for a given PhE. In this study,

* To whom correspondence should be addressed.E-mail: [email protected].

217Chem. Res. Toxicol.2007,20, 217-226

10.1021/tx060230c CCC: $37.00 © 2007 American Chemical SocietyPublished on Web 01/30/2007

an SVM, which was developed by Vapnik et al. in 1995 (33)and has been extensively used in a wide range of applications(22, 34-56), will be employed since a recent study by Yao etal. (57) showed that SVM performs no worse than any otherregression/classification tools, such as multiple linear regression(58), partial least-squares regression (59), genetic algorithm (60),and artificial neural networks (61), if no better than the others.The main advantages of SVM over other regression/classifica-tion methods are its dimensional independence, limited numberof freedom, excellent generalization capability, global optimum,and little effort to implement (62).

Computational Methods

Data Collections.To construct quality data for this investiga-tion, published IC50 values for hERG potassium channelblocking activity were selected from the literature and carefullycross-examined for consistency. If there were two or moreavailable biological data for a given compound and in very closerange, the average values were then taken in order to warrantbetter consistency. Molecular structures were cautiously in-spected so that compounds without defined stereochemistry suchas racemates were excluded. All molecules enlisted in this study,their biological data, and references to the literature are listedin Table 1.

Conformation Search.The conformational search calcula-tions of all molecules were carried out by MacroModel(Schrodinger, Portland, OR) since a number of studies (63-65) have demonstrated that the low-mode conformation search(66) together with the GB/SA hydration algorithm implementedin the MacroModel perform better than the Catalyst’s Best andFast conformation generation algorithms. Additionally, thesolvation effect was taken into consideration by using water asthe solvent with constant dielectric constant and the MMFFsforce field was selected in this study. Energy minimization wasdone by truncated-Newton conjugated gradient method (TNCG);mixed Monte Carlo multiple minimum/low mode conforma-tional search was selected because of its speed and efficiencyas compared with any other searching algorithms in general, asdescribed in the MacroModel user’s manual; and the numberof unique structures and energy windows were set to 255 and83.7 kJ/mol (or 20 kcal/mol), respectively, in order to beaccommodated by Catalyst. The generated conformer outputfiles, stored in mae format, were then converted into MDL sdfiles, which can be recognized by Catalyst, by using theSchrodinger’s utility program sdconvert.

Training Set Selection.Good pharmacophore hypotheses canonly be generated from excellent selection of a training set,suggesting that any subtle flaw in choosing compounds toconstruct the training set, especially in the case of redundancy,highly possibly results in overfitted or overtrained models. Morespecifically, the critical factor to construct a perfect trainingset is to let the program “learn” new knowledge from the input.Structurally similar compounds with substantially differentbiological activities, for example, will serve as perfect entriesfor the training set. More detailed selection criteria can be foundat a number of publications (67, 68) and in Catalyst’s usermanual.

Twenty-six compounds were selected to construct the trainingset for automatic pharmacophore generation and regressionbased on compounds’ activities and chemical structures in orderto achieve statistic significance, and the remaining 13 com-pounds were placed in the test set, which was completelyirrelevant to the model generations per se and only served tovalidate those hypotheses generated from the training set. Tables2 and 3 list compounds used in the training set and the test set,respectively, and their corresponding negative logarithm IC50

values, namely, pIC50, since all of the calculations and analyseswithin Catalyst are carried out in a logarithm scale. Anothercritical factor needed to take into consideration for categorizingcompounds into the training set and the test set was the balanceof activity span between these two sets. Consequently, the IC50

values of compounds in the training set ranged from 0.9 to120000 nM, spanning 6 orders of magnitude, while that ofcompounds in the test set ranged from 28 to 240000 nM or 5log units.

Pharmacophore Generation.Hydrogen bond donors, hy-drogen bond acceptors, and hydrophobic, ring aromatic, and/orpositive ionizable chemical features were selected for calcula-tions with different feature combinations and minimum, maxi-mum, and total numbers for each selected chemical feature andtotal features. A variety of combinations of variable weight andvariable tolerance hypothesis generation options were employedto establish the hypothesis diversity. The costs of a generatedhypothesis and the corresponding null hypothesis were retrievedfrom the log file, and the difference between these two valueswas calculated to survey the statistic quality of a hypothesis.All generated hypotheses were then employed to predict thebiological activities of those compounds in the training set by

Table 1. Selected Compounds for This Study, Their IC50 (nM)Values or Average Values if Applicable, and References

molecules IC50 (nM) refs

astemizole 0.9a 8, 13cisapride 7a 8, 9E-4031 8a 8, 76dofetilide 12a 8, 77sertindole 14a 8, 9, 78pimozide 18a 8, 79norastemizole 28a 8, 80haloperidol 28a 8, 9droperidol 32a 8, 81verapamil 143a 8, 9ziprasidone 152a 8, 9risperidone 155a 8, 9domperidone 162a 8, 80halofantrine 173b 8, 9, 80loratadine 174a 8, 82clozapine 191a 8, 83olanzapine 231a 9quinidine 320a 9, 84mesoridazine 320a 9mizolastine 355b 8, 10bepridil 550a 8, 80, 85azimilide 560b 8, 86vesnarinone 1100a 9, 87desipramine 1390a 9, 88mibefradil 1430c 8, 89chlorpromazine 1470b 8, 9, 80ketoconazole 1900a 9, 90alosetron 3200a 9, 29imipramine 3400a 8, 9, 80granisetron 3730a 8, 9, 80cocaine 5754a 8, 91dolasetron 5950a 8, 80amitriptyline 10000a 8, 80diltiazem 17300a 8, 9glibenclamide 74131d 8, 80, 92grepafloxacin 77625b 8, 93sildenafil 100000a 8, 80moxifloxacin 117490b 8, 93nicotine 244800a 9, 29

a In human embryonic kidney (HEK) cells.b In Chinese hamster ovary(CHO) cells.c In African green monkey kidney-derived cell line COS.d Inneuroblastoma cells.

218 Chem. Res. Toxicol., Vol. 20, No. 2, 2007 Leong

the best fit and fast fit algorithms, followed by the evaluationsof the correlation coefficient (r2) and root-mean-square deviation(rmsd), maximum residual, average residual, and standarddeviation of residual between the observed and the predictedpIC50 values.

Pharmacophore Evaluation. Only those hypotheses thatshowed good statistical significance, namely, cost differencesbetween the generated hypothesis and the null hypothesis,r2,rmsd, and maximum residual values, were subject to further

evaluations by those compounds in the test set, which wasconstructed by the remaining 13 compounds. Those statisticparameters, employed to evaluate the quality of a hypothesisin the training set, were served again to examine the performanceof a hypothesis in the test set by the same calculation schemes,namely, best fit and fast fit. Finally, only those pharmacophorehypotheses that functioned excellently in both training sets andtest sets were eligible to construct the PhE.

SVM Calculations. The regression calculations for the PhEwere done by the SVM package LIBSVM (69), which consistsof two modules for regression, namely, svm-train for producingSVM model based on the input data and options and svm-predictfor predicting the new samples using a model previously builtwith svm-train. The predicted pIC50 values generated from thePhE in the training set were used as input for the svm-traincalculations, while the predicted pIC50 values generated fromthe PhE in the test set were used as input for the svm-predictcalculations. The regression modes, namely,ε-SVR andν-SVR,were selected, and kernel type was set to be radial basis function,which is widely used among various kernels due to its simplicityand marked performance (70). To train the SVR, a perl scriptwas written to systemically scan through those associatedparameters, namely, cost C, the width of the RBF kernelγ, ε

in case ofε-SVR, andν in case ofν-SVR. The generated SVMmodels were further validated using a 10-fold cross-validationscheme, which was proven to work better than the widely usedleave-one-out (71).

Results and DiscussionPhE. Tables 2 and 3 list predicted pIC50 values along with

their associated statistical numbers of three pharmacophoremodels, denoted by Hypo A, Hypo B, and Hypo C, selected toconstruct the PhE from all generated pharmacophore hypothesesusing different combinations of chemical features and runtimeconditions for the training set and the test set, respectively. Thesethree models, consisting of the same four chemical features,namely, one hydrophobe, two aromatic rings, and one positiveionizable group, despite the fact that they display differenttopological relationships as shown in Figure 1, were selectedbecause of their consistent and excellent performance in boththe training set and the test set as shown in Tables 2 and 3, andtheir corresponding hypothesis characteristics, including weights,tolerances, and three-dimensional coordinates of chemicalfeatures and interfeature distances, are summarized in Tables4-6, respectively. The distance between the chemical featureshydrophobe and positive ionizable group in Hypo A, forexample, is 5.874 Å, while that increases to 5.891 and 5.899 Åin Hypo B and Hypo C, respectively, as demonstrated in Figure1. The angles centered at positive ionizable group and connect-ing to hydrophobe and two aromatic rings vary from 129.7 and151.6° in Hypo A to 155.8 and 159.2° in Hypo B and 149.5and 165.4° in Hypo C. The lengths between the positiveionizable group and the two aromatic rings show even greaterdifferences, namely, 6.440 and 8.475 Å in Hypo A, 6.189 and7.365 Å in Hypo B, and 6.448 and 7.426 Å in Hypo C. Theangles constructed by the positive ionizable group and aromaticrings provide other evidence of topological discrepancies amongthese three models, namely, 66.1 and 89.0° in Hypo A, 94.1and 114.3° in Hypo B, and 90.3 and 117.3° in Hypo C. Figure2 illustrates the superposition of these three models, and it canbe observed that the relative topological relationships are notonly different but also the absolute coordinates in the space aredifferent.

The statistical significance of a hypothesis can be determinedby cost, which is calculated based on the number of bits required

Table 2. Experimentally Observed pIC50 Values of Compounds inthe Training Set, Corresponding Predicted Values by Hypo A, Hypo

B, and Hypo C, and Associated Statistic Numbers (CorrelationCoefficient) r2, rmsd, Maximum Residual, Average Residual, and

Standard Deviation of Residual

observed Hypo A Hypo B Hypo C

molecules pIC50 pIC50 residual pIC50 residual pIC50 residual

astemizole 9.05 9.00 -0.05 9.0 -0.09 9.64 0.59cisapride 8.19 7.30 -0.89 7.6 -0.55 7.55 -0.63E-4031 8.11 7.57 -0.54 7.7 -0.44 7.57 -0.54dofetilide 7.91 7.18 -0.73 7.5 -0.46 7.30 -0.61sertindole 7.85 7.34 -0.52 8.3 0.45 7.92 0.07pimozide 7.74 7.42 -0.32 7.7 -0.05 7.85 0.11haloperidol 7.55 7.28 -0.28 7.6 0.09 7.57 0.02droperidol 7.49 7.51 0.02 7.6 0.15 7.51 0.02verapamil 6.84 6.46 -0.39 7.3 0.44 7.01 0.16domperidone 6.79 6.89 0.10 7.6 0.85 7.00 0.21halofantrine 6.76 6.74 -0.02 7.1 0.31 6.82 0.06loratadine 6.77 5.77 -1.00 5.0 -1.75 7.02 0.25mizolastine 6.46 6.46 0.00 6.5 0.01 6.44-0.01bepridil 6.26 5.82 -0.44 6.4 0.14 6.28 0.02azimilide 6.25 5.92 -0.33 5.1 -1.15 5.16 -1.09mibefradil 5.84 5.82 -0.02 7.3 1.48 6.89 1.04chlorpromazine 5.83 6.06 0.22 6.1 0.28 5.92 0.09imipramine 5.47 5.77 0.30 5.1 -0.37 5.19 -0.27granisetron 5.43 5.06 -0.37 5.1 -0.33 5.16 -0.27dolasetron 5.23 4.96 -0.27 5.1 -0.12 5.16 -0.06amitriptyline 5.00 5.00 0.00 5.0 0.03 5.09 0.09diltiazem 4.76 4.72 -0.04 5.0 0.28 5.12 0.36glibenclamide 4.13 4.20 0.07 4.0 -0.13 4.36 0.23grepafloxacin 4.11 4.25 0.14 4.8 0.66 4.85 0.74sildenafil 4.00 4.14 0.14 4.0 0.02 4.15 0.15moxifloxacin 3.94 4.30 0.37 4.3 0.41 3.74-0.19

r2 0.95 0.83 0.91RMSD 1.31 1.28 1.27max 1.00 1.75 1.09average 0.29 0.42 0.30SD 0.27 0.44 0.31

Table 3. Experimentally Observed pIC50 Values of Compounds inthe Test Set, Corresponding Predicted Values by Hypo A, Hypo B,

and Hypo C, and Associated Statistic Numbers (CorrelationCoefficient) r2, rmsd, Maximum Residual, Average Residual, and

Standard Deviation of Residual

observed Hypo A Hypo B Hypo C


norastemizole 7.55 7.52 -0.03 7.49 -0.06 7.43 -0.12ziprasidone 6.82 6.82 0.01 7.09 0.27 6.92 0.10risperidone 6.79 6.85 0.07 6.82 0.04 6.80 0.01clozapine 6.72 7.14 0.42 6.64-0.08 6.32 -0.40cocaine 5.24 4.82 -0.42 5.09 -0.15 5.16 -0.08quinidine 6.49 5.49 -1.00 6.85 0.36 6.77 0.27ketoconazole 5.72 5.55 -0.17 5.55 -0.17 6.16 0.44desipramine 5.86 5.77 -0.09 5.10 -0.75 5.16 -0.70mesoridazine 6.49 6.08 -0.42 6.12 -0.38 6.13 -0.36nicotine 3.61 3.05 -0.57 3.68 0.07 3.70 0.09alosetron 5.49 5.00 -0.49 4.62 -0.88 4.80 -0.70olanzapine 6.64 6.85 0.22 6.47-0.17 6.21 -0.43vesnarinone 5.96 5.08 -0.88 5.09 -0.87 5.15 -0.80


Prediction of hERG Liability Chem. Res. Toxicol., Vol. 20, No. 2, 2007219

to completely describe a hypothesis. The larger the costdifference between a hypothesis and its corresponding null

hypothesis, which acts like a hypothesis without features andall molecules in the training set estimated having the meanactivity, the more statistically significant a hypothesis is. If thecost difference is 60 bits or more, there is more than 90% chancethat a hypothesis shows true correlation in the data (72). Thereturned costs of Hypo A, Hypo B, and Hypo C, their costs ofnull hypotheses, and cost differences are presented in Table 7.The smallest cost difference is slightly larger than 60 bits (HypoA), and the biggest one is even larger than 85 bits (Hypo B),suggesting that all of these three hypotheses are qualifiedcandidates to construct the PhE in terms of statistics point ofview since their cost differences are greater than 60 bits.

The predictions by all of the three pharmacophore modelsare, in general, in agreement with observed values for molecules

Figure 1. Generated pharmacophore models (A) Hypo A, (B) HypoB, and (C) Hypo C, consisting of hydrophobic (light blue), ring aromatic(orange), and positive ionizable (red) chemical features. The interfeaturedistances and angles among features, depicted in white, are measuredin Ångstroms and degrees, respectively.

Table 4. Weights, Tolerances, and Three-Dimensional Coordinates ofChemical Features and Interfeature Distances of Pharmacophore

Model Hypo A

hydrophobic posionizable ring aromatic ring aromatic

weights 2.54 2.54 3.31 3.31tolerances 1.75 1.60 1.75 1.60 1.60 1.60X 2.51 2.37 -0.73 -1.40 -3.59 -0.79Y -7.14 -1.28 4.24 3.23 4.16 5.23Z 0.24 0.62 1.78 4.52-1.98 -2.19hydrophobicposionizable 5.9ring aromatic 11.9 6.4

11.9 7.1 3.0ring aromatic 13.0 8.5 4.7 6.9

13.0 7.8 4.1 7.0 3.0

Figure 2. Superposition of three pharmacophore models Hypo A, HypoB, and Hypo C, denoted in red, white, and light blue, respectively.


Model Hypo B


weights 2.63 2.63 2.63 2.63tolerances 1.60 1.60 1.60 1.60 1.60 1.60X 1.41 0.36 0.24 3.22 -1.19 0.67Y -7.34 -1.75 4.42 4.69 3.78 3.98Z 2.71 1.18 1.56 1.63 -3.43 -5.78hydrophobicposionizable 5.9ring aromatic 11.9 6.2

12.2 7.1 3.0ring aromatic 13.0 7.4 5.2 6.8

14.2 9.0 7.4 7.9 3.0


Model Hypo C


weight 2.43 2.43 2.43 2.43tolerances 1.60 1.60 1.60 1.60 1.60 1.60X 2.10 2.14 3.85 4.30 -1.10 -2.58Y -6.38 -0.73 5.91 6.06 4.59 3.04Z -2.18 -0.49 2.37 5.33 1.19 3.30hydrophobicposionizable 5.9ring aromatic 13.2 7.4

14.7 9.2 3.0ring aromatic 11.9 6.4 5.3 7.0

11.9 7.1 7.1 7.8 3.0

Table 7. Costs of Returned Hypotheses and Null Hypotheses and theCost Differences (∆) between Returned and Null Hypotheses for the

Pharmacophore Models Hypo A, Hypo B, and Hypo C

cost Hypo A Hypo B Hypo C

null hypothesis 245.68 245.68 231.36returned hypothesis 182.26 160.15 156.21∆ 63.43 85.53 75.15


in both the training set and the test set, which can be assertedfrom the small deviations between the observed and theprediction pIC50 values as described in Tables 2 and 3 and canbe further supported by the small rmsds, maximum residuals,average residuals, and standard residual deviations. Statistically,these three pharmacophore hypotheses are excellent models topredict the biological activity trend for those molecules in thetraining set since the square of correlation coefficients or thegoodness of fit (r2) between the observed and the predictionvalues are 0.95, 0.83, and 0.91 for Hypo A, Hypo B, and Hypo

C, respectively, as shown in Table 2, which can be furtherconfirmed by inspecting the scatter plot of observed vs thepredicted pIC50 values as illustrated in Figure 3. Similar assertioncan be also applied to the test set as shown in Table 3 and Figure4.

The maximum prediction error of Hypo A in the training setwas 1.00, which maintained the approximately same level forHypo C with a value of 1.09 and slightly increased to 1.75 forHypo B. The maximum residuals in the training set generatedby Hypo A and Hypo B resulted from the prediction ofloratadine, whose residual was only 0.25 by Hypo C, neverthe-less. The prediction error of azimilide, on the other hand, wasonly 0.33 by Hypo A; yet, that was the worst prediction byHypo C with a deviation of 1.09. Conversely, mizolastine wasperfectly predicted by Hypo A, Hypo B, and Hypo C with onlyerrors of 0.00, 0.01, and 0.01, respectively. When applied, thesethree models to astemizole, for example, Hypo A and Hypo B,precisely predicted the activity with the residuals of 0.05 and0.09, respectively, and Hypo C showed more prediction devia-tion with an error of 0.59. However, all of these models adopteddifferent conformations to exert the biological activities asillustrated in parts A-C of Figure 4, and this discrepancybecomes more pronounced by the overlay of these threeconformations as depicted in part D of Figure 4. On the basisof the facts mentioned above, it clearly demonstrates the needto construct a PhE in order to address the conformationvariations.

Parametersr2, rmsd, maximum residual, average residual, andresidual standard deviation in the training set suggest that HypoA is the best model and Hypo B is the worst. Such observationis opposite to the result calculated from the cost differences inthe training set as listed in Table 7, which suggests that Hypo

Figure 3. Observed pIC50 vs the pIC50 predicted by Hypo A, Hypo B,Hypo C, and SVM model for those molecules in the training set andtheir corresponding linear regression lines.

Figure 4. Pharmacophore models (A) Hypo A, (B) Hypo B, and (C) Hypo C fitted to astemizole and (D) overlay of these three models, which arecolor-coded by red, white, and light blue, respectively. The chemical features are denoted in Figure 1.


B is the best performer and Hypo A is the worst. In addition,it can be observed from Table 2 that these three pharmacophoremodels unanimously predicted some compounds in the trainingset perfectly, such as mizolastine as discussed previously, yetgave rise to some relatively large prediction errors in some othercases, implying that no single hypothesis model can flawlesslypredict all molecules in the training set. As a result, a bettersolution can be achieved by utilizing various combinations ofthese hypotheses in the PhE.

These three hypotheses in the PhE also show excellentcorrelations between the observed and the predicted pIC50 forthose molecules in the test set as shown in Table 3 and Figure5. In fact, they show very similar performance in both trainingset and test set by comparing theirr2 values (Tables 2 and 3 aswell as Figures 3 and 4), suggesting that these pharmacophoremodels were well-trained or no overtraining effect was observed,which usually results in the substantial correlation coefficientdifference between the training set and the test set. Conversely,the r2 parameter calculated by Hypo B slightly increases from0.83 in the training set to 0.88 in the test set, suggesting thatHypo B performed better in the test set than in the training set.The rmsd errors calculated for those molecules in the trainingset by Hypo A, Hypo B, and Hypo C increase marginally from1.31, 1.28, and 1.27 to 1.78, 1.67, and 1.77 in the test set,respectively, indicating that these three models performed betterin the training set. Hypo A yielded the same maximum residualsin the training set and the test set, while the maximum residualsobtained by Hypo B and Hypo C decreased from the trainingset to the test set, especially Hypo B, which showed dramaticreduction from 1.75 in the training set to 0.88 in the test set.Average residual and standard deviation of residual exhibitsimilar levels between the training set and the test set by thesethree candidate hypotheses. Moreover, risperidone, for example,is perfectly predicted by Hypo A, Hypo B, and Hypo C withresiduals of 0.07, 0.04, and 0.01, respectively. Quinidine,oppositely, is the worst predicted molecule by Hypo A with anerror of 1.00, which is merely 0.36 and 0.27 by Hypo B andHypo C, respectively. The variation of prediction errorsdescribed above demonstrates the need for the construction ofPhE using various hypothesis models.

The excellent performance of Hypo A, Hypo B, and Hypo Cin the training set and the test set can be plausibly attributed to(i) valid biological data, since only compounds demonstratingconsistent assay data were selected and any inaccurate biologicalactivity may give rise to false models, resulting in especiallyfaulty predictions for the test set; (ii) defined chemical structures,which provide clear structure information to the pharmacophoregenerations and eliminate ambiguity during calculations; (iii)better conformation generations, resulting from better conforma-tion search algorithm as well as solvation effect; and (iv) perfectselection of training compounds, providing statistically mean-ingful samples to yield valid models.

SVM. Table 8 summarizes the optimal conditions for runningSVM, which were chosen on the basis of the prediction resultsof those molecules in the training set and cross-validation asgiven in Table 9. It can be observed that the SVM modelpredicted those molecules in the training set better than all ofthose individual hypotheses in the PhE that can be furtherdemonstrated by the scatter plot of observed vs the predictedpIC50 values as illustrated in Figure 3, in which those pointsobtained from the SVM model are generally closer to the

Figure 5. Observed pIC50 vs the pIC50 predicted by Hypo A, Hypo B,Hypo C, and SVM model for those molecules in the test set and theircorresponding linear regression lines.

Table 8. Optimal Runtime Parameters for the Final SVM Model

parameter value

SVM type ε-SVRkernel radial basis functionγ 0.015625ε 0.1termination tolerance 0.004ν 0.605905

Table 9. Experimentally Observed pIC50 Values of Compounds inthe Training Set, Corresponding Predicted Values by SVM Model,

Min Model and Avg Model, Associated Statistic Numbers(Correlation Coefficient) r2, rmsd, Maximum Residual, Average

Residual, and Standard Deviation of Residual, and Cross-ValidationCoefficient q2

observed SVM model min model avg model


astemizole 9.05 9.14 0.10 9.64 0.59 9.11 0.06cisapride 8.19 7.65 -0.54 7.64 -0.55 7.47 -0.71E-4031 8.11 7.85 -0.26 7.68 -0.44 7.60 -0.51dofetilide 7.91 7.43 -0.49 7.46 -0.46 7.30 -0.61sertindole 7.85 7.75 -0.10 8.30 0.45 7.68 -0.18pimozide 7.74 7.91 0.17 7.85 0.11 7.62-0.12haloperidol 7.55 7.64 0.09 7.64 0.09 7.46-0.09droperidol 7.49 7.78 0.28 7.64 0.15 7.55 0.06verapamil 6.84 6.74 -0.10 7.28 0.44 6.78 -0.07domperidone 6.79 6.89 0.10 7.64 0.85 7.08 0.29halofantrine 6.76 6.86 0.09 7.07 0.31 6.86 0.10loratadine 6.77 6.87 0.10 7.02 0.25 5.42-1.35mizolastine 6.46 6.56 0.10 6.47 0.01 6.46 0.00bepridil 6.26 6.11 -0.15 6.40 0.14 6.09 -0.17azimilide 6.25 5.64 -0.61 5.92 -0.33 5.27 -0.98mibefradil 5.84 6.22 0.37 7.33 1.48 6.25 0.41chlorpromazine 5.83 6.02 0.19 6.11 0.28 6.02 0.19imipramine 5.47 5.57 0.10 5.77 0.30 5.27-0.20granisetron 5.43 5.17 -0.26 5.16 -0.27 5.10 -0.32dolasetron 5.23 5.12 -0.10 5.16 -0.06 5.07 -0.16amitriptyline 5.00 5.09 0.09 5.09 0.09 5.04 0.04diltiazem 4.76 5.00 0.24 5.12 0.36 4.93 0.16glibenclamide 4.13 4.06 -0.07 4.36 0.23 4.16 0.03grepafloxacin 4.11 4.57 0.46 4.85 0.74 4.54 0.42sildenafil 4.00 3.90 -0.10 4.15 0.15 4.10 0.10moxifloxacin 3.94 3.84 -0.10 4.35 0.41 4.04 0.10

r2 0.97 0.91 0.92RMSD 1.27 1.23 1.30max 0.61 1.48 1.35average 0.21 0.37 0.29SD 0.16 0.31 0.32q2 0.89


regression line than those obtained from the Hypo A, Hypo B,and Hypo C. As a result, the maximum residual calculated bythe SVM model significantly declined to 0.61 from 1.00, 1.75and 1.09 by Hypo A, Hypo B, and Hypo C, respectively, andcame from the prediction of azimilide, which deviated from theobserved value by 0.33, 1.15, and 1.09 by Hypo A, Hypo B,and Hypo C, respectively, indicating that the SVM model gavemore weight to Hypo A than to Hypo B and Hypo C forazimilide. Additionally, loratadine was miscalculated by bothHypo A and Hypo B with residuals of 1.00 and 1.75,respectively, and was modestly overestimated by Hypo C witha residual of 0.25, yet was accurately predicted by the SVMmodel with a residual of 0.10, showing a substantial decreaseof the prediction error by the SVM model.

The 10-fold cross-validation of the SVM model yielded thecorrelation coefficientq2 of 0.89 as compared with anr2 of 0.97for the training set as indicated in Table 9. This insignificantdifference between these two parameters confirms that the SVMmodel shows a statistically true relation between the observedand the predicted values and that it is highly possible that thisSVM model is an authentic model.

It may be argued that the final model can be selected fromthe best fit model, which is defined as the model with the highestpotency or the minimum biological activity among all predic-tions calculated by all candidates in the ensemble for any givenmolecule, or the minimum model, whose prediction results arelisted in Table 9. It seems that the minimum model showedmixed results since the minimum model has anr2 value of 0.91,for example, obtained from the training set as compared withvalues of 0.95, 0.83, and 0.91 by Hypo A, Hypo B, and HypoC, respectively, suggesting that the minimum model performedas good as Hypo C, yet better than Hypo B and worse thanHypo A. However, when compared with the SVM model (r2 )0.97), the minimum model was clearly outperformed by theSVM model.

Because the minimum model is taken from the minimumbiological activity for any given molecule, it can be expectedthat the maximum residual of the minimum model should fallin the range of the worst and the best ones. In fact, that of theminimum model (1.48) was smaller than that of Hypo B (1.75)and larger than that of Hypo A (1.00) and Hypo C (1.09).However, the predictions from the SVM model deviated fromthe observed values by no more than 0.61, strongly confirmingthat the predominance of SVM model to the minimum modelin terms of the maximum residual. In general, all of the statisticalnumbers, listed in Table 9, suggest that the SVM model issuperior to the minimum model for the performance of thosemolecules in the training set except rmsd, in which the minimummodel showed slight improvement (1.23 vs 1.27).

Similarly, it can be assumed that the final predicted valuescan be obtained from the average value of all predictionscalculated by those hypotheses in the PhE for any givenmolecule to yield the consensus model or the average model.Table 9 lists the prediction results of those molecules in thetraining set calculated based on the average model. It can beseen that the average model shows better performance than thatof the minimum model in terms ofr2, maximum residual, andaverage residual and slightly worse in rmsd and residual standarddeviation. The largest prediction deviation of those moleculesin the training set by the average model comes from loratadinewith an error of 1.35, which is merely 0.10 and 0.25 by theSVM model and the minimum model, respectively. On the otherhand, mibefradil was worst, predicted by the minimum modelwith a residual of 1.48, and was only 0.37 and 0.41 by the SVM

model and the average model, respectively. Conversely, asmentioned above, the prediction of azimilide deviated from theobserved value by 0.61, 0.33, and 0.98 by the SVM model, theminimum model, and the average model, respectively, whichis the largest deviation by the SVM model. On the basis of thefact mentioned above, it can be stated that the SVM model isthe best performer for those molecules in the training set amongthese three models.

Table 10 lists the prediction and statistical results of thosemolecules in the test set obtained from the SVM model, theminimum model, and the average model. Among these threeregression models, the SVM performed slightly better than theother two for those molecules in the test set in terms of theparameterr2 (0.94 vs 0.89 and 0.93). Other thanr2, the minimumand average models when applied to the test set maintained thesame performance level as the SVM model (Table 10), and bothmodels even had slightly better average residuals than the SVMmodel (0.29 and 0.32 vs 0.34, respectively).

In general, the prediction results obtained from these threemodels in the test set were slightly worse than in the trainingset, especially the rmsd values. The similar observation can alsobe found for those individual hypotheses in the PhE (Tables 2and 3). However, the three regression models still demonstratedbetter results than those three individual models for thosemolecules in the test set (Tables 3 and 10). More importantly,the SVM model performed better than the other two regressionmodels despite the fact that these two also yielded satisfactoryresults not only in the training set but in the test set, suggestingthat the resultant SVM model can be applied to predict thosemolecules outside of the training set because of the performanceconsistency in both sets. As a result, it can be asserted that theSVM model is a superior model as compared with the others.

In addition to the accurate predictions and excellent perfor-mance as discussed above, this novel PhE/SVM approach alsohas the flexibility advantage as compared with traditionalmodeling methods especially in case there are more biologicalactivities that become available, in which the target proteinadopts different conformations to interact with structurallydistinct ligands, giving rise to discrepant pharmacophorehypotheses from those models in the PhE. The traditionalmodeling methods will demand to rebuild a new model in orderto accommodate the variations of these new structures, or somelevel of deviations from those new molecules can be expected.

Table 10. Published and Predicted hERG pIC50 Values ofCompounds in the Test Set by SVM, Average and Minimum

Models, and Associated Statistic Numbers

observed SVM model min model avg model


norastemizole 7.55 7.76 0.21 7.52-0.03 7.48 -0.07ziprasidone 6.82 6.98 0.16 7.09 0.27 6.93 0.11risperidone 6.79 6.98 0.19 6.85 0.07 6.82 0.04clozapine 6.72 6.90 0.18 7.14 0.42 6.58-0.14cocaine 5.24 5.07 -0.17 5.16 -0.08 5.00 -0.24quinidine 6.49 6.27 -0.22 6.85 0.36 5.93 -0.56ketoconazole 5.72 6.16 0.44 6.16 0.44 5.68-0.04desipramine 5.86 5.55 -0.31 5.77 -0.09 5.26 -0.60mesoridazine 6.49 6.20 -0.29 6.13 -0.36 6.11 -0.39nicotine 3.61 2.76 -0.85 3.70 0.09 3.36 -0.25alosetron 5.49 4.90 -0.59 5.00 -0.49 4.78 -0.72olanzapine 6.64 6.65 0.01 6.85 0.22 6.44-0.20vesnarinone 5.96 5.18 -0.78 5.15 -0.80 5.11 -0.85



As a result, it will require more time to generate the newprediction model, and more importantly, the new model in turncan be expected to show more prediction errors for thosemolecules used in the previous calculations. Conversely, it willonly take a fraction of time to build new pharmacophorehypotheses based on those new structures, which in turn willbe implemented in the PhE to refine the SVM model. The newPhE/SVM model does not have to comprise its predictabilityfor those molecules used in the previous calculations after addingstructural diversity of molecules as in the case of loratadinementioned above.

Overall, this novel PhE/SVM scheme works perfectly topredict the hERG liability, which can be achieved by some othermodeling tools within some limitations. Nevertheless, this PhE/SVM approach is derived from the pharmacophore modeling,which has been proven to be a robust and efficient screeningtool in terms of virtual screening speed (73, 74) since it onlytakes hours to screen millions of compounds on a descentmachine (75) once the database is constructed, which, in turn,only demands one-time conformation generation and can be usedfor a variety of applications. As a result, pharmacophore-basedscreening was suggested to be used as primary screening amongvarious virtual screening tools (68). In other words, this PhE/SVM technique can be employed to virtually screen a compoundlibrary in order to filter out those compounds with potentialhERG problem, resulting in expediting the drug discoveryprocess. Additionally, this PhE/SVM scheme can not only beapplied to the prediction of hERG liability but any ligand-basedmodeling. In fact, the PhE/SVM approach provides a fast,accurate, and versatile way to conduct analogue-based drugdesign.

Conclusion

A novel approach, based on the combination of PhE, whichaddresses the issue of protein conformational flexibility whileinteracting with structurally diverse small molecules, and SVM,which provides robust and fast regression, has been developedto accurately predict the hERG liability values for those 26 and13 compounds in the training set and test sets, respectively, withexcellent predictability and statistical significance. Additionally,this PhE/SVM approach also provides flexibility and swiftnessfor model refining in case new molecules are augmented in thesample panel that will otherwise require model reconstructionslike any traditional modeling approaches. It can be asserted,based on the facts mentioned above, that this PhE/SVMapproach serves as a good model for hERG liability predictionand also provides a valuable tool for analogue-based modelingand drug design. Certainly, it can be expected that more qualitydata will be published in the literature, which in turn can beemployed as sample pool to further verify this generated PhE/SVM model or to improve the model in case some significantdeviations are observed in the hopes of producing a reliableand useful model for prediction of hERG liability.

Acknowledgment. This work was supported by the NationalScience Council, Taiwan. Parts of calculations were performedat the National Center for High-Performance Computing,Taiwan. I thank Dr. G. H. Hakimelahi for reading the manuscriptand Kadir Liano for valuable discussions about the SVM.

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